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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY STRUCTURAL EQUATION MODEL ON LEARNERS' CONCEPTIONS OF LEARNING AND APPROACHES TO LEARNING AS PREDICTORS OF PHYSICS SELF-EFFICACY
A Doctoral Dissertation Presented to the Faculty of Graduate School of the College of Policy Studies, Education and Management Mindanao University of Science and Technology Cagayan de Oro City, Philippines
In Partial Fulfillment of the Requirements for the Degree, Doctor of Philosophy in Educational Planning and Management (Ph.D. in EPM)
RAY BUTCH DACLAN MAHINAY March 2014
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY APPROVAL SHEET In partial fulfillment of the requirements for the degree, Doctor of Philosophy in Educational Planning and Management, this dissertation entitled, “Structural Equation Model on Learners' Conceptions of Learning and Approaches to Learning as Predictors of Physics Self-Efficacy”, has been prepared and submitted by RAY BUTCH D. MAHINAY for approval of the panel of examiners.
AMPARO V. DINAGSAO, Ph.D. Adviser
-----------------------------------------------------------------------------------------------------------Approved in partial fulfillment of the requirements for the degree, Doctor of Philosophy in Educational Planning and Management:
RUTH G. CABAHUG, DTE Panel Member
JUANA M. DE LA RAMA, Ph.D. Panel Member
NENITA D. PALMES, Ph.D. Panel Member
WARREN O. LUZANO, Ph.D. Panel Member
ESTRELLA F. PEREZ, DALL Panel Member
AMPARO V. DINAGSAO, Ph.D. Panel Chairman
--------------------------------------------------------------------------------------------------------------------Accepted and approved in partial fulfillment of the requirements for the degree, Doctor of Philosophy in Educational Planning and Management:
ESTRELLA F. PEREZ, DALL Dean, CPSEM Graduate School Date Signed: _______________
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY ABSTRACT To substantively improve the understanding of learner’s self-efficacy, education researchers must need to explore how it is structured. This study proposed structural models of physics self-efficacy (PSE) and outlined its relationships with (1) conceptions of learning physics (CLP); (2) approaches to learning physics (ALP); (3) physics learning results (PLR); and (4) students’ career interest to science, technology, engineering and math (STEM) through structural equation modeling. Questionnaires from Tsai (2004) were adapted to measure the scales on PSE, CLP and ALP. A researcher-made validated tool, Physics Knowledge Contest Test, was likewise administered to measure PLR. STEM Semantics Survey by Wood, Knezek and Christensen (2010) was given too to gauge the respondents’ interest to STEM career. All these tools were managed for a field survey to 317 fourth year students randomly chosen among the national high schools in the east district of Cagayan de Oro City. Path and regression analyses showed that CLP has significant relationship with ALP. Consequently, ALP also has significant effect to PSE. Moreover, significant interactions among students’ physics self-efficacy, physics learning results and career interest to STEM were established. These results provide valuable information in understanding the nature and process of learning physics such that recommendations for physics instructional improvements and for further research directions are in order.
Keywords: learning conception, learning approaches, physics learning, self-efficacy, physics achievement, STEM career interest
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY ACKNOWLEDGMENT This researcher extends his whole-hearted thanks to the following who in many ways gave support and encouragement in the realization of this dissertation: Ultimately to Our God Almighty, for the all-out love and providence He bestowed upon the researcher and this humble piece of work; Dr. Amphie Vedua-Dinagsao, for her expertise as the researcher’s adviser that really set direction to this study to a more achievable one. She unselfishly gave this work a patent of distinction; Dr. Estrella F. Perez, Dr. Nenita D. Palmes, Dr. Ruth G. Cabahug, Dr. Juana M. de la Rama, and Dr. Warren O. Luzano, the researcher’s panel of oral examiners, for their bright suggestions that really shaped up this work; Dr. Eduardo T. Cartel, Fe S. Pablico, and Alven L. Gomez, for the generosity of their time and skill in validating the researcher’s survey tools; Dr. Sol G. Simbulan, former dean of Bukidnon State University Graduate School, who is the researcher’s external examiner, for her remarks and clarifications that really polished this paper; Dr. Anthony M. Penaso, vice-president of Central Mindanao University, for his ideas that truly made this work comparable to the standards of research; Dr. Genaro V. Japos, president of the International Association of Multidisciplinary Research, for his trust that this work is worthy of journal publication; The administrative office of the Department of Education-Division of Cagayan de Oro City, through Mr. Judson M. Pastrano, the school head of Tablon National High
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY School, for their consent that this researcher can avail of the paid study leave, and in the due course, for the permit that the researcher can conduct survey among the sampled schools; The researcher’s colleagues in Tablon National High School, for their unwavering support in this journey, especially to Psyche B. Cambo for all the technical assistance; The researcher’s classmates, who have earned the esteem to be called doctors, for imparting not only academic companionship but sincere friendship; The researcher’s long-time friends, Dr. Ma. Angeles Dano-Hinosolango, Ma. Victoria Bicbic-Trinidad, Anito R. Librando, Michael S. Villamor, Jason M. Alcudia, Sofia Julieta C. Beja, Mary Jane P. Fabre, Jana Jane G. Dacobor, Myra Joseal ClarinMoreno, Linie Pates-Slades, Alvin D. Rasonable, and all those who are actually worth mentioning, for being most compassionate and supportive; And the researcher’s family and relatives here in Cagayan de Oro City and in Bohol, for their love and understanding that provide life and sustenance to the researcher to set forth with his goals and ambitions. This is not a mere manuscript of data and insights – but of hard work and dedication, feat and achievement. Ad majorem dei gloriam, for God’s greater glory!
RAY BUTCH D. MAHINAY
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY
DEDICATION
Just like always, I sincerely dedicate this work to our Almighty Father; to my decedent parents; to my family in Bohol; to my sister, Aga Emm; to my brother, Cris Daven; and to my niece, Samantha Sle. -RBM
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY CONTENTS Page i ii
Title Page Approval Sheet
iii v vii viii xi xiii xiv
Abstract Acknowledgment Dedication Contents List of Tables List of Figures List of Abbreviations Chapter 1
Chapter 2
BACKGROUND AND OVERVIEW 1.1
Introduction
1
1.2
Conceptual Framework
3
1.3
Statement of the Problem
14
1.4
Hypotheses of the Study
15
1.5
Significance of the Study
16
1.6
Delimitation of the Study
18
1.7
Definition of Terms
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LITERATURE REVIEW 2.1
On Students’ Conceptions of Learning Physics
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2.2
On Students’ Approaches to Learning Physics
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2.3
On Students’ Physics Self-Efficacy
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY
Chapter 3
Chapter 4
2.4
The Interaction of Conceptions of Learning Physics, Approaches to Learning Physics, and Physics Self-Efficacy
32
2.5
Physics Learning Achievement among Filipino Students
36
2.6
The Science, Technology, Engineering and Math (STEM) and Brain Drain Situation in the Philippines
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2.7
Associating Self-Efficacy and STEM Career Options
39
2.8
About Structural Equation Modeling
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METHODOLOGY 3.1
Research Design
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3.2
Research Locale
50
3.3
The Respondents
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3.4
Sampling Procedure
52
3.5
Validity and Reliability of Instruments
54
3.6
Data-Gathering Procedure
59
3.7
Statistical Treatment
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DATA PRESENTATION AND ANALYSIS 4.1
Findings and Discussion
63
4.1.1
Students’ Conceptions of Learning Physics
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4.1.2
Students’ Approaches to Learning Physics
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4.1.3
Students’ Level of Physics Self-Efficacy
88
4.1.4
Relationships among Conceptions of Learning, Approaches to Learning, and Physics Self-Efficacy
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY
Chapter 5
4.1.5
Students’ Proficiency Level in Physics Learning
98
4.1.6
Students’ Perception in Taking Career in Science and Technology, Engineering and Mathematics (STEM)
106
4.1.7
Relationships among Physics Self-Efficacy, Physics Learning Results, and Career Perception in STEM Degrees
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4.2
Structural Equation Modeling (SEM) on Students’ Conceptions of Learning Physics, Approaches to Learning Physics, and Physics Self-Efficacy
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4.2.1
Fitness of Measurement Model on Conceptions of Learning Physics
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4.2.2
Fitness of Measurement Model on Approaches to Learning Physics
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4.2.3
Fitness of Structural Model on Students’ Conceptions of Learning Physics, Approaches to Learning Physics, and Physics Self-Efficacy
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4.3
Structural Equation Modeling (SEM) on Students’ Physics Self-Efficacy, Physics Learning Results, and STEM Career Interest
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4.3.1
Fitness of Measurement Model on STEM Career Interest
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4.3.2
Fitness of Structural Model on Students’ Physics Self-Efficacy, Physics Learning Results, and STEM Career Interest
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SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS 5.1
Summary and Findings
136
5.2
Conclusions
140
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY
Bibliography Appendices Vita
5.3
Generation of Theories
142
5.4
Recommendations
143
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY LIST OF TABLES Table
Page
1
Distribution of the respondents.
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2
Scoring guide for Physics self-efficacy, conceptions of learning Physics and approaches to learning.
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3
Scoring guide for students’ perception in STEM disciplines and careers.
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4
Scoring guide for Physics learning results.
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5
Descriptive statistics on low-level conceptions of learning Physics.
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6
Descriptive statistics on high-level conceptions of learning Physics.
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Final description of the students’ level on conceptions of learning Physics.
73
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Descriptive statistics on deep approaches to learning Physics.
77
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Descriptive statistics on surface approaches to learning Physics.
82
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Final description of the students’ level on approaches to learning Physics.
87
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Descriptive statistics on Physics self-efficacy.
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Correlations among the subscales on conceptions of learning Physics, approaches to learning Physics, and Physics selfefficacy.
95
13
Per item description of the students’ Physics learning results.
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Per topic and the overall description of the students’ Physics learning results.
105
15
Semantic perception data on STEM disciplines and careers.
107
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Descriptive statistics on students’ perception on STEM disciplines and career.
110
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Correlations among the scales on physics self-efficacy, physics learning results, and career perception in STEM degrees.
114
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Summary of re-specification of structural model for conceptions of learning Physics, approaches to learning Physics, and Physics self-efficacy.
121
19
Fit indices of measurement and structural models for conceptions of learning Physics, approaches to learning Physics, and Physics self-efficacy.
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Regression weights on the interaction among conceptions of learning Physics, approaches to learning Physics, and Physics self-efficacy.
125
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Summary of re-specification of measurement model for STEM career interest.
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Summary of re-specification of structural model of students’ physics self-efficacy, physics learning results, and STEM career interest.
129
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Fit indices of measurement and structural models for Physics self-efficacy, Physics learning results, and STEM Career Interest.
131
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Regression weights on the interaction among conceptions of learning Physics, approaches to learning Physics, and Physics self-efficacy.
133
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY LIST OF FIGURES Figure
Page
1
The hypothesized structural model on conceptions of learning, approaches to learning, and physics self-efficacy.
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2
The hypothesized structural model on physics self-efficacy, physics learning results and STEM career interest.
13
3
Model on the basic approach to performing SEM.
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4
Google map screenshot of the survey locations in the East II district of the division of Cagayan de Oro City.
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5
Item distribution of the scales among the survey tools.
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6
Students’ levels on the subscales of conceptions of learning physics.
72
7
Model for Process-induced learning in the DLP against the traditional teacher-dominated strategies.
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8
Sample flow of topics for four sessions in Physics Essentials Portfolio.
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9
Students’ levels on the subscales on approaches to learning physics.
86
10
Histogram of the raw scores of the students with the normal curve in the 50-item Physics Knowledge Contest Test.
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Standardized solutions of the measurement model of conceptions of learning physics.
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Standardized solutions of the measurement model of approaches to learning physics.
118
13
Standardized solutions of the structural model of conceptions of learning, approaches to learning, and physics self-efficacy.
123
14
Standardized solutions of the measurement model of STEM Career Interest.
127
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Standardized solutions of the structural model of Students’ Physics Self-Efficacy, Physics Learning Results, and STEM Career Interest.
132
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY LIST OF ABBREVIATIONS Abbreviation
Acronym
AGFI
Adjusted Goodness of Fit Index
ALP
Approaches to Learning Physics
AMOS
Analysis of Moment Structures
BEC
Basic Education Curriculum
CFI
Comparative Fit Index
CLP
Conceptions of Learning Physics
DepEd
Department of Education
DLP
Dynamic Learning Program
HEI
Higher Education Institution
LPON
Learning Physics as One Nation
MPS
Mean Percentage Score
MRS
Mean Raw Score
PEP
Physics Essentials Portfolio
PLR
Physics Learning Results
PPMC PSE
Pearson Product Moment Correlation Physics Self-Efficacy
PSSLC
Philippine Secondary Schools Learning Competencies
RMSEA
Root Mean Square Error of Approximation
SEM
Structural Equation Modelling
STEM
Science, Technology, Engineering, and Mathematics
TIMSS
Trends in Mathematics and Science Study
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY
Chapter 1 BACKGROUND AND OVERVIEW
1.1. Introduction In the aim to raise learning performances in high schools, it has been a constant effort in research to investigate on students’ aspects, dynamics and influences to explain causations and effects of poor learning achievement. The contention of these researches are enthused by the results of the National Achievement Test which is locally prepared and administered by the Department of Education (DepEd) and the Trends in Mathematics and Science Study (TIMSS) which is an international standardized testing on students’ achievement. For the past years, results for both tests inferred poor condition of Philippine math and science education. Conceding to this shortcoming, high school graduates seemed to be less appealed to science, technology, engineering and math (STEM) degrees for tertiary education. An enrolment report from the Commission on Higher Education or CHED (2010) presented that only 0.87% enrolled to science courses, 0.89% to math courses, and 12% to engineering courses of two million college enrollees on the school year 2009-2010. This must stir up manpower concerns in the future if our country wanted to be at par with other industrialized Asian nations. This researcher being a physics teacher for almost 10 years in DepEd, it is motivating to study on how high school students view and learn physics. In one
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY institutional pre-test performance report conducted by Center for Educational Measurement (2011) in Cagayan de Oro City public schools, it was revealed that 68% of the students have a quality index of very poor and only 2% reached with a quality index of below average. This dismal situation needs attention on strengthening physics learning in high schools especially that it is foundational to most college STEM degrees. There are myriad of ways on how to explore learners’ factors toward learning achievement as dealt with many researchers over the past millennia. One remarkable study is on self-efficacy by Bandura (1977) which is about learner’s beliefs in one’s capabilities to organize and execute course of action required to produce given attainments. Self-efficacy provided strong foundational concept to education researchers such that it has been generally correlated to academic motivation and achievement (Schunk & Pajares, 2009). However, Usher and Pajares (2008) claimed that in order to find out ways and enhance learner’s self-efficacy, education researchers must need to know how it is formed and structured. To explore the structural relationships within learner’s belief system of selfefficacy, two learning constructs, namely, conceptions of learning and approaches to learning can be adopted (Chiou & Liang, 2012). These two constructs are like inputoutput variables in a student’s learning continuum. It befits this researcher’s intention to ascertain how high school students view and learn physics. Given the aforementioned circumstances, it is the contention of this study to focus on physics self-efficacy of the public high school senior students. Accordingly
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY the learning constructs are specified to conceptions of learning physics and approaches to learning physics. A structural equation model is proposed to explain the interaction of learner’s conceptions of learning physics, approaches to learning physics, and physics self-efficacy. In addition, physics self-efficacy is deemed as an influencing factor to senior students’ learning results in physics subject and to their career interest to STEM degrees. This intended structural equation model (SEM) incorporates behavioral, cognitive, affective, and social constructs; therefore the SEM provides integrated information about the development of physics self-efficacy of the students. The researcher believes that through this model, science educators can be guided on designing more appropriate learning pedagogies to improve learner’s self-efficacy and learning experience towards Physics. This will be most useful especially in public high schools where the learning situation is most challenging.
1.2. Conceptual Framework This study explored on the extent of relationships among these five variables, namely: (1) conceptions of learning physics (CLP); (2) approaches to learning physics (ALP); (3) physics self-efficacy (PSE); (4) physics learning results (PLR); and (5) students’ career interest to science, technology, engineering and mathematics (STEM) degrees.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY 1.2.1. Conceptions of Learning Physics (CLP) The construct on conceptions of learning is treated as one’s beliefs and understanding of the nature of learning (Chiou & Liang, 2012). It is formed from a learner’s personal experience and is therefore a task- and domain-dependent. Many researches into students’ conceptions of learning have indicated that students conceive learning in qualitatively different ways (Purdie & Hattie, 2002). It is a strengthened notion that learners have varying conceptions of learning towards different subject domains. Among the science areas, students’ conceptions of learning biology may reveal different conceptions of learning in physics. So it is important that there is focus in one domain for this construct. This study adopted the concept of Tsai (2004) on conceptions of learning physics to which he dichotomized it into two broad categories: high level and low level. For the low level category, Tsai (2004) purported that students’ conception of learning is represented by the following subcategories: (1) memorizing (learning is a rote process to remember related formula, laws and terms); (2) testing (learning is a preparation for examinations and the pursuit of high scores in tests); and (3) calculating and practicing (learning is viewed as a series of calculating, practicing tutorial problems, and manipulating formulas and numbers). It denotes that these are passive and transmissive view of science learning. On the other hand, the higher level category encompasses the following: (1) increasing in knowledge (learning is treated as a process of acquiring and extending knowledge); (2) applying (learning is for using acquired knowledge and skills to solve
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY related problems); and (3) understanding (learning is to understand the connections between and among concepts). It suggests that students view learning science in an active and constructive view. These are measurable scales such that the levels of public high school students’ conception of learning physics were identified. Consequently, these same subscales were associated with the other variables posed in this study.
1.2.2. Approaches to Learning Physics (ALP) A generic way of describing “what the student does” is precisely in terms of their ongoing approaches to learning (Biggs et al., 2001). An approach to learning describes the nature of the relationship between student, context and task. Students may utilize different ways to study different academic subjects; therefore the nature of student’s approaches to learning is also domain-specific. This holds true on the science areas that student’s approaches to learning biology, chemistry and physics vary. So like with the conceptions of learning physics, approaches to learning physics as a construct demands for sole concentration to one domain. Based on studies on approaches to learning, researchers have identified two approaches to learning science (Chiou & Liang, 2012) which are congruent to the original concept of Biggs, Kember and Leung’s (2001) and Tsai’s (2009) student learning processes, the deep and surface approaches. Deep approach is characterized as an intrinsic motivation (deep motive) to actively comprehend and integrate the new learning materials with existing ideas (deep strategy); on the other
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY hand, the surface approach is featured by its external motivation (surface motive) to solely memorize or reproduce the learning materials (surface strategy). Specifically, deep motive is when students express intrinsic motivation in learning as triggered by intense curiosity and interest; deep strategy is when students utilize a more meaningful way to learn such as making connections and extracting key points. Conversely, surface motive is when students possess extrinsic motivation to learn such as passing an exam and pursuing a high grade; surface strategy is when students use more rote-like strategies to learn such as unreflective memorization. The same with conceptions of learning, these approaches to learning subscales (i.e. deep and surface approaches) are measurable such that the learning approaches of public high school students to physics subject will be identified. These same subscales were also associated with other variables being investigated in this study.
1.2.3. Theory of Physics Self-Efficacy (PSE) Self-efficacy plays a crucial role in determining various aspects of human behaviors, such as the choice of goals and strategies, the effort needed to achieve the goals, and the persistence when confronting obstacles (Bandura, 1997). People who have high self-efficacy beliefs in a particular domain “act, think, and feel differently� from those with low self-efficacy (Bandura, 1984). They are more persistent, more effective, and more self-regulated (Magno, 2008; Pajares & Urdan,
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY 2006). Bandura hypothesized that this belief is domain-specific, which means it cannot be expected that a person is self-efficacious in all human endeavors (Bandura, 1997). Skaalvik and Skaalvik (2010) cited Bandura (2006) that self-efficacy is grounded in the theoretical framework of social cognitive theory emphasizing the evolvement and exercise of human agency that people can exercise some influence over what they do. From this standpoint, self-efficacy affects one's goals and behaviors and is influenced by one's actions and conditions in the environment. Efficacy beliefs determine how environmental opportunities and impediments are perceived and affect choice of activities, how much effort is expended on an activity, and how long people will persevere when confronting obstacles. In educational settings, studies have shown that self-efficacy exerts a positive effect on their learning goals, academic achievements and career choices (Chiou & Liang, 2012). Based on social cognitive theory, physics self-efficacy may be conceptualized as physics students' beliefs in their own ability to plan, organize, and carry out activities that are required to attain given educational goals. Self-efficacy is domainspecific and task-dependent. So like with the two aforementioned constructs, selfefficacy for this study is accordingly pointed to one content domain which is physics. Bandura (1997) proposed four theoretical sources of self-efficacy: (1) mastery experiences, one’s interpretation of his/her own attainment; (2) vicarious experience, one’s interpretation of others’ accomplishment; (3) social persuasion, others’ comments on one’s capabilities; and (4) physiological states, one’s assessment of his/her own emotional and physical states. However, these four theoretical sources of
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY experience may not directly influence self-efficacy, given that the original experiences may be mediated by other variables (Usher & Pajares, 2008). Accordingly, it would be difficult to examine and identify the real sources and effects of self-efficacy as Pajares (1996) addressed this issue as “chicken-or-egg” question. This issue reveals a lack of research on tracing the sources and causal structure of self-efficacy (Chiou & Liang, 2012).
1.2.4. Physics Self-Efficacy, Conceptions of Learning Physics and Approaches to Learning Physics Bandura (1997) and Schunk and Pajares (2008) purported that science selfefficacy is affected by both a learner’s pre-task personal characteristics and duringtask learning behaviors that conclusively determine his academic performance. Conceptions of learning refer to an individual’s belief in learning (Chiou & Liang, 2012) and can stand as a pre-task factor. Whereas approaches to learning denote how a learner performs learning tasks (Biggs et al., 2001) and this can represent during-task behavior. With this in a Physics learning continuum, the conceptions of learning physics is a pre-task construct and in the same manner that the approaches to learning physics a during-task construct. Furthermore, the relationships between self-efficacy and variables of interest are reciprocal in nature (Bandura, 1997). The students’ pre-existing self-efficacy may influence their learning motivations, behaviors and performances, their interpretation
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY of the resultant learning experiences and outcomes will in turn affect their self-efficacy, and it creates a cyclical pattern in the process. Implicitly both conceptions of learning physics and approaches to learning physics are strong variables that can influence the formation of physics self-efficacy. The congruence of the content domain is maintained for this study. It has been elaborated richly in literature that studies on self-efficacy are suggested to focus on a specific domain, to explore the criteria that students use to interpret their learning experiences, and to find out the essential predictive variables that may involve in the causal, reciprocal structure of self-efficacy (Chiou & Liang, 2012). Hence, physics is consistently used as a content domain with the two important academic constructs, conceptions of learning physics and approaches to learning physics, which are supposed to serve as criteria for self-interpretation and contributing factors to the formation of physics self-efficacy.
1.2.5. Physics Self-Efficacy and Physics Learning Results (PLR) The introduction of Bandura’s (1977) construct on self-efficacy has surfaced many studies on cognitive psychology and learning. It even reached universality in meaning that self-efficacy is related to academic motivation and achievement. However, self-efficacy is domain-specific and task-dependent (Bandura, 1997). As Chiou and Liang (2012) put it, self-efficacy is not a general, stable personal trait that can represent an individual’s efficacy beliefs in all aspects. Rather, it is a measure of one’s confidence of capability in a distinct content domain.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY As stressed for this study, self-efficacy is specific to physics (PSE) in domain. It is to be established that physics self-efficacy has relationship to students’ physics learning results (PLR). The linearity in the domain is also conservative in this aspect. Basing from the postulation of Bandura (1997), this linearity is to draw specificity and consistency in domain from conceptions of learning physics and approaches to learning physics in a Physics learning continuum to physics self-efficacy and then to physics learning results.
1.2.6. Physics Self-Efficacy and STEM Career Interest It is apparent that first-world countries have stable science, technology, engineering and mathematics (STEM) manpower such as in the cases of Japan and Korea. For these countries, strength and advancement in STEM is equitable to economic stability. Hence, there is demand for productivity and sustainability of STEM training in schools, colleges and universities in a country aiming for development. Adjusting our lens to high school environment, the likelihood that senior students will take STEM degrees in college is low. Retention is likewise a problem for those students who are already in a STEM program. According to Winston, Estrada & Howard (2008), one of the factors that affect students’ interest to STEM degrees is their academic self-efficacy. Chiou & Liang (2012) hypothesized that self-efficacy has an influencing effect on their students’ career choices. In this vein, this study ascertained the association
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY of physics self-efficacy of the high school senior students with their career interest to STEM degrees. Considering all the aforementioned concepts as a guide, two hypothesized structural models are made (see Figures 1 & 2 on the next page). These hypothesized structural models are represented by path diagrams which is a key feature of structural equation modelling that shows the network of relationships among the exogenous (predictor) and endogenous (response) variables investigated in this study. Moreover, these hypothesized structural models were the input models for statistical processing with IBM SPSS Amos.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY
Figure 1. The hypothesized structural model on conceptions of learning, approaches to learning, and physics self-efficacy. List of input variables for AMOS processing: Measurable Variables
Scale/Subscale
Description
MEMORIZING TESTING CALCPRAC KNOWLEDGE APPLYING UNDERSTANDING DEEPMOTIVE DEEPSTRAT SURFACEMOTIVE SURFACESTRAT PSE
Memorizing Testing Calculating and Practicing Increasing One’s Knowledge Applying Understanding Deep Motive Deep Strategy Surface Motive Surface Strategy Physics Self-Efficacy
A 5-item subtest of CLP. A 5-item subtest of CLP. A 4-item subtest of CLP. A 5-item subtest of CLP. A 3-item subtest of CLP. A 3-item subtest of CLP. A 3-item subtest of ALP. A 5-item subtest of ALP. A 3-item subtest of ALP. A 4-item subtest of ALP. A 7-item test of PSE.
Latent Variables
Scale
CLP ALP e
Conceptions of Learning Physics Approaches to Learning Physics Error Variables
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY
Figure 2. The hypothesized structural model on physics self-efficacy, physics learning results and STEM career interest. List of input variables for AMOS processing: Measurable Variables
Scale/Subscale
Description
PSE PLR SCIENCE TECH ENGG MATH STEM CAREER
Physics Self-Efficacy Physics Learning Results Science Technology Engineering Mathematics STEM Career
A 7-item test of PSE. A 50-item test of PLR. A 5-item subtest of STEM Career Interest. A 5-item subtest of STEM Career Interest. A 5-item subtest of STEM Career Interest. A 5-item subtest of STEM Career Interest. A 5-item subtest of STEM Career Interest.
Latent Variables
Scale/Subscale
STEMCI e
STEM Career Interest Error Variables
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY 1.3. Statement of the Problem This study aimed to investigate the structure of physics self-efficacy and delineate its relationships with conceptions of learning physics, approaches to learning physics, physics learning results and career interest to STEM degrees among senior public high school students. Specifically, this study answered the following questions: 1. What is the respondents’ level of conceptions of learning physics in the following: a. Memorizing; b. Testing; c. Calculating and Practicing; d. Increasing One’s Knowledge; e. Applying; and f. Understanding? 2. What are the respondents’ level of approaches to learning physics, a. Surface Motive; b. Surface Strategy; c. Deep Motive; and d. Deep Strategy? 3. What is the respondents’ level of physics self-efficacy?
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY 4. Are there significant relationships among conceptions of learning, approaches to learning, and physics self-efficacy? 5. What are the respondents’ proficiency level in physics learning? 6. What is the respondents’ level of interest in STEM career? 7. Are there significant relationships among physics self-efficacy, physics learning results and STEM career interest? 8. What model would best fit the structure of students’ physics self-efficacy? 9. Basing the best fit model, are there significant effects of conceptions of learning physics and approaches to learning physics to physics selfefficacy? 10. Basing the best fit model, are there significant effects of physics selfefficacy to physics learning results and STEM career interest?
1.4. Hypotheses of the Study The following null hypotheses were tested at 0.05 level of significance: H01. There is no significant relationship between conceptions of learning physics and approaches to learning physics in a learning continuum. H02. There are no significant relationships among physics self-efficacy, physics learning results and STEM career interest.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY H03. The hypothesized structural model on conceptions of learning, approaches to learning, and physics self-efficacy does not show satisfactory degree of fit to the observed data. H04. The hypothesized structural model on physics self-efficacy to physics learning results and STEM career interest does not show satisfactory degree of fit to the observed data. H05. There is no significant direct effect of the physics learning continuum to physics self-efficacy. H06. There is no significant direct effect of physics self-efficacy to physics learning results. H07. There is no significant direct effect of physics self-efficacy to STEM career interest. H08. There is no significant direct effect of physics learning results to STEM career interest.
1.5. Significance of the Study The results of this study provide information on the students’ conceptions of learning physics, approaches to learning physics, and physics self-efficacy, physics learning results and their career interest in STEM degrees that bear significant implications to the following: The students. Introduction of lessons and activities that are sensible of improving students’ approaches to learning physics and physics self-efficacy will
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY greatly benefit the students in the subject achievement. In the process, it might draw interest in considering STEM degrees when they reach college. Physics teachers. Awareness of the students’ physics self-efficacy will guide the teachers to plan lessons and employ classroom management style that could boost student confidence and participation in the subject. This is essential especially that many students perceive science as a difficult subject (Shepherd, 2008). School administrators. Results of this research could provide the administrators the opportunity to keep track with their school’s physical and educational situation. Consequently it would make them more attentive and supportive to raise teaching and learning outcomes. The Department of Education-Division of Cagayan de Oro City. The recommendations of this study could be basis for DepEd-CdO division officials to initiate in-service trainings and upgrading programs that would help teachers in their pedagogical management to improve learners’ self-efficacy. Also, the effects of this study could serve as baseline information for the formulation of decisions and policies relevant to the delivery of quality science instruction. Mindanao University of Science and Technology (MUST). Reference to this study would give the administration of MUST, being an institution of science, technology, engineering and mathematics in typology, a profound idea about STEM career interest of the high school students. It gives significant information with regards to marketing, enrolment and retention policies.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Science education researchers. The findings of this research could serve as benchmark data for other researchers who would have to do parallel studies and improve whatever is seen limited along the course of the conduct of this study.
1.6. Delimitation of the Study This study was conducted in the Division of Cagayan de Oro City in the school year 2013-2014. The researcher covered seven public high schools in the East II District. Following a sampling design, fourth year students were tapped as respondents. The scope anyhow is not representative of the whole division nor of DepEd in its entirety. The foci of this study are senior students’ conceptions of learning physics, approaches to learning physics, physics self-efficacy, physics learning results and their interest in STEM careers as the theoretical variables. Conceptions of learning physics includes the following aspects: (1) memorizing; (2) testing; (3) calculating and practicing; (4) increasing one’s knowledge; (5) applying; and (6) understanding. Approaches to learning physics encompasses the following domains: (1) surface motive and/or strategy; and (2) deep motive and/or strategy. Physics self-efficacy is being treated as one holistic construct. The four theoretical sources of self-efficacy according to Bandura (1997) were not particularly explored as hypothetical variables. Anyhow, Usher and Pajares (2008) specified that only mastery experience had been repeatedly confirmed as the major source of selfefficacy.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Students’ learning results in physics were based on topics under the first and second grading periods which generally covered (1) the nature of scientific inquiry; (2) scientific procedures and techniques; (3) mechanics; and (4) kinematics. All the physics teachers in the sampled schools followed the same instructional materials, source and pedagogical concepts of the Dynamic Learning Program (DLP) - Learning Physics as One Nation portfolio of Bernido and Bernido (2008). Other factors that might have bearing on this study like teachers’ participation and influence in the learning process, the students’ socio-demographic profiles especially on gender and cultural background, parental and social elements and among others were not incorporated.
1.7. Definition of Terms The meaning of the following terms used in this study are defined either theoretically or operationally: Approaches to Learning Physics (ALP). It denotes how a student performs learning tasks in physics. In this study, ALP is dichotomized into the following: a. Surface approach. This is featured by its external motivation (surface motive) to solely memorize or reproduce the learning materials (surface strategy). b. Deep approach. This is categorized as an intrinsic motivation (deep motive) to actively comprehend and integrate the new learning materials with existing ideas (deep strategy).
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Conceptions of Learning Physics (CLP). It represents student’s beliefs and understanding of the nature of learning Physics. CLP is classified into the following: a. Low-level CLP. This denotes that students have passive, transmissive view of physics learning. It is characterized by the following subscales: (1) memorizing; (2) testing; and (3) calculating and practicing. b. High-level CLP. This suggests that students learning physics in an active and constructive view. It is represented by the following subscales: (1) increasing one’s knowledge; (2) applying; and (3) understanding. Physics Learning Continuum. This is the process by which students view (pre-task) and learn (during-task) Physics. In this study, this is represented by conceptions of learning physics and approaches to learning physics. Physics Learning Results (PLR). This is the score attained by the fourth-year students in a multiple-choice questionnaire constructed solely for this study. Qualitative interpretation of the scores were based on DepEd Memo 158, s.2011. Physics Self-Efficacy (PSE). This is a measure of one’s confidence of capability in learning Physics. STEM Degrees. These are baccalaureate degrees in sciences, technology, engineering and mathematics that can be obtained in higher education institutions (HEIs). Structural Equation Model (SEM). It is a methodology for representing, estimating, and testing a network of relationships between variables (Suhr, 2006). It
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY is also known as analysis of covariance structures or causal modeling (Arbuckle, 2008). Basic terms that are associated with SEM are as follows: a. Error variable. Error represents much more than random fluctuations in a data due to measurement error. Error also represents a composite of age, socioeconomic status, verbal ability, and anything else on which a factor may depend but which was not measured in this study. The variable error is enclosed in a circle because it is not directly observed. b. Fit indices. This indicates the degree to which a pattern of fixed and free parameters specified in the model are consistent with the pattern of variances and covariances from a set of observed data. Joint criteria for acceptable fit by Hu and Bentler (1999) have been adopted in this study. b. Fixed parameters. A requirement of model specification to which these are not estimated from the data and their value is typically fixed to zero or one. c. Free parameters. A requirement of model specification to which these are estimated from the data. d. Latent variable. A construct that is not directly or exactly measured. e. Measured variable. A construct that is directly measured. f. Measurement model. A portion of the path diagram that specifies how the observed variables depend on the unobserved, or latent, variables. g. Path diagram. A pictorial representation of a model. h. Structural model. A portion of the path diagram that specifies how the latent variables are related to each other.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY
Chapter 2 LITERATURE REVIEW
This chapter reviewed some related literature and studies on (1) conceptions of learning physics (CLP); (2) approaches to learning physics (ALP); (3) physics selfefficacy (PSE); (4) physics learning results (PLR); and (5) students’ career interest to science, technology, engineering and mathematics (STEM) degrees. These are researches, articles and data sources from web contents found in the internet and from reliable e-journal portals.
2.1. On Students’ Conceptions of Learning Physics As students begin studying science as comprised of the distinct disciplines of chemistry, physics and biology in high school, they develop perceptions about each discipline and about the nature of science (Dickie, 2003). Most current theories of the learning of physics view the student as an active agent who constructs an understanding of the content by combining his or her existing knowledge with new experiences and information. Dickie (2003) described this as an effortful process in which the student's world view is built upon during the learning process. On the other hand if the student supposes that physics is a collection of weakly connected pieces of information (Hammer, 1995, as cited by Dickie, 2003), the student may think that knowing facts, formulae and problem solving algorithms constitutes
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY understanding physics. When told that a deep understanding of concepts and their interrelationships is important the student may not understand what is implied. There is a large body of research that compares the prior knowledge about the content and concepts of physics held by students with those of expert physicists as accorded by Dickie (2003). A number of authors have examined the consequences of these conceptions for the teaching of physics and yet another body of knowledge points to the effectiveness of active engagement teaching methods in changing student’s conceptual knowledge of physics compared with the ineffectiveness of traditional. A comparative study of middle school and high school students’ views about physics and learning physics by Ding (2013) presented that although middle school students received fewer years of education in physics, they demonstrated more expert-like conceptions about this subject matter than high school students.
2.2. On Students’ Approaches to Learning Physics Wilson, Georgakis and Sharma (2012) investigated student approaches to learning in physics. They explored whether different streams of study or exposure to different syllabi were associated with deep or surface approaches to learning. A total of 2,030 first year physics students at an Australian metropolitan university over three different year cohorts and three streams completed an adaptation of the Study Processes Questionnaire (SPQ) which produces measures of deep and surface approaches to learning. Students studied within 'Advanced', 'Regular' and
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY 'Fundamentals' streams, based upon prior experience in physics study. Students within the three cohorts were exposed to different senior high school syllabi, as the exam board introduced a new and innovative syllabus. The authors made comparison on approaches to learning across streams and across the three year cohorts. Findings showed that the behavior of the mean scale scores for students in different streams in first year physics is in agreement with expectations; ‘Advanced’ streams reported higher levels of deep approaches while ‘Fundamentals’ streams reported higher levels of surface approaches. Furthermore, different year cohort performance on the scales reflects changes in senior high school syllabus; with a new syllabus reflecting a shift toward more deep approaches to learning. Research into teacher thinking offers potential insights into ways of promoting better teaching and learning approaches. A qualitative study of Mulhall (2012) explored the views about physics, and learning and teaching physics of a group of teachers whose classroom practice was 'traditional' and a group who used conceptual change teaching approaches. It focused on the views about learning physics held by the two groups. To summarize and compare the groups, a composite description was created for each group. The composite description represents all the common views of teachers who were in that group. The composite description is termed 'typical' teacher. The study concluded that the conceptual change teachers' views about learning physics were constructivist while the traditional teachers held absorptionist views.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Kizilgunes, Tekkaya, and Sungur (2009) proposed a model to explain how epistemological beliefs, achievement motivation, and learning approach related to achievement. The authors assumed
that
epistemological beliefs influence
achievement indirectly through their effect on achievement motivation and learning approach. Participants were 1,041 6th-grade students. Results of the path analysis suggested that students who believed knowledge to be evolving (i.e., development) and handed down by authority (i.e., source) were more likely to be self-efficacious in their learning and were found to have higher levels of learning- and performance-goal orientations. In addition, although learning goal was positively related to meaningful learning, performance goal and self-efficacy were negatively related to the learning approaches. The direction of the relation between learning approaches and achievement was positive. Stephen (2010) aimed at investigating the effect of technological attitude of students on academic achievement in Physics. Four co-education schools were randomly drawn from urban centers in Nigeria. Sample size of 110 senior secondary physics students were considered. Two researcher-made instruments, students’ attitude towards technology (SATTQ) and Physics Achievement Test (PAT) were used in generating the data for the study. The data generated were analyzed using t-test statistics. The findings of the investigation revealed that technological attitude, type of school and gender showed significant effects on students’ achievement in physics. Bernardo (2003) shown that a critical variable in determining academic achievement in different cultures and educational systems is approaches to learning.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY In his study, results indicated that the Deep and Achieving subscale scores of the LPQ were positively related to academic achievement even when the effects of school ability and prior academic achievement were controlled; and with some slight exceptions, the relationship between the LPQ scale scores and academic achievement were generally similar between male and female Filipino students. Delialioglu and Askar (1999) investigated the contribution of mathematical skills and spatial ability to achievement in secondary school physics. Correlational analysis showed that the correlation coefficient for mathematical skills and physics achievement was 0.46 (p<0.05), and for spatial ability and physics achievement was 0.45 (p<0.05). To see the combined contribution of mathematics and spatial ability to physics achievement, multiple regression analysis was applied. The results showed that the contribution of the two predictor variables (mathematical skills and spatial ability) accounted for almost 31% of the variance in the physics achievement test scores.
2.3. On Studentsâ&#x20AC;&#x2122; Physics Self-Efficacy Sawtelle (2011) conducted a study that focus on understanding the role selfefficacy plays in retaining students in a physics course. She used an explanatory mixed methods approach to first investigate quantitatively the influence of self-efficacy in predicting success and then to qualitatively explore the development of self-efficacy. Results indicated that self-efficacy is a significant predictor of success for all students. She disaggregated the data to examine how self-efficacy develops differently for
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY women and men. Results also showed women rely on different sources of self-efficacy than do men. Moreover, in the qualitative phase of the project of Sawtelle (2011), she introduced a methodology for understanding how self-efficacy develops moment-bymoment using the lens of self-efficacy opportunities. According to Schunk and Meece (2005), adolescentsâ&#x20AC;&#x2122; school experiences help shape their self-efficacy beliefs. With cognitive maturity, adolescents are better able to interpret and integrate multiple sources of information about their competencies, and they have a much more differentiated view of their abilities. There often is a stronger relation between performance feedback and competence beliefs for adolescents than for younger children. For them, there is strong relation of selfefficacy to academic motivation (effort, persistence) and achievement. Among students of different ages, significant and positive correlations have been obtained between self-efficacy for learning (assessed prior to instruction) and subsequent motivation during learning. Self-efficacy for learning also correlates positively with post-instruction self-efficacy and skillful performance. Studies across different content domains (e.g., reading, writing, mathematics) using children and adolescents have yielded significant and positive correlations between self-efficacy and academic achievement (Schunk & Pajares, 2002). Bouffard-Bouchard, Parent, and LarivĂŠe (1991) found that high school students with high self-efficacy for problem solving demonstrated greater performancemonitoring and persistence than did students with lower self-efficacy. Among college students, Zimmerman and Bandura (1994) obtained evidence that self-efficacy for
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY writing correlated positively with goals for course achievement, self-evaluative standards, and actual achievement. Periods of transition in schooling can cause changes in self-efficacy (Schunk & Pajares, 2002). Much research has investigated the transition from elementary to junior high/middle school with its many changes in teachers, peers, classes, and grading criteria (Urdan & Midgley, 2003). Young adolescents often experience declines in their competence and efficacy beliefs as they make the transition from elementary to middle school; however, negative changes in self-perceptions are not inevitable and may result from changes in the school environment. Moreover, Urdan and Midgley (2003) revealed that elementary and secondary classrooms tend to have different goal structures. Compared with elementary students, middle school students perceive their learning environment as less focused on learning and mastery and more focused on competition and ability differences. When classroom environments emphasize competition and normative evaluation (performance goals) rather than individual mastery and self-improvement, adolescents can experience a decline in their self-efficacy. In contrast, classroom environments that emphasize the importance of effort, meaningful learning, selfimprovement, collaboration, and student interests help adolescents maintain positive perceptions of their efficacy and competence (Urdan & Midgley, 2003). Researches (Schunk & Pajares, 2002) on the effects of different classroom environments is consistent with experimental studies designed to examine relations between instructional conditions and adolescentsâ&#x20AC;&#x2122; self-efficacy beliefs. Social
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY cognitive theory predicts that at the outset of an activity, students differ in their selfefficacy for learning as a function of their prior experiences, personal qualities, and social supports (e.g., extent that parents and teachers encourage them, facilitate their access to learning resources, and teach them strategies for learning). As students engage in activities they are affected by personal (e.g., goals, cognitive processing) and situational influences (e.g., instruction, feedback). These factors provide students with cues about how well they are learning, which they then use to gauge self-efficacy for continued learning. Some instructional conditions that have been shown to develop self-efficacy among adolescents are proximal and specific learning goals, instruction on learning strategies, social models, performance and attributional feedback indicating progress, and rewards contingent on improvement (Schunk, 1995). These processes are hypothesized to affect self-efficacy and motivation through the common mechanism of informing students of their progress in learning. Schunk and Meece (2005) gave middle school students instruction on a novel mathematical task. Prior to the instruction girls judged self-efficacy for learning lower than did boys. Following the instruction (which included performance feedback), girls and boys did not differ in achievement or self-efficacy for solving problems. There also were no differences in male and female studentsâ&#x20AC;&#x2122; problem solving during instruction. The performance feedback indicating that learners were successful overrode the girlsâ&#x20AC;&#x2122; preconceptions about learning mathematics.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Zimmerman and Kitsantas (1997) found that providing learning goals raised self-efficacy and self-regulation during dart throwing. High school girls were assigned to a process-goal condition and asked to focus on the steps in dart throwing; others were assigned to a product-goal condition and asked to concentrate on their scores. After each throw some girls self-recorded progress by writing down steps accomplished properly or the outcome. Process-goal girls demonstrated higher selfefficacy and performance than did product-goal girls, and self-recording enhanced these outcomes. The authors replicated these results and also included a shifting-goal group where girls pursued a process goal, but, once they could perform the steps, they switched to a product goal of attaining high scores. The shifting goal led to the highest self-efficacy and performance. Schunk and Meece (2005) pretested students on self-efficacy and performance of computer applications and on how well and often they applied self-regulation strategies while learning computer skills (e.g., set goals, use appropriate manuals). Students were assigned to a process (learn the applications) or product (do the work) goal condition; within each condition half of the students evaluated their progress during the instruction on computer applications. The process goal, with or without selfevaluation, led to higher self-efficacy and strategy competence and frequency than did the product goal with no self-evaluation. Students who received the process goal with self-evaluation judged self-efficacy higher than did process-goal students who did not receive self-evaluation and product-goal students who self-evaluated. Among selfevaluation students, those who pursued process goals evaluated their learning
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY progress better than did those who received product goals. These results corroborate those of Schunk (1995), who found with children that self-evaluation combined with process goals is beneficial for self-efficacy and self-regulation. Li and Demaree (2013) suggested in a study that a student did not see any compelling reason to change his beliefs about the nature of learning even though there were pedagogical elements used in the course that reinforce the practices of a physics learner in a class. Learners’ existing learning identity was not sufficiently and appropriately challenged because they could achieve success in the course without shifts in their ideas about the nature of learning physics. Fencl and Scheel (2004) reported that self-efficacy, or a person’s situationspecific belief that he can succeed in a given task, has been successful in a variety of educational studies for predicting behaviors such as perseverance and success (grades), and for understanding which behaviors are attempted or avoided. The focus of this study was to examine if classroom factors such as teaching strategies and classroom climate contribute to students’ physics self-efficacy. Students in sections including a mix of teaching strategies did significantly better than students in the traditional section on outcome variables including self-efficacy. When individual strategies were examined, the strongest relationships were found between cooperative learning strategies and all sources of self-efficacy, and between climate variables and all sources of efficacy.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY 2.4. The Interaction of Conceptions of Learning Physics, Approaches to Learning Physics and Physics Self-Efficacy Chiou and Liang (2012) investigated on science self-efficacy and proposed a model to explain its relationships with students’ conceptions of learning and approaches to learning in science to 321 Taiwan high school students. The results confirmed their hypothesis that students’ conceptions of learning science had a direct effect on their approaches to learning science, which in turn contributed to their science self-efficacy. More specifically, the students’ lower-level conceptions of learning science, memorizing, testing and calculating and practicing, exerted positive effects on their surface approaches to learning science, but had negative effects on their deep approaches to learning science. In contrast, students’ higher-level conceptions of learning science, increasing one’s knowledge, applying, understanding and seeing in a new way, could positively induce the deep motive, deep strategy and surface motive to learn science, but prohibited the surface strategy. The students’ deep motive, deep strategy and surface motive, in sequence, were likely to make direct contributions to their science self-efficacy. The study of Cavallo (2004) investigated differences and shifts in learning and motivation constructs among male and female students in a nonmajors, yearlong structured inquiry college physics course and examined how these variables were related to physics understanding and course achievement. Tests and questionnaires measured
students'
learning
approaches,
motivational
goals,
self-efficacy,
epistemological beliefs, scientific reasoning abilities, and understanding of central
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY physics concepts at the beginning and end of the course. Course achievement scores were also obtained. The findings showed that male students had significantly higher self-efficacy, performance goals, and physics understanding compared to females, which persisted throughout the course. Differential shifts were found in students 'meaningful learning approaches, with females tending to use less meaningful learning from beginning to end of the course; and males using more meaningful learning over this time period. For both males and females, self-efficacy significantly predicted physics understanding and course achievement. For females, higher reasoning ability was also a significant predictor of understanding and achievement; whereas for males, learning goals and rote learning were significant predictors, but in a negative direction. The findings revealed that different variables of learning and motivation may be important for females 'success in inquiry physics compared to males. Instructors should be cognizant of those needs in order to best help all students learn and achieve in college physics. Lynch (2010) presented a study about college physics semester and lab grades that were associated with motivational beliefs and learning strategies as measured by the Motivated Strategies for Learning Questionnaire (MSLQ). Notable correlations with semester grades were found for self-efficacy, both intrinsic and extrinsic motivation, and valuing the task. The learning strategy of elaboration was also correlated with final grades. Even though male and female final grades were not statistically different, the study found stark gender differences. There was large correlation between male self-efficacy scores and final grades. Task value and
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY rehearsal were correlated for males but not females. Extrinsic goal orientation and critical thinking was correlated for females but not males. Females had significantly higher physics lab scores than males. Male metacognitive and the time-study environment scores were significantly correlated with lab scores. In contrast, extrinsic goal orientation, self-efficacy, elaboration and effort were correlated for females. An ANOVA demonstrated gender differences in self-efficacy, critical thinking and test anxiety. Male self-efficacy scores were significantly higher than female scores and critical thinking. Female test anxiety scores were significantly higher than male scores. The study suggests that physics faculty should create an instructional environment to enhance female self-efficacy, attend to possible perceptional differences between males and females regarding their purpose for studying physics. Faculty should communicate with students and set assignments in the course to foster success. Faculty involvement should encourage students to monitor their own goal performance. Chan (2012) tested teacher education students of a university in Hong Kong to examine their epistemological beliefs, conceptions of learning, and learning strategies. Correlation and path analysis showed significant relations between epistemological beliefs and conceptions of learning and learning strategies. The results suggested the significant roles of epistemological beliefs in learning, through their impact on and relations with the conceptions of learning and strategies adopted by the students.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Schunk and Meece (2005) found that high school students reported more positive self-efficacy when their teachers used learner-centered instructional practices that promoted higher-order thinking, honored student voices, created supportive relations, and adapted instruction to individual and developmental needs. Tabago (2012) developed constructivist approach experiments to determine its effectiveness in teaching physics concepts. The quasi‐experiment following a non‐ equivalent control group design was used. The study administered pre‐test and post‐ test. The scores in the achievement test and standardized attitude inventory test were compared and the significance of their difference was determined using the t‐test. The control group and the experimental group were equal in terms of cognitive level in Physics. However, the students exposed to the constructivist approach of laboratory teaching had significantly higher posttest scores and higher mean gain scores than the students exposed to the traditional approach after the study was conducted. The experimental group developed a more positive attitude towards Physics than the control group. Moreover, there was a significant difference between the post achievement scores and post attitude scores of the students exposed to constructivist approach‐based experiments and traditional experiments. The Constructivist Approach Experiments are effective in enhancing students’ achievement and in developing a more positive attitude towards the subject than the traditional approach.
2.5. Physics Learning Achievement among Filipino Students
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY The study of Orleans (2007) aimed at assessing the state of Philippine secondary school physics education using data from a nationwide survey of 464 schools. Teacher-related indicators revealed academic qualification deficiency, low continuing professional involvements, substantial physics teaching experience, and good licensure status. Academic environment indices revealed that the number of physics classes per teacher is manageable, but the individual classes are large. Results also showed limited instructional materials and technologies, the unpopularity of professional mentoring, and favorable library and internet access. From these findings, challenges to developing a larger pool of competent physics teachers and equipping schools with relevant instructional devices were identified. In the local level, taking from the Physics results from the National Achievement Test conducted last February 2013 showed that the entire Cagayan de Oro division encompassing both the public and private schools has a mean percentage score (MPS) of below 75%. The highest MPS reached 73.91% while among all the schools, only three schools got an MPS above 70%. This has stirred the division office considering the curricular efforts made like the adoption of the Learning Physics as One Nation (LPON) under the Dynamic Learning Program of Bernido and Bernido (2008). Orleans (2007) assessed the status of the Philippine secondary school physics education, and to identify challenges for substantive improvements. A nationwide survey of 464 schools, and 767 physics teachers was done. Indicators revealed deficiency in academic qualifications, low continuing professional involvements, but
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY substantial physics teaching experience and good licensure status; manageable physics classes, big physics class size, and limited instructional materials and technologies; professional mentoring is unpopular but access to libraries and internet is favorable. Challenges to development of a bigger pool of competent physics teachers and equipping schools with relevant instructional devices were identified.
2.6. The STEM and Brain Drain Situation in the Philippines Brain drain is when the skilled workers and professionals of one country migrate to another place in search of a better life or simply for higher wages. Most of these skilled workers and professionals are of STEM manpower which includes physicists,
chemists,
mathematicians,
statisticians,
computing
professionals,
engineers, life science professionals, health professionals, nurses, and midwives. According to Purgill (2010), there are both positive and negative effects associated with the movement. The brain drain is known to cause social and economic problems for the government of the country where the workers are leaving as well as for the citizens of that country. In many cases, the country cannot continue to develop when there are many professionals leaving the country. The brain drain in the Philippines is a big problem for both the government as well as for the common citizens. Purgill (2010) said that there are helpful effects for the migrant workers as well as for their families. This is a positive economic effect in the Philippines because it helps raise the amount of money flowing in the country, which is good for the
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY government, and it also raises the amount of money a family of an overseas worker has. Another economic benefit to the professionals leaving the country is that many workers earn higher salaries when working abroad. In an article by Domingo (2012) which appeared in Philippine Daily Inquirer, it presented facts about international migration of science, technology, engineering and mathematics (STEM) manpower-OFWs by the Department of Science and Technologyâ&#x20AC;&#x2122;s Science Education Institute. It stated that the brain drain has become a bigger problem in the last 12 years, as the yearly exodus of people trained in STEM grew by about two and a half times from 1998 to 2009. Results showed that during the 12-year period, STEM deployment grew by an average of 11 percent yearly, peaking at a 59-percent increase in 2001 when 17,756 professionals left, compared with 11,186 the previous year. The administration of President Benigno S. Aquino III through the Commission on Filipino Overseas has taken steps to make sure that migration is incorporated into development planning at the national level together with the development planning institution called National Economic Development Authority (NEDA) to integrate migration into our countryâ&#x20AC;&#x2122;s national development framework (Nicolas, 2011). As a result, the CFO managed to get at least 60 migration-related provisions (in seven out of nine chapters) in the 2011-2016 Philippine Development Plan (PDP) covering such diverse issues as remittances, financial literacy for overseas Filipinos (OFs), OFs appreciating our Philippine culture and heritage, the use of OFs human capital, and
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY ways to reverse the brain drain and turn it into brain gain, strengthening the campaign against human trafficking and many more. A CHED report presented by Alburo and Abella (2002) revealed that the biggest manpower depletion due to migration occurred among the engineers at 28.84% increase from 1990-1998. In 1993-2002, there is an apparent increase of college graduates and at the same time, an increase in professionals working abroad. It can be surmised that Filipino professionals are going out of the country for work. The said increase is predicted to continue for the next years especially that the STEM manpower from other countries are in quandary.
2.7. Associating Self-Efficacy and STEM Career Options Zhu (2007) revealed that self-efficacy is a successful predictor of students’ course-taking. Many factors have been reported to have influences on physics selfefficacy (PSE), but most of them are contextual variables. It was suggested that learning content is also an influencing factor. Physics learning content in high schools is far from being congruent with girls’ development of cognitive psychology and social cognition. This incongruence contributes to the lower PSE of girls, and consequentially leads to their less course-taking in physics. As accorded by Rittmayer and Beier (2009), STEM self-efficacy predicts academic performance beyond one’s ability or previous achievement because confident individuals are motivated to succeed. Students with high science self-
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY efficacy set more challenging goals and work harder to accomplish those goals than students with low science self-efficacy. Additionally, high self-efficacy is associated with greater self-regulation, including more efficient use of problem-solving strategies and management of working time (Rittmayer and Beier, 2009). In addition to expending greater effort, efficacious individuals persist longer to complete a task, particularly in the face of obstacles and adversity. On average STEM self-efficacy is positively related to STEM task performance. The relationship between self-efficacy and performance is reciprocal and ongoing, whereby successful task performance boosts self-efficacy leading to the adoption of more difficult goals. The adoption of more difficult goals requires greater effort, which will positively affect performance. Successful performance with the new, more difficult, goal will, in turn, lead to even greater self-efficacy and thus the cycle continues (Bandura, 1997). Because of the reciprocal self-efficacy â&#x20AC;&#x201C; performance relationship, it is important that beliefs about oneâ&#x20AC;&#x2122;s capabilities are accurate. Being over- or under-confident can undermine performance. Dickie (2013) shared his observations that the various workplaces now expects physics graduates to have a range of skills beyond the content knowledge that has been the focus of traditional physics instruction. In a study done for the American Institute of Physics found that employers expected graduates to not only have problem solving skills but also the interpersonal skills required for work in cooperative groups on complex real-world problems. In this same study graduates reported that in the
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY workplace technical writing and management skills were more important than knowledge of physics. Yet much of current instruction in physics, even at the college level, seems designed only for those who intend pursuing a PhD in physics and not for the vast majority of students for whom physics is but one of many requirements for entry into, for example, engineering or biology. Today's knowledge society requires a much larger fraction of the population to be able to think critically about issues in science and technology and a challenge for educators is to ensure that a greater percentage of high school and college graduates possess these skills. The ability to think critically and reason logically are more relevant for entrants to university than a shallow but wide content knowledge poorly connected to real world situations memorized from the fire-hose of facts and information projected at students by professors determined to cover the curriculum. Unfortunately, much of current practice in college education encourages just the opposite. University of Massachusetts (2011) through its STEM Pipeline Fund Programs reported that (1) high school seniors’ and juniors’ average rating of their level of awareness of, interest in and motivation to pursue STEM related careers was higher after their internship and workshop experiences; (2) a review of raw data indicated there was a positive shift in students’ sense of self-efficacy in math and science and student responses demonstrated more positive views of becoming an engineer on the post survey; (3) pre-post surveys that were designed to assess students’ preference for STEM or engineering activities over other activities did not show any measureable changes after the program, however, the evaluator reported observing a high level of
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY student participation and interest in the classroom activities; (4) students generally indicated positive attitudes and beliefs about STEM subjects prior to the program, which made it difficult to show a positive change in interest and that teachers who responded to a post-only survey reported an increase in their own sense of selfefficacy and their belief in the importance of promoting and teaching STEM; (5) prepost survey results did not indicate any measureable change in students’ interest in pursuing STEM careers, however, other qualitative information gathered from surveys, observations and interviews with teachers, parents and administrators suggest participation may have increased student interest in STEM; (6) students surveyed after the program reported that their interest in and understanding of STEM subjects and careers increased as a result of their participation in science and math fairs; parents’ responses to pre-post surveys did not indicate expectations for their child’s science course taking in high school had changed; and (7) after an exposure to STEM academy, two-thirds of student respondents to a post-program survey agreed they were now more motivated to “study math and science in high school” and “to prepare myself to go to college;” all students agreed with “Math and Science is important for me to be successful in life” and over 80% of agreed that the subjects of math, science and engineering are important and interesting. Purdue University Strategic Planning Steering Committee (2013) reported declining interest, poor preparedness, a lack of diverse representation, and low persistence of U.S. students in STEM (Science, Technology, Engineering and Mathematics) disciplines. It is imperative that as a university, they should respond
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY proactively to the needs for education in STEM and STEM-influenced fields. In order to continue raising the profile of its educational programs nationally and internationally in all disciplines, Purdue must address numerous systemic and programmatic challenges that are fundamental to the education of our students. Weber (2012) recommended that STEM teachers should become familiar with the various ways studentsâ&#x20AC;&#x2122; STEM interests are supported in the school and community and disseminate information about STEM-related activities or programs. STEM teachers should also provide counselors, as well as parents, with up-to-date information on the workforce needs related to STEM careers and the benefits of encouraging both males and females into these fields.
2.8. About Structural Equation Modeling Suhr (2006) defined structural equation modeling (SEM) as (1) a comprehensive statistical approach to testing hypotheses about relations among observed and latent variables; and (2) a methodology for representing, estimating, and testing a theoretical network of (mostly) linear relations between variables. SEM tests hypothesized patterns of directional and nondirectional relationships among a set of observed (measured) and unobserved (latent) variables. Furthermore, Suhr (2006) explained that the two goals in SEM are (1) to understand the patterns of correlation/covariance among a set of variables; and (2) to explain as much of their variance as possible with the model specified. The purpose of the model, in the most common form of SEM, is to account for variation and
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY covariation of the measured variables. Special cases of SEM are regression, canonical correlation, confirmatory factor analysis, and repeated measures analysis of variance. SEM is similar to traditional methods like correlation, regression and analysis of variance in many ways. First, both traditional methods and SEM are based on linear statistical models. Second, statistical tests associated with both methods are valid if certain assumptions are met. Traditional methods assume a normal distribution and SEM assumes multivariate normality. Third, neither approach offers a test of causality. On the other hand, traditional approaches differ from the SEM approach in several areas. First, SEM is a highly flexible and comprehensive methodology. This methodology is appropriate for investigating achievement, economic trends, health issues, family and peer dynamics, self-concept, exercise, self-efficacy, depression, psychotherapy, and other phenomenon. Second, traditional methods specify a default model whereas SEM requires formal specification of a model to be estimated and tested. SEM offers no default model and places few limitations on what types of relations can be specified. SEM model specification requires researchers to support hypothesis with theory or research and specify relations a priori. Third, SEM is a multivariate technique incorporating observed (measured) and unobserved variables (latent constructs) while traditional techniques analyze only measured variables. Multiple, related equations are solved simultaneously to determine parameter estimates with SEM methodology. Fourth, SEM allows researchers to recognize the imperfect nature of their measures. SEM explicitly specifies error while traditional
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY methods assume measurement occurs without error. Fifth, traditional analysis provides straightforward significance tests to determine group differences, relationships between variables, or the amount of variance explained. SEM provides no straightforward tests to determine model fit. The best strategy for evaluating model fit is to examine multiple tests (e.g., chi-square, Comparative Fit Index (CFI), BentlerBonett Non-normed Fit Index (NNFI), Root Mean Squared Error of Approximation (RMSEA). SEM resolves problems of multicollinearity. Multiple measures are required to describe a latent construct (unobserved variable). Multicollinearity cannot occur because unobserved variables represent distinct latent constructs. Finally, a graphical language provides a convenient and powerful way to present complex relationships in SEM. Model specification involves formulating statements about a set of variables. A diagram, a pictorial representation of a model, is transformed into a set of equations. The set of equations are solved simultaneously to test model fit and estimate parameters.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY
Chapter 3 METHODOLOGY
This chapter presents the research method, procedures and materials used in the study. It includes the (1) research design; (2) research locale; (3) the respondents; (4) sampling procedure; (5) the research instruments; (6) data gathering procedure; and (7) statistical treatment.
3.1. Research Design 3.1.1. Descriptive-Correlation This study employed quantitative methods of research. It is descriptivecorrelation in design which involved a survey on (1) conceptions of learning physics (CLP); (2) approaches to learning physics (ALP); (3) physics self-efficacy (PSE); (4) physics learning results (PLR); and (5) students’ career interest to science, technology, engineering and mathematics (STEM) degrees. The perceptions of the fourth year students on the said variables were the main elements of this study. It described whether these students have very high or very low conceptions of learning physics and approaches to learning physics as well as their physics self-efficacy. It also assessed the students’ achievement in physics and their career interest in STEM as a degree choice in pursuing college studies. The students’ STEM career interest were described to be extremely positive or extremely negative.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY The correlational part was focused on the interactions among the five variables. It described whether the associations are significant or not at 0.05 alpha level.
3.1.2. Structural Equation Modeling (SEM) As a theory-generating study, structural equation modeling (SEM) among the variables were done. SEM is a methodology for representing, estimating, and testing a network of relationships between variables (Suhr, 2006). Structural equation modeling is a relatively new statistical technique. It is also referred to as covariance structure modeling, because covariance, instead of correlation, is analyzed in SEM. The main task of SEM is â&#x20AC;&#x153;to determine the goodness of fit between the hypothesized model and the sample dataâ&#x20AC;? (Byrne, 1994). A good fit suggests that the hypothesized relations among constructs are acceptable; a bad fit suggests the rejection of the theorized relations among constructs in the model. A structural equation model normally consists of a measurement model and a structural model. The measurement model delineates relations between measured variables and the latent variables for which they are used as approximations. All latent factors are allowed to co-vary in the measurement model. The structural model specifies the hypothesized causal structure among latent variables which is indicated as a path or arrow connecting the two variables. A two-step process was used to test the measurement and structural models. First, the initial measurement models (Figures 11 and 12 on pages 118 and 119) were tested by allowing all latent factors to co-vary. This null model served as a basis for
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY the computation of some of the fit indices. Maximum likelihood estimation was performed and model fit was tested using the joint criteria of CFI>0.90 and AGFI>0.90 or CFI>=0.90 and RMSEA<0.05 (Hu & Bentler, 1999). The purpose of the first step was to evaluate the contributions of the multiple measures to the measurement of the latent constructs. Testing the validity of the measurement model before evaluating the structural model allows the research to distinguish rejections of the proposed model because of problems stemming from measurement inadequacies from problems related to the actual proposed theory (Mueller, 1996). If the initial measurement model did not fit satisfactorily, new models would be developed as a refinement of measurement model based on the computation of modification index (MI). The modified models will be retested again. The final measurement model must gain significant goodness of fit and retain the revised specification throughout all analyses of the structural model in the next phase. The second step tested the theorized causation of the structural model, which was in the direction of the key constructs. First, the initial structural models (see Figures 1 and 2) was imposed on the final measurement model. Maximum likelihood (ML) was used to estimate the path coefficients between the latent variables; same criteria of fit indices (CFI>0.90 and AGFI>0.90 or CFI>=0.90 and RMSEA<0.05) were used to test the fit of the structural model. In sum, SEM analysis processes for measurement model and structural model include the following: (1) model specification; (2) model identification; (3) model estimation; (4) assessment of data-model fit; and (5) possible model modification and
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY re-estimation. These processes served as guide in the discussion of SEM analyses of the hypothesized models (see Figures 1 and 2) on the subsequent chapter. The SEM as an approach was utilized to examine the patterns of correlation and/or covariance of conceptions of learning physics, approaches to learning physics and physics self-efficacy. Likewise, the SEM was used to establish patterns of correlation and/or covariance of physics self-efficacy, physics learning results and studentsâ&#x20AC;&#x2122; career interest to STEM degrees. This author followed the research methodology in performing SEM by the Eins Review and Statistical Consultancy Center (2011) as summarized in Figure 3 below.
THEORY MODEL CONSTRUCTION INSTRUMENT CONSTRUCTION INTERPRETATION DATA COLLECTION MODEL TESTING RESULTS
Figure 3. Model on the basic approach in performing structural equation modeling.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY 3.2. Research Locale The researcher carried out this study in his current affiliation in the objective to contribute significant findings to it. Thus, a survey was conducted in the public secondary schools in the East II district of the Division of Cagayan de Oro City for school year, 2013-2014. The Division of Cagayan de Oro City was used to be a part of the Division of Misamis Oriental until in the year 1963. The Division of Cagayan de Oro City is composed of two districts following the congressional territorial boundaries, District I and District II. These districts are separated with a natural demarcation which is the trailing Cagayan de Oro River. However for communication and delegation purposes, this Division is also categorized into four districts, North, West, South and East taking the pattern of the primary directions of the map. Figure 4 on the next page shows the survey locations of the seven schools in the East II district of Cagayan de Oro City. It is a screenshot of the terrain via Google Map which was accessed online.
3.3. The Respondents The respondents of this study were 317 fourth year public high school students from the East II District schools in the Division of Cagayan de Oro City. As prescribed by the Basic Education Curriculum of DepEd, these fourth year high school students focus on Physics as their science subject. Since the emphases of this study is on physics learning, it is appropriate to tap the fourth high school students.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY
Figure 4. Google map screenshot of the survey locations in the East II district of the Division of Cagayan de Oro City. Legend: (A) East Gusa National High School (B) Cugman National High School (C) Tablon National High School (D) Agusan National High School (E) Puerto National High School (F) Balubal National High School (G) Bugo National High School
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Among these respondents, 141 were males and 176 were females. Their ages ranged from 16-20 years old. These respondents came from different socio-economic classes and academic achievement groups.
3.4. Sampling Procedure 3.4.1. SEM Assumptions on Sample Size Structural equation modeling is a large sample technique (Bentler, 1993). Both the estimation methods and tests of model fit are based on the assumption of large samples. In general, a sample size of at least 200 observations would be an appropriate minimum. Bentler and Chou (1987) suggested that the ratio of sample size to number of free parameters can go as low as 5:1 with normally distributed data. For this study, a sample of 317 high school students should be sufficient to test the hypothesized model with 16 free parameters.
3.4.2. Identification Process Of the 37 national high schools in the Division of Cagayan de Oro City, seven national high schools in the eastern part of District II were purposively chosen. One inclusive criterion is that these schools are integrating the DLP methodology in their instructional practices. Another is that these schools are operating under the Basic Education Curriculum (BEC). The emphasis of this study is on the profundity of the senior public high school studentsâ&#x20AC;&#x2122; physics self-efficacy and to the factors that were
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY investigated by this researcher, and not on the span nor the depth of representation of the students in Cagayan de Oro City. Furthermore, systematic random sampling procedure with proportionate allocation guided the researcher in identifying the respondents for this study. Using Slovinâ&#x20AC;&#x2122;s formula, the sample size of 317 students was determined. With proportionality procedure, x = (n/N) x 100, 21% of the actual number of fourth year high school students was prorated to the schools to serve as the respondents of the study. Table 1 presents the distribution and the total number of respondents from the seven national high schools.
Table 1. Distribution of the respondents. East II District Schools
N
n
1
Agusan National High School
405
85
2
Balubal National High School
60
12
3
Bugo National High School
383
79
4
Cugman National High School
200
42
5
East Gusa National High School
240
50
6
Puerto National High School
150
31
7
Tablon National High School
90
18
1,528
317
TOTAL
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY 3.5. Validity and Reliability of Instruments This study used survey questionnaires to gather the necessary data on conceptions of learning physics, approaches to learning physics, physics self-efficacy, physics learning results and career interest to STEM of the senior high school students. The researcher adapted the following tools: (1) Self-Efficacy in Learning Science Survey (Tsai, 2002); (2) Conceptions of Learning Questionnaire (Tsai, 2002); (3) Questionnaire on Approaches to Learning (Tsai, 2002); and (4) STEM Semantics Survey (Wood, Knezek & Christensen, 2010).
3.5.1. Content and Face Validity Substantial revisions were made to suit the instruments to local conditions such as the use of language, grammar and syntax, and the curricular set-up of DepEd. All the questionnaires were then reviewed for content and face validity by a panel of content specialists in DepEd-Cagayan de Oro City Division (see Appendices A and B). The panel is composed of (1) a practicing physics teacher for more than 10 years to assess the integration of Dynamic Learning Program-Learning Physics as One Nation in the questionnaires; (2) an English communication arts specialist to check for grammar propriety; and an (3) education program supervisor for secondary science for current assessment standards. Their comments and suggestions were integrated in the final draft of the questionnaires prior the pilot and actual floating of these survey tools.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY 3.5.2. Construct Validity and Reliability The said questionnaires were pilot-tested to 30 trial-respondents for construct validity and reliability statistics. The Physics Self-Efficacy (PSE) Survey which contained seven items, has a range value of 0.44 in an inter-item correlation test and the value of Cronbachâ&#x20AC;&#x2122;s alpha coefficient is 0.84. The result of the exploratory factor analysis indicated that the factor loadings of all the seven items were weighted greater than 0.80 (see Appendix E). These seven items could thus be treated as one single factor that adequately measures studentsâ&#x20AC;&#x2122; physics self-efficacy. The 25-item Conceptions of Learning Physics (CLP) Questionnaire has an inter-item correlation coefficient range value of 0.74. The resulting Cronbachâ&#x20AC;&#x2122;s alpha coefficient was 0.87 and the total variance explained was 93.2%. Confirmatory factor analysis perfectly supported the six-subscale structure of the CLP survey (đ?&#x153;&#x2019; 2 =8.302, df=9; CFI=1.000; AGFI=0.980; RMSEA=0.000). The Questionnaire on Approaches to Learning Physics (ALP) which has 15 items, has a coefficient range value of 0.66 in an inter-item correlation test and the resulting Cronbachâ&#x20AC;&#x2122;s alpha coefficient is 0.76. In addition, the results of the confirmatory factor analysis held the four-subscale structure of the ALP survey (đ?&#x153;&#x2019; 2 =3.402, df=2; CFI=0.994; AGFI=0.973; RMSEA=0.047). Details of the confirmatory factor analysis are discussed on Chapter 4. All the tools for physics self-efficacy, conceptions of learning physics and approaches to learning physics, which used a 5-point Likert scale ranging from strongly agree (5 points) to strongly disagree (1 point), demonstrated statistical validity
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY and reliability (see Appendix C for statistical output). Figure 5 below shows the distribution of the items for all the subscales.
MEMORIZING Items 1,2,3,4,5 LOW-LEVEL CONCEPTIONS OF LEARNING PHYSICS
TESTING Items 6,7,8,9,10 CALCULATING AND PRACTICING Items 11,12,13,14 INCREASING ONE’S KNOWLEDGE Items 15,16,17,18,19
HIGH-LEVEL CONCEPTIONS OF LEARNING PHYSICS
APPLYING Items 20,21,22 UNDERSTANDING Items 23,24,25
DEEP APPROACHES TO LEARNING PHYSICS
SURFACE APPROACHES TO LEARNING PHYSICS
PHYSICS SELF-EFFICACY
DEEP MOTIVE Items 1,2,3 DEEP STRATEGY Items 4,5,6,7,8 SURFACE MOTIVE Items 9,10,11 SURFACE STRATEGY Items 12,13,14,15 Items 1,2,3,4,5,6,7
Figure 5. Item distribution of the scales among the survey tools.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Moreover, the STEM Semantics Survey has a total of 25 items. There are five sets of polar adjectives in five subscales. The Cronbach’s alpha coefficients among its components are as follows: science – 0.78, math – 0.73, engineering – 0.67, technology – 0.76 and STEM Career – 0.85. For the composite group, the Cronbach’s alpha coefficient is 0.91 (see Appendix C for statistical output). Lastly for the Physics Content Knowledge Test, it contained 50 multiple choice questions. The table of specification for these items are found in Appendix B. Using Kuder-Richardson 21 test for its internal consistency reliability, it yielded a coefficient value of 0.77. For this tool’s construct validity, some of the items were revised after having an average item difficulty index of 0.52.
3.5.2. Scoring Guide The scoring guide for physics self-efficacy, conceptions of learning physics and approaches to learning physics and its qualitative description is found on Table 2.
Table 2. Scoring guide for Physics self-efficacy, conceptions of learning Physics and approaches to learning. Scale
Range
Qualitative Description
5
4.21 – 5.00
Very High
4
3.41 – 4.20
High
3
2.61 – 3.40
Moderate
2
1.81 – 2.60
Low
1
1.00 – 1.80
Very Low
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Scoring, qualitative description, and qualitative interpretation for the students’ perception on STEM disciplines and careers (Siegle, 2010) are found on Table 3.
Table 3. Scoring guide for students’ perception in STEM disciplines and careers. Scale
Range
Description
Qualitative Interpretation
7
6.11 – 7.00
Completely Agree
Extremely Positive
6
5.26 – 6.10
Mostly Agree
Highly Positive
5
4.41 – 5.25
Slightly Agree
Positive
4
3.56 – 4.40
Neither Agree nor Disagree
Fair
3
2.71 – 3.55
Slightly Disagree
Negative
2
1.86 – 2.70
Mostly Disagree
Highly Negative
1
1.00 – 1.85
Completely Disagree
Extremely Negative
The mean percentage scores attained by the students in the Physics Content Knowledge Test were interpreted based on DepEd Memorandum 158, s.2011 to which the performance of the students are rated on the corresponding levels of proficiency as seen in Table 4.
Table 4. Scoring guide for Physics learning results. Mean Percentage Score
Level of Proficiency
90 - above
Advanced
85 – 89
Proficient
80 – 84
Approaching Proficiency
75 – 79
Developing
74 - below
Beginning
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Featured in DepEd Memorandum 158, s.2011 is the standards-based student assessment and rating system for the secondary level. Being standards-based, this system considers what the students knows (knowledge), can do (process or skills, i.e. how the students makes sense of or constructs meanings out of the facts and information), and understands (understandings and meanings made). The students are described based on the following levels of proficiency: (1) Beginning – the student at this level struggles with his/her understanding; (2) Developing – the student at this level possesses the minimum knowledge and skills and core understanding; (3) Approaching Proficiency – the student at this level has developed the fundamental knowledge and skills and core understandings and, with little guidance from the teacher and/or with some assistance from peers, can transfer these understandings through authentic performance tasks; (4) Proficient – the student at this level has developed the fundamental knowledge and skills and core understandings and can transfer them independently through authentic performance tasks; and (5) Advanced – the student at this level exceeds the core requirements in terms of knowledge, skills and understandings, and can transfer them automatically and flexibly through authentic performance tasks.
3.6. Data-Gathering Procedure This researcher asked permission from the Office of the Schools Division Superintendent and among the school heads in the administration of the survey tools in the chosen public high schools on October-November 2013 (see Appendix F). The
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY actual survey among the schools was personally managed by this researcher. The respondents were asked to complete the five survey tools in one setting with no time limit. In most sessions, the tools were completed approximately in an hour. The respondents were assured of the confidentiality of their answers as well and that their answers do not reflect their ability in answering the questions. The gathered data were then collated, coded and tabulated for statistical processing.
3.7. Statistical Treatment Consolidation and data analyses through appropriate statistical treatment were done. The following statistical tools and techniques were employed: Measures to Meet Assumptions. Through Kolmogorov-Smirnov normality test, assumptions on normality and linearity were met (see Appendix N). No assumptions were imposed on multicollinearity as it cannot occur in structural equation modeling because unobserved variables represent distinct latent constructs (Suhr, 2006). Moreover, boxplot graph method was used to identify outliers from the data set on the respondentsâ&#x20AC;&#x2122; conceptions of learning physics, approaches to learning physics, physics self-efficacy, physics learning results, and on their STEM career interest. Data input errors were consequently corrected. Boxplots were then redrawn as seen in Appendix D. One-Factor Model Confirmatory Factor Analysis (CFA). Confirmatory factor analysis as a technique seeks to confirm if whether the theoretical constructs is
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY reflected in the data. As an application of SEM, it was used to determine the construct validity which is the extent to which subscale designed to measure a particular factor actually do so. Therefore, it is applied to validate the subscales used in this present study to ensure that the subscales were suitable for use. So this was processed to support the six-subscale structure of the conceptions of learning physics survey, namely, (1) memorizing; (2) testing; (3) calculating and practicing; (4) increasing one’s knowledge; (5) applying; and (6) understanding. Also for the four-subscale structure of the approaches to learning physics survey, to mention, (1) deep motive; (2) deep strategy; (3) surface motive; and (4) surface strategy. Exploratory Factor Analysis (EFA). Exploratory factor analysis is applied to validate if the seven items could be treated as one single factor to measure the students’ physics self-efficacy. Frequency and Percentage. These were used to tally the socio-demographic information of the respondents and on descriptive statistics across all variables. Weighted Mean and Standard Deviation. These were used to describe the data on students’ conceptions of learning physics, approaches to learning physics and physics self-efficacy. Likewise it was used for the data on students’ physics learning scores and on their STEM career interest. Pearson Product Moment Correlation (PPMC). Pearson r was used to determine the magnitude of the relationships among the variables set for this study. The correlation probability value (p-value) were based to establish the significance of the relationship.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Structural Equation Modeling (SEM). SEM techniques like the regression analysis and path diagramming were both used to test and represent the relationships of the measured and latent variables (Suhr, 2006). Probability value (p-value) were likewise based to ascertain the significance of the relationships and of the model in totality. Graphical presentations (i.e. path diagram) were generated to show the network of relationships of the variables. Measures of Fit of Model. These were used to assess overall fit of the model produced by the structural equation. Although the estimations minimize the differences between the observed data and the proposed model, a model still may not fit the data on an acceptable level. Statistical tests can be performed to test the fit between the observed data and the hypothesized model. There are three categories of fit indices: (1) absolute fit indices; (2) parsimonious fit indices; (3) and incremental fit indices, through which model fitness assessment can be made. Absolute fit indices, such as the model Chi-square statistic, the Standardized Root Mean Square Residual (SRMR), and the Goodness of Fit Index (GFI), improve as the discrepancy between the observed and reduced (co)variances decrease. These fit indices tend to improve as the complexity of the model increases. The lower the Chi-square, the better the model fits. It is recommended that the ratio of Chi-square to its degree of freedom should be less than 3. Parsimonious fit indices, such as the Adjusted Goodness of Fit Index (AGFI) and the Root Mean Square Error of Approximation (RMSEA), take into account not only the overall absolute fit but also the degree of complexity required to achieve that
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY fit. There indices indicate the best model fit when there is good absolute fit and the models are relatively simple (i.e. have few parameters). Incremental fit indices, such as the Normal Fit Index (NFI) and the Comparative Fit Index (CFI), test the fit of the model in relation to a baseline model with fewer parameters. Judgments regarding data-model fit or misfit are based on several criteria. First, individual parameter estimation and associated statistics must be scrutinized for substantive and/or statistical impossibilities. Second, multiple overall fit indices should be considered since each was developed for a different purpose and comes with certain disadvantages (Mueller, 1996). Joint criteria for acceptable fit (Hu and Bentler, 1999) have been adopted in this study. This criteria particularly uses the chi-square and df ratio, CFI, RMSEA, and AGFI.
Descriptive and inferential statistics were done using statistical software (i.e. MS Excel, Minitab 16) which the confidence level was set at 95% and 99%. Confirmatory factor analysis and structural equation modeling were processed using IBM SPSS Amos 22 (Temporary Edition) at the same confidence level. Compared with the standard edition, the IBM SPSS Amos 22 (Temporary Edition) has a limitation on the number of measured variables that can be included in the model. This restriction however is not detrimental to the processes carried in this study.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY
Chapter 4 DATA PRESENTATION AND ANALYSIS
This chapter presents, analyzes and interprets the data obtained from the respondents and ran through appropriate statistical techniques. The presentation of results is organized based on the order of the research problems stated in Chapter 1.
4.1. Findings and Discussion 4.1.1. Studentsâ&#x20AC;&#x2122; Conceptions of Learning Physics (CLP) Conceptions
of
learning
physics
represents
studentsâ&#x20AC;&#x2122;
beliefs
and
understanding of the nature of learning Physics. This is classified into the following: high level and low level (Tsai, 2004). A 25-item survey tool, Conceptions of Learning Physics Questionnaire (see Appendix A), was used to gather data on conceptions of learning physics to which it was interpreted using the scoring guide in Table 2 (see at page 58) and processed through descriptive statistics.
4.1.1.1. On Low-Level Conceptions of Learning Physics (CLP) Low-level conceptions of learning physics denote that these are passive and transmissive view of science learning (Chiou & Liang, 2012). It is characterized by the following subscales: (1) memorizing; (2) testing; and (3) calculating and practicing. In the Conceptions of Learning Physics Questionnaire, there are five items for
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY memorizing, five items for testing, and four items for calculating and practicing. Table 5 on the following page shows the level of respondentsâ&#x20AC;&#x2122; low-level conceptions of learning physics for every subscale and its corresponding indicators. Almost 50% of the students have high conceptions on memorizing. They think that learning physics means memorizing the definitions, formulas, and laws found in the activity sheets. This indicator has the highest mean of 3.88 among the set of indicators for the memorizing subscale. Close to this is a follow-up indicator that says the students think learning physics is memorizing the important concepts found in the activity sheets (đ?&#x2018;ĽĚ&#x2026; =3.82). As an example during the first quarter, the students came across topics on linear motion where there are definitions and equations to use for displacement, distance, speed, velocity and acceleration. These students need to memorize the different equations and concepts for them to do calculations in problemsolving items. They are aware that they cannot proceed in performing the solution if they miss remembering the correct equations and concepts distinctive to each problem. This lower level conception of learning physics emphasized increase in the quantity of knowledge gained and memory work than process and inquiry mode of learning. Recognizing the value of this conception subscale, the students in this study characterized physics learning as rote memorizing and recalling exercise such that even problem-solving is procedural in nature. Evidently, their responses on memorizing indicators are all high which manifests that this specific cognitive skill may be basic but effective for them in learning physics.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Table 5. Descriptive statistics on low-level conceptions of learning Physics. Range
Qualitative Description (QD)
Memorizing
Calculating and Practicing
Testing
f
%
f
%
f
%
4.21 â&#x20AC;&#x201C; 5.00
Very High
46
14.51
14
4.42
137
43.22
3.41 â&#x20AC;&#x201C; 4.20
High
158
49.84
118
37.22
127
40.06
2.61 â&#x20AC;&#x201C; 3.40
Moderate
97
30.60
165
52.05
44
13.88
1.81 â&#x20AC;&#x201C; 2.60
Low
14
4.42
20
6.31
9
2.84
1.00 â&#x20AC;&#x201C; 1.80
Very Low
2
0.63
0
0.00
0
0.00
Overall Mean
3.70
3.46
4.01
Standard Deviation (SD)
0.59
0.80
0.65
QD
High
High
High
Indicators
đ?&#x2018;ĽĚ&#x2026;
SD
QD
3.88
0.99
High
3.82
0.89
High
3.57
0.91
High
3.82
0.91
High
3.44
0.98
High
3.24 3.57 2.91 3.66 3.88 4.05
0.84 0.81 1.18 0.82 3.38 0.85
Moderate High Moderate High High High
4.04
0.82
High
4.02
0.89
High
3.91
0.83
High
I think learning physics meansâ&#x20AC;Ś 1 2 3 4 5 6 7 8 9 10 11 12 13 14
memorizing the definitions, formulas, and laws found in the activity sheets. memorizing the important concepts found in the activity sheets. memorizing the proper nouns found in the activity sheets that can help answer the teacherâ&#x20AC;&#x2122;s questions. memorizing scientific symbols, scientific concepts, and facts. memorizing the content in the activity sheets just like learning Araling Panlipunan. getting high scores on physics exams. answering correctly on physics exams. studying physics only if there is a test. doing well on math-related tests. making relationship between learning physics and taking tests. practicing calculation and solving problems. learning calculations or problem-solving to help me improve my performance in other subjects. knowing how to use the correct formulas when solving problems. knowing that the way to learn physics well is to constantly practice calculations and problem-solving.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Among the students, more than half have moderate conceptions on testing. They have moderate view on getting high scores on physics exams (đ?&#x2018;ĽĚ&#x2026; =3.24) partly because they study only if there is an exam (đ?&#x2018;ĽĚ&#x2026; =2.91). Possibly, the students still have inferiority in assessment routines just as it is widely established that students have negative perceptions or even anxieties in test-taking (Ashcraft & Krause, 2007). There is unanimity in their responses for this indicator (SD=1.18) although these students make relationship between physics and taking tests (đ?&#x2018;ĽĚ&#x2026; =3.88). These students also think that learning physics is doing well in math-related tests (đ?&#x2018;ĽĚ&#x2026; =3.66). Physics being a practical science uses figures and data such that students make sense of numbers. As an example, numerical values that describe motion or estimate the amount of force is something that can be directly observed in physics. The students may have developed an appreciation towards physics considering that concepts with numbers are concrete and tangible. They must have realized that learning physics will make them good in other tests that involve numbers. In the same vein, these students thought that learning physics means learning calculations and problem-solving for them to improve their performance in other subjects (đ?&#x2018;ĽĚ&#x2026; =4.04) like mathematics. There are topics in physics that coincide very well in Math 4 such as the trigonometric functions. An example of this is the use of tangent function in solving for the measure of degree of elevation of a resultant vector. Exposure of certain concepts in more than one subject is apparently more beneficial to the students. It encourages multidisciplinary and thematic coherence of concepts across the subjects.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY The students find it important on how to use correct formulas when solving problems and these students have high perception for this indicator (đ?&#x2018;ĽĚ&#x2026; =4.02). Part of the skill on calculating is identifying appropriate equation in order to arrive at the correct answer in problem-solving exercises. The use of equations in classical mechanics is quite tricky. As an instance, the formula to solve for distance and displacement of a moving object is practically the same but it has slight dissimilarity in concept. These students may have developed a sense of alertness in this complication. To substantiate the aforementioned impressions, 43% of the students have very high level of conceptions on calculating and practicing. Altogether, more than 80% held high perceptions on this particular subscale. In fact, it is of highest mean (đ?&#x2018;ĽĚ&#x2026; =4.05) that these students think learning physics means practicing calculations and solving problems. It is a common notion that the students, even teachers, think of mathematics with problem-solving exercises when they think of physics. Among the three subscales on low-level conceptions of learning physics, it is the calculating and practicing which has the highest mean (đ?&#x2018;ĽĚ&#x2026; =4.01) and it is qualitatively described as high. It is always noteworthy to mention that physics students view physics learning with mathematics. Anyhow, all the three subscales are high which would imply that these students typify passive and transmissive view of physics learning. According to Tsai (2004), low-level conceptions of learning physics is like copying what the teacher or the books said and then re-present the information intact.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Essentially, it is duplicating knowledge. If to be based in the revised Bloomâ&#x20AC;&#x2122;s taxonomy of educational objectives (2001), these low-level conceptions of learning physics would correspond to the low cognitive levels. These students may have opted to possess these low-level conceptions of learning physics because it is easier and more convenient for them. Memorizing the important concepts in the activity sheets and studying physics only if there is a test are some of the indicators that these students have highly affirmed (SD=0.99 and 1.18). If these are how the students view physics learning, there will be complications later on during the assessment part where apparently teachers design tests with few items to represent knowledge level.
4.1.1.2. On High-Level Conceptions of Learning Physics (CLP) High-level conceptions of learning physics suggests that students learning physics in an active and constructive view (Chiou & Liang, 2012). High-level conceptions of learning physics is represented by the following subscales: (1) increasing oneâ&#x20AC;&#x2122;s knowledge; (2) applying; and (3) understanding. In the Conceptions of Learning Physics Questionnaire, there are five items for increasing oneâ&#x20AC;&#x2122;s knowledge, three items for applying, and three for understanding. Table 6 on the next page shows the level of respondentsâ&#x20AC;&#x2122; high-level conceptions of learning physics for every subscale and its corresponding indicators. In the first subscale, increasing oneâ&#x20AC;&#x2122;s knowledge, 47.63% of the respondents have high conceptions. These students think that learning physics means knowing scientific facts that they did not know before (đ?&#x2018;ĽĚ&#x2026; =3.92) and getting more knowledge
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Table 6. Descriptive statistics on high-level conceptions of learning Physics.
Range
Qualitative Description (QD)
Increasing Oneâ&#x20AC;&#x2122;s Knowledge
Applying
Understanding
f
%
f
%
f
%
4.21 â&#x20AC;&#x201C; 5.00
Very High
80
25.24
62
19.56
84
26.50
3.41 â&#x20AC;&#x201C; 4.20
High
151
47.63
126
39.75
129
40.69
2.61 â&#x20AC;&#x201C; 3.40
Moderate
72
22.71
115
36.28
95
29.97
1.81 â&#x20AC;&#x201C; 2.60
Low
14
4.42
13
4.10
9
2.84
1.00 â&#x20AC;&#x201C; 1.80
Very Low
0
0.00
1
0.32
0
0.00
Overall Mean
3.85
3.65
3.79
Standard Deviation (SD)
0.65
0.63
0.67
QD
High
High
High
Indicators
đ?&#x2018;ĽĚ&#x2026;
SD
QD
3.78 3.92
0.93 0.86
High High
3.92
0.90
High
3.90
0.88
High
3.74
0.92
High
3.68
0.84
High
3.46
0.82
High
3.83
0.83
High
3.98 3.85
0.81 0.79
High High
3.56
0.91
High
I think learning physics meansâ&#x20AC;Ś 15 16 17 18 19 20 21 22 23 24 25
acquiring knowledge that I did not know before. knowing scientific facts that I did not know before. getting more knowledge about natural phenomena and topics related to nature. helping me acquire more facts about nature. increasing my knowledge of natural phenomena and topics related to nature. knowing how to apply methods I already know to unknown problems. solving or explaining unknown questions and phenomena. acquiring knowledge and skills to enhance the quality of our lives. understanding scientific knowledge. understanding the connection between scientific concepts. understanding and knowing the unknown questions and phenomena before.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY about natural phenomena and topics related to nature (đ?&#x2018;ĽĚ&#x2026; =3.92). Physics as a distinct discipline in broad body of knowledge, is full of concrete and dynamic scientific facts. It entices and amazes students because physics concepts are discernible and thus, interesting. In a physics classroom, a teacher will always get mixed and hyped responses when the students are asked if a piece of bond paper will fall in synch with a book. After a simple demonstration, students will get the impression on how physics can be so realistic and relatable to nature. As such, the students think that physics learning will help them acquire more facts about nature (đ?&#x2018;ĽĚ&#x2026; =3.90). The falling paperbook demonstration is an example of gravityâ&#x20AC;&#x2122;s interaction on different bodies. Gravity as a concept in physics opened a profound understanding on the behavior of things in the physical environment. Almost 40% of the students held high level of conceptions in applying. They think that learning physics is acquiring knowledge and skills to enhance the quality of their lives (đ?&#x2018;ĽĚ&#x2026; =3.83). It is their belief that learning physics will equip them with important concepts that make them a knowledgeable or a learned person. Since physics as a practical science is very relatable, it is deemed by these students that learning physics will give them life-long skills. In cognizance, one of the important objectives of DepEd to all Filipino learners is equipping the students with life-long skills so they will become productive members of the society. For the last subscale in high-level of conceptions of learning physics, 40.69% have high conceptions in understanding. They think that physics learning means understanding scientific knowledge (đ?&#x2018;ĽĚ&#x2026; =3.98) and understanding the connection
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY between scientific concepts (đ?&#x2018;ĽĚ&#x2026; =3.85). It is deemed anticipatory for students to connect that physics is a realm of science and it is implicit for them to understand as such. These students are made aware that physics is the concentration in Science and Technology IV under the Basic Education Curriculum (BEC). One of the first topics in physics in the first quarter is a paradigm showing how physics is interrelated to other branches of science such as chemistry, biology, earth science, engineering and mathematics. This presents the idea that learning physics occasions the understanding of network of concepts in science. These students must have appreciated this structure to have these indicators with the highest mean among the items for this subscale. Corollary to this, the physics topics in the Physics Essential Portfolio (PEP) of Bernido and Bernido (2008) are arranged in increasing difficulty and complexity. This allows the students to appreciate earlier topics because they are important and applicable to the next topics. There are also portions in the PEP where there are review sections for chemistry and other sciences which are necessary for the present topic and competency. This provides students to clarify the connections of science concepts. All the subscales of high-level conceptions of learning physics are quantitatively described as high with the subscale on increasing oneâ&#x20AC;&#x2122;s knowledge as the highest (đ?&#x2018;ĽĚ&#x2026; =3.85). All the 11 indicators have high conceptions level. According to Tsai (2004) on high-level conceptions of learning physics, students view learning science in an active and constructive view. It suggests that learning involves a process of
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY transforming what students have perceived into a meaningful whole. These levels will correspond to the higher order thinking skills in the taxonomic cognitive levels of Bloomâ&#x20AC;&#x2122;s (2001). Appreciably, these students largely conceived physics learning to be increasing their knowledge of natural phenomena and topics related to nature, and understanding and knowing the unknown questions and phenomena before (SD=0.92 and 0.91). They are able to relate physics concepts with the singularities and occurrences in the physical world. They have made sense of the concepts and perhaps they can communicate them to technology and to the society. These application and synthesis skills will be of great value especially during classroom assessment.
3.79 3.65 3.85 4.01 3.46 3.7
3.1
3.2
3.3
3.4
3.5
3.6
3.7
3.8
3.9
4
Understanding
Applying
Increasing One's Knowledge
Calculating and Practicing
Testing
Memorizing
4.1
Figure 6. Studentsâ&#x20AC;&#x2122; levels on the subscales of conceptions of learning physics.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY In sum, the students have high level perceptions in the six subscales of the conceptions of learning physics. As can be seen in Figure 6, among the subscales, it is calculating and practicing that has the highest representation (đ?&#x2018;ĽĚ&#x2026; =4.01) while testing has the least (đ?&#x2018;ĽĚ&#x2026; =3.46). It is inferred that senior students most highly conceive physics learning as practicing calculation and solving problems. As previously mentioned, it is a common scenario for students to relate that physics always entail numbers and that the way to learn physics well is to constantly practice calculations and problemsolving.
Table 7. Final description of the studentsâ&#x20AC;&#x2122; level on conceptions of learning Physics.
Range
Qualitative Description (QD)
Low-level CLP
High-level CLP
OVERALL
f
%
f
%
f
%
4.21 â&#x20AC;&#x201C; 5.00
Very High
46
14.51
71
22.08
50
15.77
3.41 â&#x20AC;&#x201C; 4.20
High
200
63.09
171
53.94
191
60.25
2.61 â&#x20AC;&#x201C; 3.40
Moderate
69
21.77
70
22.08
74
23.34
1.81 â&#x20AC;&#x201C; 2.60
Low
2
0.63
5
1.58
2
0.63
1.00 â&#x20AC;&#x201C; 1.80
Very Low
0
0.00
0
0.00
0
0.00
Mean
3.72
3.77
3.74
Standard Deviation (SD)
0.49
0.56
0.48
QD
High
High
High
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Table 7 displays the final description of the studentsâ&#x20AC;&#x2122; level on conceptions of learning physics. It can be gathered that higher percentage of the students held high low-level conceptions of learning physics than the number of percentage who have high high-level conceptions of learning physics. Although as a whole in treating this scale as a single construct, majority of the students have high conceptions of learning physics (đ?&#x2018;ĽĚ&#x2026; =3.74). It manifested that these students have high beliefs and understanding of the nature of learning physics. According to Chiou and Liang (2012), a student may possess more than one category of conception of learning at the same time. A student may conceive learning physics as a process of developing understanding (high-level CLP) but still thinks that it is necessary to memorize important scientific concepts (low-level CLP). So it would have limited or no value to assign an individual into either the lower-level or higherlevel group. Instead, it can be surmised that a student may hold multiple conceptions of learning physics. Therefore it would be of merit to identify which level of conception is dominant among the students. With the same survey tool from Tsai (2004), this result goes well with latest studies of other Asian high school students where they also held high conceptions of learning physics (Chiou & Liang, 2012; Chan, 2012; Lee, Johanson, and Tsai 2008). Other studies on conceptions of learning physics used different survey tools such that of Biggs (1992) and Purdie and Hattie (2002). Results somehow indicated similar reports that high school students have high conceptions of learning physics (Ghorban,
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Moradi & Gholam, 2013; Alamdarloo, Moradi & Dehshiri, 2013; Paul, Pillay & Barry, 2003). Imperatively in the learning process, Dickie (2003) cited a number of authors who have examined the consequences of these conceptions of learning for the teaching of physics. It all points to the effectiveness of active engagement on teaching methods in improving student’s conceptions of learning physics compared with the ineffectiveness of traditional ways of interaction.
4.1.2. Students’ Approaches to Learning Physics (ALP) A generic way of describing “what the student does” is precisely in terms of their ongoing approaches to learning. Biggs, Kember and Leung’s (2001) and Tsai’s (2009) have identified two approaches to learning science, the deep and surface approaches. A 15-item survey tool, Questionnaire on Approaches to Learning Physics (see Appendix A), was used to gather data on ALP to which it was interpreted using the scoring guide in Table 2 (see at page 57) and likewise processed through descriptive statistics.
4.1.2.1. On Deep Approaches to Learning Physics (ALP) Deep approach is characterized as an intrinsic motivation (deep motive) to actively comprehend and integrate the new learning materials with existing ideas (deep strategy). In the Approaches to Learning Physics Questionnaire, there are three items (item numbers 1, 2, 3) to describe deep motive and five items (item numbers 4,
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY 5, 6, 7, 8) for deep strategy. Table 8 shows the level of respondentsâ&#x20AC;&#x2122; surface ALP for every subscale and its corresponding items. The students equally held high and moderate approaches on deep motive (40% for each level) but the overall mean is 3.47 which is described as high. They learn physics by finding the activity sheets interesting (đ?&#x2018;ĽĚ&#x2026; =3.85). As a backgrounder, the students are encouraged to accomplish one activity sheet per session in the structure of Learning Physics as One Nation (LPON). These prepared activity sheets were based on the Physics Essential Portfolio of the Dynamic Learning Program (DLP) of Bernido and Bernido (2008). The activity sheet includes the activity title which encapsulates the main idea to be learned. This is followed by one or two learning targets. These are similar to the objectives written in lesson plans and follow the same principles in writing of instructional objectives (simple, clear, specific, behavioral and attainable), but are phrased from the point of view of the student. References used by the teacher are also indicated on the activity sheet. The learning activity follows the classical format starting with brief concept notes (introduction, background, concept or main idea to be learned). This is followed by one or two illustrative examples, and then the questions, exercises, graphs, drawings and other tasks. An activity sheet must be accomplished by the students with no prior intervention coming from the teachers. So finding the activity sheets interesting is first and foremost like a sparkplug in learning physics. According to Bernido and Bernido (2008), learning of new material initially without a guide enhances analytical thinking and focuses inquiry. DLP encourages student-centeredness and constructivism (i.e.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Table 8. Descriptive statistics on deep approaches to learning Physics. Deep Motive
Qualitative Description (QD)
Range
Deep Strategy
f
%
f
%
4.21 â&#x20AC;&#x201C; 5.00
Very High
39
12.30
42
13.25
3.41 â&#x20AC;&#x201C; 4.20
High
127
40.06
164
51.74
2.61 â&#x20AC;&#x201C; 3.40
Moderate
127
40.06
91
28.71
1.81 â&#x20AC;&#x201C; 2.60
Low
23
7.26
20
6.31
1.00 â&#x20AC;&#x201C; 1.80
Very Low
1
0.32
0
0.00
Overall Mean
3.47
3.66
Standard Deviation (SD)
0.62
0.58
QD
High
High
Indicators
đ?&#x2018;ĽĚ&#x2026;
SD
QD
3.85
0.76
High
3.24
0.96
Moderate
3.31
0.96
Moderate
3.66
0.83
High
3.30
0.92
Moderate
3.63
0.87
High
3.74
0.83
High
3.97
0.85
High
I learn physics byâ&#x20AC;Ś 1 2 3 4 5 6 7 8
finding the activity sheets interesting. finding that I continually go over my physics class work in my mind even whenever I am not in class. working on science topics by myself so that I can form my own conclusions and feel satisfied. relating what I have learned in physics subjects to what I learn in other subjects. making theories to fit odd things together when I am learning physics topics. making relationship between the contents of what I have learned in other science subjects. relating the new activity sheets to what I already know about the topic when I am studying physics. understanding the meaning of the concept notes in the activity sheets.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY learning by doing, discovery approach, problem solving) in the teaching-learning process. These have been the learning methodologies of the students in physics classes for some schools since its adoption by DepEd-Cagayan de Oro City Division Office four years ago. It will be certainly a good update for all DLP-LPON teachers that the students find the activity sheets interesting because it is a necessary approach for students to finish their targets for the session. The students must have learned to imbibe the structure of DLP for them to exhibit interest and keenness on the activity sheets. Two indicators for deep motive is of moderate level which somehow accounts for the 40% of the respondents to hold moderate approaches for this particular subscale in general. The students have moderate level in continually going over their physics class work in their minds even whenever they are not in class (đ?&#x2018;ĽĚ&#x2026; =3.24). It is actually understandable that these students do not think anymore of physics when not in classes because of the in-school activity policy of the DLP-LPON scheme. The activity sheets and their portfolio are not allowed to be brought outside the classroom or the school. Aside from that, this ensures the safekeeping of the activity sheets from possible dilemmas, Bernido and Bernido (2012) portends that family time must be cultivated as well in studentsâ&#x20AC;&#x2122; lives to target holistic human development. Also these students have moderate level in working on science topics by themselves so that they can form their own conclusions and feel satisfied (đ?&#x2018;ĽĚ&#x2026; =3.31). This derives from the studentâ&#x20AC;&#x2122;s uncertainty in accomplishing the tasks completely and
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY feeling assured afterwards that what they did is right. It is a normal response given that there is no preceding involvement from the teacher. Half of the respondents have high approaches on deep strategy. The students learn by understanding the meaning of the concept notes in the activity sheets (đ?&#x2018;ĽĚ&#x2026; =3.97). This is apparently observable largely due to the DLP-LPON structure. As mentioned, the activity sheets contain a concept note and exercises. Since the students are independently tasked to go through the exercises by themselves, the students have the propensity to understand the concept notes first. Indeed, this is crucial to the Brunerian development of the independent learner â&#x20AC;&#x153;in which instruction aims to help the learner be a self-sufficient problem-solverâ&#x20AC;? (Bernido & Bernido, 2009). More so that the students are required to write the activity sheets by hand. Bernido and Bernido (2008) structured the DLP process where the students shall copy by hand all parts of the given activity sheets, starting from the title up to the exercises. According to them, the pedagogical basis is that combining visual and psychomotor modals of learning enhances memory activation and retention. This has insights from the neurosciences: neurons that fire together are wired together (D. Hebb as cited by Bernido & Bernido, 2012). Furthermore, the DLP structure prompts process-induced learning of the students. Figure 7 on the next page shows that studentsâ&#x20AC;&#x2122; involvements in the activity sheets takes 70-80% of the classroom session compared with the traditional teacherdominated practice where it is the teacher who consumes 70-80% of the time. With this, it can be pictured out that the students are probably most engaged in the activity
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY sheets. Pedagogically, this is an exemplification of learning by doing â&#x20AC;&#x201C; a constructivist learning approach. Students must not be permanently dependent on the teacherâ&#x20AC;&#x2122;s correction of errors, but must be able to take over the corrective function. This selfmonitoring behavior is a goal of cognitive learning (Bustos & Espiritu, 1996).
Figure 7. Model for process-induced learning in the DLP against the traditional teacher-dominated strategies (Bernido & Bernido, 2009).
Given all the aforementioned discussions, it is not surprising that the students will have high level in deep motive and deep strategy with means, 3.47 and 3.66, respectively. Lee, Johanson, and Tsai (2008) advances that in deep motive, the students express intrinsic motivation while learning. In relation to this study, physics learning among the students is triggered by their intense curiosity and interest. This is internal in nature. While in deep strategy, the students utilize a more meaningful way
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY to learning. Such as in physics learning, these students make connections and extracting key points on the physics concepts. These are higher-order thinking skills such that these students can demonstrate conceptual understanding and show evidences of learning through performances and outcomes.
4.1.2.2. On Surface Approaches to Learning Physics (ALP) Surface approach is featured by its external motivation (surface motive) to solely memorize or reproduce the learning materials (surface strategy). In the Approaches to Learning Physics Questionnaire, there are three items (item numbers 9, 10, 11) to describe surface motive and four items (item numbers 12, 13, 14, 15) for surface strategy. Table 9 shows the level of respondentsâ&#x20AC;&#x2122; surface approaches to learning physics for every subscale and its corresponding indicators. About 30% of the students have high level of surface motive. These students learn physics by making sure that they get a good grade in physics so that they can get a better job in the future (đ?&#x2018;ĽĚ&#x2026; =3.96). Also they learn physics so they can please their family and the teacher (đ?&#x2018;ĽĚ&#x2026; =3.76). These two statements are clearly motivations brought upon by external sources. It is an undeniable fact that in the public schools, many students view schooling as a way to alleviate their present socio-economic situation. Parents influence their children by encouraging them to study well. It is the expectations of these parents that their children can join the workforce immediately after high school or pursue vocational or formal higher education even with their meager means. This actuality propels the students to study hard and warrant a
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Table 9. Descriptive statistics on surface approaches to learning Physics. Surface Motive
Qualitative Description (QD)
Range
Surface Strategy
f
%
f
%
4.21 â&#x20AC;&#x201C; 5.00
Very High
94
29.65
28
8.83
3.41 â&#x20AC;&#x201C; 4.20
High
121
38.17
103
32.49
2.61 â&#x20AC;&#x201C; 3.40
Moderate
88
27.46
134
42.27
1.81 â&#x20AC;&#x201C; 2.60
Low
13
4.10
47
14.83
1.00 â&#x20AC;&#x201C; 1.80
Very Low
1
0.32
5
1.58
Overall Mean
3.81
3.25
Standard Deviation (SD)
0.69
0.63
QD
High
High
Indicators
đ?&#x2018;ĽĚ&#x2026;
SD
QD
making sure that my performance in our physics class will satisfy my teacherâ&#x20AC;&#x2122;s expectations. making sure that I get a good grade in physics so that I can get a better job in the future. making sure that I do well in physics so I can please my family and the teacher. studying topics that are likely to be on the exams only. restricting my study to what is specially set as I think it is unnecessary to do anything extra in learning physics. finding out that studying each topic in depth is not helpful or necessary when I am learning physics. finding the best way to pass science examinations like trying to remember the answers to possible questions.
3.69
0.87
High
3.96
0.83
High
3.76
0.89
High
3.01
1.05
Moderate
3.17
0.85
Moderate
3.02
1.03
Moderate
3.81
0.93
High
I learn physics byâ&#x20AC;Ś 9 10 11 12 13 14 15
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY brighter future in the process. It may be a noble contention but in the learning process per se, external motives may not be the best driving force. Obrentz (2012) shared that one strong predictor of science success is the kind and level of motivation. Specifically, intrinsic motivation had a weighty impact on the studentsâ&#x20AC;&#x2122; final course grades. On the other hand, 42% of these students have moderate level only in the surface strategy. This can be attributed to the three out of four indicators which has moderate level. These students learn physics by restricting their studies to what is specially set as they think it is unnecessary to do anything extra in learning physics (đ?&#x2018;ĽĚ&#x2026; =3.17), finding out that studying each topic in depth is not helpful or necessary when I am learning physics (đ?&#x2018;ĽĚ&#x2026; =3.02) and studying topics that are likely to be on the exams only (đ?&#x2018;ĽĚ&#x2026; =3.01). The last two has high uniformity of agreement among the students (SD=1.03 and 1.05). These students have the tendency to cull out topics from the entire coverage when they study and which they think will come out in the examination. The reason for this can be credited to the quantity of topics in the Physics Essential Portfolio for every quarter. In the first grading period, around 40 activity sheets can be accomplished. This will correspond to more or less 40 stand-alone or connecting topics as well. It may be too much for the students to delve into these topics in studying especially during the final examinations. Moreover, the students may find it too difficult as well to study in depth for all the topics. One observable setback in the DLP-LPON structure is that each activity
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY sheet is good for one session only. There is pressure for the physics teachers as they are set to finish a certain number of activity sheets in the number of non-negotiable contact days per grading quarter. It could be that the students cannot catch up on the pacing of the topics day by day, thus limiting their chances to study the physics topics in detail. Figure 8 below shows an example of four topics that the students will finish in four sessions. These four topics are actually interrelated but for an average student, these series may be difficult to learn. As a response, they learn physics by finding the best way to pass science examinations like trying to remember the answers to possible questions (đ?&#x2018;ĽĚ&#x2026; =3.81).
Figure 8. Sample flow of topics for four sessions in Physics Essentials Portfolio (Bernido & Bernido, 2008; 46-49). Another possible reason on why students may not be able to study in depth is because of the no homework policy of the DLP-LPON structure. As mentioned earlier,
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Bernido and Bernido (2012) encourages the students to enjoy wholesome leisure and have more relaxing family time. They also advised the students to have the appropriate number of sleep hours so they can be fresh and energized for the next dayâ&#x20AC;&#x2122;s schoolwork. The US National Sleep Foundation as cited by Bernido and Bernido (2012) that high school teens (10-17 years old) should have a range of 8.5-9.25 hours of sleep. So all academic activities must be done inside the school during school hours. All in all for the studentsâ&#x20AC;&#x2122; surface approaches, they have high level in surface motive and in surface strategy with means, 3.80 and 3.25, respectively. Lee, Johanson, and Tsai (2008) noted that in surface motive, students possess extrinsic motivation in learning. In relation, the students in physics learning are motivated by merely passing an examination and pursuing a high grade. Or as in the results herein, they are motivated to impress their families or their teachers. On the other hand, it is deemed that in surface strategy, the students use more rote-like strategies in learning. Such as in physics learning, the students do unreflective memorization of the concepts. Like in the results of this study, the students would just remember the answers to possible questions and doing nothing extra to learn physics. To encapsulate on the approaches to learning physics, Figure 9 shows the representation of four subscales with their means. It is shown that surface motive has the highest mean (đ?&#x2018;ĽĚ&#x2026; =3.81). It is implicit that most of the studentsâ&#x20AC;&#x2122; motivation to learn physics is reinforced by external bases. Anyhow, the studentsâ&#x20AC;&#x2122; approaches to learn
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY physics is mediated by their high deep motives (đ?&#x2018;ĽĚ&#x2026; =3.47) and high deep strategy (đ?&#x2018;ĽĚ&#x2026; =3.66). Chiou and Liang (2012) specifies that there is no dichotomy between the deep and surface approach. A learner may possess both deep and surface motive in learning physics at the same time. The weighing between these two motives is taskdependent. In the same manner, a learner may have deep motive but adopts the surface strategy to do a learning task.
3.66
3.47
3.25
3.81
2.9
3
3.1
3.2
Deep Strategy
3.3 Deep Motive
3.4
3.5 Surface Strategy
3.6
3.7
3.8
3.9
Surface Motive
Figure 9. Studentsâ&#x20AC;&#x2122; levels on the subscales on approaches to learning physics.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Furthermore, it can be collected in Table 10 that more than half of the students held high perceptions in surface and deep approaches to learning physics (52% and 54% respectively). Basing on their means, both subscales are qualitatively described as high. As a single construct, these students have high level in their approaches to learning physics (đ?&#x2018;ĽĚ&#x2026; =3.55). These results do not directly concord to other studies on approaches to learning. With the use of the same survey tool in this study, the separate studies of Chiou and Liang (2012) and Lee, Johanson, and Tsai (2008) presented that Taiwanese high school students have moderate level in this construct.
Table 10. Final description of the studentsâ&#x20AC;&#x2122; level on approaches to learning Physics.
Range
Qualitative Description (QD)
Surface Approach
Deep Approach
f
%
f
%
f
%
OVERALL
4.21 â&#x20AC;&#x201C; 5.00
Very High
28
8.83
34
10.73
24
7.57
3.41 â&#x20AC;&#x201C; 4.20
High
164
51.74
165
52.05
170
53.63
2.61 â&#x20AC;&#x201C; 3.40
Moderate
114
35.96
105
33.12
114
35.96
1.81 â&#x20AC;&#x201C; 2.60
Low
10
3.15
13
4.10
9
2.84
1.00 â&#x20AC;&#x201C; 1.80
Very Low
1
0.32
0
0.00
0
0.00
Mean
3.53
3.56
3.55
Standard Deviation (SD)
0.52
0.53
0.46
QD
High
High
High
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Chan (2012) used a different questionnaire from Biggs et al. (2001) yet the results of her study also indicated that Hong Kong students held moderate perceptions on their approaches to learning. Wilson, Georgakis and Sharma (2012) also reported neutral mean scores of Australian first year college students for this similar study. Although is it not conclusive in this study, Filipinos have higher perceptions on their ongoing approaches to learn physics. Perhaps the DLP-LPON structure which is unique as an instructional methodology made the students in this study to have high levels in their approaches to learning physics compared to other Asian students. This is in assumption that these Asian counterparts did not employ strategies similar to the pedagogical designs of Bernido and Bernido (2008). Aside from this, these students held high conceptions of learning physics which is feasibly a contributory element for them to exhibit high levels in their approaches to learning physics.
4.1.3. Studentsâ&#x20AC;&#x2122; Level of Physics Self-Efficacy (PSE) Physics self-efficacy is a measure of oneâ&#x20AC;&#x2122;s confidence of capability in learning Physics. The tool on Physics Self-Efficacy Survey (see Appendix A) which contained eight items was conducted to the respondents to obtain PSE data. These data were interpreted using the scoring guide in Table 2 (see at page 57) and then processed through descriptive statistics. Table 11 shows the studentsâ&#x20AC;&#x2122; level of physics self-efficacy and its corresponding indicators. These students are highly confident that they can understand the simple concept notes in the activity sheets (đ?&#x2018;ĽĚ&#x2026; =3.97). This supports
104
MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Table 11. Descriptive statistics on Physics self-efficacy.
Range
Physics Self-Efficacy
Qualitative Description (QD)
f
%
4.21 â&#x20AC;&#x201C; 5.00
Very High
20
6.31
3.41 â&#x20AC;&#x201C; 4.20
High
149
47.00
2.61 â&#x20AC;&#x201C; 3.40
Moderate
135
42.59
1.81 â&#x20AC;&#x201C; 2.60
Low
13
4.10
1.00 â&#x20AC;&#x201C; 1.80
Very Low
0
0.00
Overall Mean
3.42
Standard Deviation (SD)
0.49
QD
High
Indicators
đ?&#x2018;ĽĚ&#x2026;
SD
QD
3.35 3.32
0.74 0.78
Moderate Moderate
3.97
0.82
High
3.19
0.79
Moderate
3.40
0.86
Moderate
3.54
0.79
High
3.17
0.74
Moderate
I am confident thatâ&#x20AC;Ś 1 2 3 4 5 6 7
I will receive an excellent score every after physics class. I can understand difficult learning activities in physics. I can understand the simple concept notes in the activity sheets. I can understand the difficult concept notes in the activity sheets. I can do an excellent work in meeting the learning targets in the activity sheets. I can perform well in learning about physics. I can master the physics skills expected during the activities.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY the previous data in deep strategy that they learn physics by understanding the meaning of the concept notes in the activity sheets. It is part of the DLP-LPON methodology that the students have to go through the concept notes first and understand the contents therein before they can complete the tasks required of them in the exercises. The activity sheets are designed in simplest format and presumes to contain simple physics concepts. It is being ensured that each activity sheet is realistic in time as physics classes should be taught in 60 minutes as designed in the DLP-LPON structure. The concept notes are short and concise such that the physics concepts are conveyed in portions. As Bernido and Bernido (2008) would portend, it has pedagogical basis that when the students learn in chunks, there is better concept absorption and retention. This may account on why the students are highly confident that they can perform well in learning about physics (đ?&#x2018;ĽĚ&#x2026; =3.54). On the contrary, these students are moderately confident that they can understand difficult concept notes in the activity sheet (đ?&#x2018;ĽĚ&#x2026; =3.19) and difficult learning activities in physics (đ?&#x2018;ĽĚ&#x2026; =3.32). In spite the fact that the concept notes in design are simple and in chunks, there are topics that are still difficult for an average learner. More so that there is no prior teacher intervention when the students accomplishes the tasks. The DLP-LPON proponents always stressed that when the students are exposed and work on unfamiliar concepts or materials, there is development of analytical and problem-solving skills. The students will always have a tenacity to create new pathways and inspect for solutions (Bernido & Bernido, 2012).
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY One admirable feature of the DLP-LPON is that the learning targets are clearly stated in each activity sheets. The students would know what is expected of them in every activity. It is foundational in the teaching-learning process that the learning objectives must be conveyed to the learners and that it should be collinear to the assessment procedure (Corpus & Salandanan, 2007). In relation to this, the students are moderately confident that they can do an excellent work in meeting the learning targets in the activity sheets (đ?&#x2018;ĽĚ&#x2026; =3.40). This result is in parallel to their moderate level in deep motive that they work on the topics by themselves so they can form their own conclusions and feel satisfied. It is still reassuring to recognize that these students do not have low confidence on this aspect. The fact still conjectures that these students have the confidence to meet the learning agenda for the day and it implies that there is always a room for progression. Along this vein, the students are moderately confident that they will receive an excellent score every after physics class (đ?&#x2018;ĽĚ&#x2026; =3.35). It appears incongruous when these students have high confidence that they can perform well in learning about physics. The demarcation may lie on the fact that the former is about assessment and the latter is on the learning routine in general. As stated in earlier discussion that there might be looming uncertainty among the students if they did correct on the exercises of the activity sheets. It is a typical response of a learner when there is no account of introductory discussion or other activities coming from the teacher. In summary for the studentsâ&#x20AC;&#x2122; physics self-efficacy, 47% have high physics selfefficacy but a considerably large percentage, about 43%, have moderate physics self-
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY efficacy. In general, these students have a mean of 3.42 which is qualitatively described as high. This result submits that these students have high beliefs that they are capable of learning physics. The DLP-LPON protocols must have helped the students in the foundation of these approaches. These students have experienced the DLP methods since its introduction in DepEd Cagayan de Oro City division four years ago. Although the LPON part is focused for the fourth year students only, but the DLP proprieties such as the in-school comprehensive portfolio, daily practices for accomplishing the activity sheets, the strategic rest periods and the parallel class scheme are the same all throughout the year levels. This present batch of fourth year students who served as respondents of this study is the same group of students who were exposed with the DLP ways since their first year in high school. As accorded by Bandura (2006) and his social cognitive theory, these students are self-organizing. All daily activities, including quizzes and exams, are compiled in the in-school comprehensive student portfolios. Students manifest reflective and selfevaluative behavior when filing and organizing activities in their portfolios. The compilation per se as an activity can boost up studentsâ&#x20AC;&#x2122; skills on organization and cumulative scholarship. Likewise, these students with high self-efficacy are proactive, self-regulating and self-reflecting (Bandura, 2006). Students work on their activity sheets for most of the class period without earlier lecture or demonstration from the teacher. When the teacher commences the discussion, students already have particular questions or
108
MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY problems in mind. The DLP approach provides students the opportunity to give directed questions that have direct bearing on the problems they tried to solve earlier. The teacher simply reinforces correct understanding, points out common errors, or compares the merits of different approaches and solutions. The flash of insight or understanding is more often observed than in traditional situations where the teacher introduces the topic, lectures, explains, and gives examples, before the students work on the lessons. This agrees to the studies of Schunk and Meece (2005) that high school students reported more positive self-efficacy when their teachers used learnercentered instructional practices that promoted higher-order thinking, and adapted instruction to individual and developmental needs. Classroom environments that emphasize the importance of effort, meaningful learning, self-improvement, collaboration, and student interests help adolescents maintain positive perceptions of their efficacy and competence (Urdan & Midgley, 2003). With all these, it could be that the DLP-LPON structure, or the DLP in its entirety after the students’ duration of time of exposure, have facilitated them to be highly confident that they can learn physics. There is an opportunity though that the students’ physics self-efficacy can be improved more. As Fencl and Scheel (2004) portends, a class that includes a mix of teaching strategies did develop the students’ self-efficacy better. When these strategies were examined, the strongest relationship with self-efficacy were established on cooperative learning. This gives a profound implication on the use of DLP as a method because of its monotonous classroom dynamics.
109
MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY 4.1.4. Relationships among Conceptions of Learning Physics (CLP), Approaches to Learning Physics (ALP), and Physics Self-Efficacy (PSE) It is contended in this study that implicitly both the studentsâ&#x20AC;&#x2122; conceptions of learning physics and their approaches to learning physics are the pre-task and duringtask factors in a Physics learning continuum. Consequently these are strong variables in the formation of physics self-efficacy. Correlation analysis (i.e. PPMC) was used to establish the relationships of the aforementioned variables and to test the null hypotheses stated in Chapter 1 (see at page 13) with the alpha level set at 0.05. Table 12 presents the correlation matrix among the subscales on CLP, ALP and PSE. Overall, it can be seen that most relationship is of moderate strength but these are significant even at 99% confidence level. The relationship that has the highest correlation in the said table is on understanding and deep strategy (r=0.613, p=0.00). This result confirms that understanding as a high-level conception of learning physics can be demonstrated through deep approaches to learning the subject. As an example, these students think that learning physics means understanding the connection between scientific concepts (QD=high CLP) and these same students learn physics by making relationship between the contents of what they have learned in other science subjects (QD=high ALP). It makes sense that how these students conceive learning physics are being translated into how they will approach the subject in actual learning.
110
MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Table 12. Correlations among the subscales on conceptions of learning Physics, approaches to learning Physics, and Physics self-efficacy. Conceptions of Learning Physics Subscales
Approaches to Learning Physics Subscales
Physics SelfEfficacy
Surface Motive
Surface Strategy
Deep Motive
Deep Strategy
Memorizing
.415**
.075NS
.289**
.457**
.413**
Testing
.143**
.117*
.057NS
.176**
.199**
Calculating and Practicing
.378**
.032NS
.282*
.514**
.349**
Increasing One’s Knowledge
.390**
.115*
.244**
.471**
.269**
Applying
.507**
.159**
.340**
.563**
.357**
Understanding
.485**
.093*
.361**
.613**
.382**
Physics Self-Efficacy
.419**
.096NS
.415**
.508**
---
*p<.05 **p<.01 NS – not significant
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Another significant relationship that is noteworthy is on applying and deep strategy (r=0.563, p=0.000). These students think that learning physics means knowing how to apply methods they already know to unknown problems (QD=high CLP) so in their approach, these students learn physics by relating the new activity sheets to what they already know about the topic when they are studying physics (QD=high ALP). There is apparent congruency from the conception to approach in learning physics. The weakly correlated subscales are not significant, and most of these are on subscales in low-level CLP and in the surface strategy. It may be assumed that the students view memorizing and calculating and practicing not just a surface strategy. Rather it is like a “gold standard approach” on how physics is being learned. Recalling in the previous discussion, these students have high level of conceptions on memorizing and calculating and practicing. It is always possible that a student may hold multiple conceptions of and approaches to learning physics at a time (Chiou & Liang, 2012). It varies with the nature of the learning tasks and materials. And these conceptions and approaches to learning gradually develop from an individual’s actual and lasting learning experience. Another subscale that is not significant is on testing and deep motive. This gives justice as to why testing is the least among the six subscales of conceptions of learning physics. The students’ view of testing is not about the demonstration of their understanding of the concepts and the application therein, but simply about responding to test items answerable by memorized concepts and procedural
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY calculation. It can be remembered as well that these students are more motivated by external sources. Lastly, physics self-efficacy and surface strategy is not significant. It can be interpreted that the formation of the studentsâ&#x20AC;&#x2122; confidence in physics learning is not just approached on a surface level. Instead, physics self-efficacy is embodied in deeplevel strategies (r=0.508, p=0.000) and driven by deep-level motives (r=0.415, p=0.00) and surface-level motives (r=0.419, p=0.00). As Bandura (1977) postulated it, selfefficacy refers to beliefs in oneâ&#x20AC;&#x2122;s competences and is therefore internal in nature. Moreover, self-efficacy as a construct is not a general and stable personal trait; it is rather a consequence of continuous and dynamic confluences of learning motives, strategies and their interpretation of learning experiences (Chiou & Liang, 2012). In general, the correlation between conceptions of learning physics and approaches to learning physics is highly significant (r=0.56, p=0.00). The null hypothesis that states that there is no significant relationship between conceptions of learning and approaches to learning in a Physics learning continuum is rejected. A basis to the contention that the two constructs are like pre-task and during-task variables in a physics learning continuum is supported. There is constancy among the levels of the two learning constructs such that the high conceptions of learning physics of the students led them to have high approaches to learning physics. These results go well with the studies of Chan (2012), Chiou and Liang (2012) and Lee, Johanson and Tsai (2008) with regards to the significant relationship between conceptions of learning and approaches to learning.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY 4.1.5. Studentsâ&#x20AC;&#x2122; Proficiency Level in Physics Learning The proficiency level of the studentsâ&#x20AC;&#x2122; in physics learning were based on their raw scores in a 50-item multiple-choice questionnaire, Physics Content Knowledge Test. Qualitative interpretation of the scores were based on DepEd Memo 158, s.2011 and were processed through descriptive statistics. Table 13 shows the frequency count and percentage of the students who got the items correct for the assessment of their physics learning. There are four general areas covered in the test, namely: (1) nature of science learning; (2) scientific procedures and techniques; (3) mechanics; and (4) kinematics. These are the areas taken by the students in their physics classes using the DLPLPON on the first and second quarters. For the first area on the Nature of Scientific Inquiry, almost majority of the students (about 73%) was correct for the item on identifying which science is an applied science. The students had known that medicine is an example of applied science where several scientific concepts across many science disciplines were coherently weaved into something beneficial. This is one of the earliest topics in the physics essentials portfolio which only requires students to be familiar with. In spite that it is very easy, the proficiency level is beginning. A quarter of the students still have not remembered this particular concept. On the second area, Scientific Procedures and Techniques, 80.44% of the students had it correct on identifying what device to use to measure length. Among
114
MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Table 13. Per item description of the students’ Physics learning results. No.
Test Items
Results f %
Proficiency Level
1 2 3
Nature of Scientific Inquiry The following is an applied science… The following ways of action is… The statements below is a scientific hypothesis…
230 156 156
72.56 49.21 49.21
Beginning
4 5 6
Scientific Procedures and Techniques Express in scientific notation… How many kilograms are there in… The following apparatus can be used to…
239 133 255
75.39 41.96 80.44
Developing
212 181 135 237 210 134
66.88 57.10 42.59 75.00 66.25 42.27
93
29.34
Beginning
72
22.71
Beginning
109
34.38
Beginning
157
49.53
Beginning
109 88 235 84 96 91 168
34.38 27.76 74.13 26.50 30.28 28.71 53.00
Beginning Beginning Beginning Beginning Beginning Beginning Beginning
205 202 159
64.67 63.72 50.16
Beginning Beginning Beginning
7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
24 25 26
The following is the MKS unit of time… The following is the MKS unit of the slope… The new volume reading on the graduated cylinder… A right angle has a measure of… The longest side of a right triangle is called… The following materials can be used to hang the bob of a simple pendulum… The period of pendulum depends on the mass of the bob, the dependent variable is… The mass of the bob of a simple pendulum is decreased into half, its … The time required for a complete to-and-fro swinging of a simple pendulum... The length of a simple pendulum is shortened, it swings… Peter’s measurements are… Mark’s measurements are… The important variables in this experiment… The controlled variable… The dependent variable… The independent variable… The words and phrases are synonymous with “constant” except… Mechanics Basic difference between a vector and a scalar is… The following is a vector… The direction of a vector is easily determined by…
Beginning Beginning
Beginning Approaching Proficiency
Beginning Beginning Beginning Developing
Beginning Beginning
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Table 13 continued… 27 It decreases the accuracy in tail- to- tip method of adding vectors… 28 The northeast direction has a measure from the eastward direction….
29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Kinematics The total distance walked by the man… The magnitude of his total displacement… The total distance traveled by the jeep… The total displacement of the jeep… The displacement after 3 hours… The change in velocity over the corresponding elapsed time… The distance-velocity graph would be… It describes the relationship between velocity and time… The change in velocity over the corresponding elapsed time gives … The acceleration of the man… The speed after 4 seconds… The velocity-time graph would be… The slope of the graph is… The acceleration of the car is… The velocity of the car at time 2.5 seconds is… What point does the graph cross the V-axis… Graph show that the dependent variable does not change when the independent variable is changed… Graph has a constant slope… Graph shows the alternately increasing and decreasing behavior… Graph shows the circumference of a circle… The resultant force… The opposite force… TOTAL
118
37.22
Beginning
142
44.79
Beginning
225 65 137 107 79 86
70.98 20.50 43.22 33.75 24.92 27.13
Beginning Beginning Beginning Beginning Beginning Beginning
184 110
58.04 34.70
Beginning Beginning
153
48.26
Beginning
109 180 177 69 100 112 56 129
34.38 56.78 55.84 21.77 31.55 35.33 17.67 40.69
Beginning Beginning Beginning Beginning Beginning Beginning Beginning Beginning
87 182
27.44 57.41
Beginning Beginning
75 162 146
23.66 51.10 46.06
Beginning Beginning Beginning
23
45.09
Beginning
Summary of students according to level of proficiency: Mean Percentage Score 90 - above 85 – 89 80 – 84 75 – 79 74 - below
Level of Proficiency Advanced Proficient Approaching Proficiency Developing Beginning TOTAL
f 0 0 2 4 311 317
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY the choices, they identified the ruler. They have a proficiency level of approaching proficiency for this competency. It is only in this item that the students reached this proficiency level. According to DepEd in their standards-based assessment, approaching proficiency means that the students at this level has developed the fundamental knowledge and skills and core understandings and, with little guidance from the teacher and/or with some assistance from peers, these students can transfer these understandings through authentic performance tasks. There are two items that the students got a proficiency level of developing. Seventy-five percent of the students got it right on an item about expressing a number of decimal form into scientific notation. Another item where 75% of the students were correct is on the item that asks them to identify the degree of elevation of a right angle. These topics were already taken up on their first year mathematics and has been a repetitive concept in higher math. So most of these students can really answer on this one. However, even if these are not new concepts to them, they are still far from reaching the mastery level. The students have the lowest score on understanding the relationship of independent and dependent variables in an experiment. Only about 23% were certain about the effects of varying the mass and length as the independent variables to the period of the pendulum. This item is based from the physics essentials portfolioâ&#x20AC;&#x2122;s one of the learning laboratory stations. This activity allows students to investigate on a simple pendulum with different variables. Basing from the experience of this researcher, the students almost always fail to arrive at the ideal conclusions. Perhaps
117
MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY the conditions of setting-up a simple pendulum and in manipulating the variables where not met by the students in their actual hands-on. Another item where these students got among the lowest proficiency level is on the topic about accuracy and precision. These students find these two terms either synonymous or ambiguous, thus their difficulty in relating the terms to practical situations. The item in the test asks the students to identify if the measurements in the example are more or less accurate or precise. Only about 28% of the students understood this concept. On the third topic, Mechanics, only about 65% of the students were able to distinguish the basic difference between a vector and a scalar while nearly 64% were able to identify on which among the choices is an example of a vector. Conventionally, this should be one of the easiest concepts in physics. It only takes one to identify if a certain quantity has a stated direction for it to be a vector. Nonetheless, the students only reached a beginning proficiency level for this competency. This topic is important in the study of succeeding topics like in kinematics. It is preemptive that there will be a problem on later topics because the students have not mastered a foundational topic such as scalars and vectors. In this line, the students have a proficiency level of beginning in all items in the last area covered in the test which is Kinematics. It is only on the item about solving for total distance where about 71% of the students got it right. It takes simple math skills (i.e. addition and subtraction) to answer the item. In contrast, only about 21% correctly answered on getting the magnitude of the displacement. For this particular
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY item, the students need to use the Pythagorean Theorem to solve for the resultant vector. Perhaps the students had difficulty in performing this application. Distance and displacement are two related topics in physics. Aside from the difference in math approaches, the former is a scalar quantity and the latter is a vector quantity. This is a clear manifestation that foundational topics are essential to fully understand the topic at hand. Many items in this area are about interpreting graphs with variables like distance, velocity, and time. In general, the students exhibit beginning level of proficiency in this skill. Graphs basically show the relationship between the independent and dependent variables. As it was discussed previously, these students have difficulty in this competency. From this researcherâ&#x20AC;&#x2122;s experience, these students can identify trends in graphs like if it is increasing or decreasing. They just cannot infer if the relationship of the variables is linear or inverse, or otherwise. There are only two out of 317 students who are approaching proficiency level and four who are in the developing level. The rest should have a score of 37-38 to reach the 75% proficiency level. Basing the histogram found in Figure 10 on the next page, only a few scored on this range such that the data are positively skewed. Most scores are lumped around 13 to 28. The highest heap (or bar) represents the most frequent (mode) scores of 17 and 18 which is collectively 14% of the students. The second highest heap is on the scores 21 and 22 where it denotes 12% of the students. These scores are close to the mean raw scores of the students which is 22.50.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY
Figure 10. Histogram of the raw scores of the students with the normal curve in the 50-item Physics Knowledge Contest Test.
In Table 14, it can be summarized that the studentsâ&#x20AC;&#x2122; mean raw score is 22.50 out of 50 items, and this consequently yields to a mean percentage score (MPS) of 45.10. These outcomes are far from the target MPS in DepEd which is 75. Qualitatively in a range of beginning to advanced, this MPS has a proficiency level of beginning. According to DepEd in their standards-based assessment and rating system, the students at this level struggles with their understanding such that prerequisite and fundamental knowledge and/or skills have not been acquired or developed adequately to aid understanding. Among the areas, the highest MPS is 62.71 which is on
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Table 14. Per topic and the overall description of the students’ Physics learning results. SD
Mean Percentage Score (MPS)
Proficiency Level
1.71
0.84
56.99
Beginning
19
9.05
3.06
47.65
Beginning
Mechanics
5
3.14
1.75
62.71
Beginning
Kinematics
23
8.55
3.07
37.44
Beginning
OVERALL
50
22.55
6.73
45.09
Beginning
Topics
No. of Items
Mean Score
Nature of Scientific Inquiry
3
Scientific Procedures and Techniques
Legend (DepEd Memo 158, s.2011): Mean Percentage Score 90 - above 85 – 89 80 – 84 75 – 79 74 - below
Level of Proficiency Advanced Proficient Approaching Proficiency Developing Beginning
Mechanics, and the lowest MPS is 37.44 which is about Kinematics. True enough, the students still lack mastery in these topics. The results herein are the same with the physics results from the National Achievement Test conducted last February 2013 in the Division of Cagayan de Oro City involving all public and private schools. It revealed that the mean percentage score (MPS) is below 75%. The highest MPS reached by a school is only 73.91% while among all the schools, only three schools got an MPS above 70%.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY The reported performance indicators of DLP would not agree on the results of this study on the students’ physics learning results. Bernido and Bernido (2009) recounts that one gauge, among others, in assessing the effectiveness of the DLP is the government administered National Career Assessment Examination (NCAE) which all students nationwide have to take in the early part of their fourth year in high school. In the 2009 NCAE, 27 of their 115 senior students in Central Visayas Institute Foundation (CVIF), Jagna, Bohol obtained an overall general scholastic aptitude score in the range of 90-99 percentile rank. This means that 23% of the CVIF students belong to the top 10% in the country.
4.1.6. Students’ Career Interest in Science and Technology, Engineering and Mathematics (STEM) The respondents in this study were among the thousands of next-in-line entrants to college and their decision on what course to take are crucial while they are still studying in high school. To gauge the students’ perception on STEM disciplines and if they are interested to take related careers with it, the STEM Semantics Survey which has a total of 25 items was used. The survey data gathered were processed though descriptive analysis and were interpreted based on Table 3 (see at page 58) as the score guide. Table 15 shows the semantic perception of the students on STEM disciplines and careers. There are two sets of polar adjectives – positive and negative positioned in each side. In the original instrument used during the actual survey, these adjectives
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Table 15. Semantic perception data on STEM disciplines and careers. Completely Agree
Mostly Agree
Slightly Agree
Neither Agree / Disagree
Slightly Agree
Mostly Agree
Completely Agree
%
%
%
%
%
%
%
Interesting
52.37
22.08
11.99
9.78
1.58
1.26
0.95
Ordinary
Attractive
20.50
21.14
26.81
18.30
8.20
4.10
0.95
Unattractive
Exciting
36.91
21.45
16.09
16.40
6.31
2.52
0.32
Means a lot
40.69
15.46
16.72
15.46
4.42
2.84
4.42
Motivating
26.81
19.56
20.50
18.93
5.68
2.52
5.99
Unexciting Means Nothing Boring
Interesting
50.10
22.40
9.78
10.41
3.79
0.63
1.89
Ordinary
Attractive
28.08
21.77
22.08
20.19
5.05
2.21
0.63
Unattractive
Exciting
37.54
16.40
17.03
19.87
5.68
1.89
1.58
Means a lot
38.80
12.93
15.46
17.67
6.62
3.47
5.05
Motivating
38.60
15.77
16.40
20.50
5.99
6.94
3.79
Unexciting Means Nothing Boring
To me, SCIENCE is
To me, MATH is
To me, ENGINEERING is Interesting
42.27
17.67
17.35
13.25
3.47
3.79
2.21
Ordinary
Attractive
25.55
22.08
20.82
18.30
8.83
2.21
2.21
Unattractive
Exciting
28.08
17.35
22.40
21.14
6.94
2.52
1.58
Means a lot
23.34
18.61
19.56
20.50
7.26
4.73
5.99
Motivating
21.45
14.51
22.08
23.66
7.89
4.10
6.31
Unexciting Means Nothing Boring
To me, TECHNOLOGY is Interesting
58.68
18.30
7.89
12.30
1.26
0.63
0.95
Ordinary
Attractive
43.85
21.77
15.14
14.20
3.15
1.58
0.32
Unattractive
Exciting
44.48
19.24
14.20
15.46
3.47
2.84
0.32
Means a lot
31.23
19.24
14.83
20.82
7.89
3.15
2.84
Motivating
32.81
14.51
21.77
21.45
3.47
2.84
3.15
Unexciting Means Nothing Boring
To me, a CAREER in science, technology, engineering and math (is) Interesting
51.10
23.03
10.41
11.67
1.89
0.95
0.95
Ordinary
Attractive
33.75
23.34
17.03
17.98
4.73
2.21
0.95
Unattractive
Exciting
36.91
17.67
18.93
17.03
4.42
2.84
2.21
Means a lot
32.81
18.03
16.40
19.24
5.05
5.05
3.15
Motivating
31.86
21.45
16.40
18.61
5.99
2.21
3.47
Unexciting Means Nothing Boring
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY were not homogeneously grouped in one pole only to avoid the halo effect (Raagas, 2010). From the said table, half of the students completely agree that Science is interesting. Whereas only about 27% to 42% completely agree that science is exciting, means a lot, and motivating. Twenty-six percent agree that science is attractive. With their conceptions of learning physics, approaches to learning physics, and physics self-efficacy that are all generally high, these students will have a tenacity to like the science domain. For the Math discipline, many students completely agree that it is altogether interesting, attractive, exciting, means a lot and motivating. Although of differing percentages, these students find math pleasant and agreeable. This is unanticipated result considering the actual low math achievement results at the present. Studies would even sustain that many students have math anxiety (Ashcraft & Krause, 2007). It could be that since these students have high conceptions on learning physics such that they hold high beliefs in doing well on math-related tests and in learning calculations and problem solving, they developed an open and optimistic attitude towards the subject. For the Engineering discipline, about 23%- 42% completely agree that it is interesting, attractive, exciting, and means a lot. While many students, about 24% of them, neither agree nor disagree that engineering is motivating nor boring. Perhaps these students do not have a clear idea on this field as to its nature and dynamics. Nonetheless it is not far that nearly 21% expressed that engineering is motivating.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY This just shows the variability of the studentsâ&#x20AC;&#x2122; responses if the idea is indistinct to them. Like with Math, the students also completely agree in all the positive descriptions for Technology. The studentsâ&#x20AC;&#x2122; orientation on technology is most associated to the subject, Technology and Livelihood Education. In this subject, the students can really see that concepts are actually practiced. These students appreciate these areas a lot because it is practical to the real world; just as these students appreciate physics as a practical science. And along this impression, it is on Technology that the students have highest mode of agreement among the STEM disciplines. As a Career in STEM, many students completely agree that it is interesting, attractive, exciting, means a lot and motivating. The studentsâ&#x20AC;&#x2122; grasp of understanding and relating to it as oneâ&#x20AC;&#x2122;s career in the future is rooted on how they view and experience the STEM disciplines. Since these students have affirmative dispositions to the disciplines, they have grounding to have the same kind of disposition towards seeing it as a career. Additionally, Table 16 accounts that in a range from extremely negative to extremely positive, the studentsâ&#x20AC;&#x2122; perceptions on most STEM disciplines and career are highly positive. The highest mean is on Technology (đ?&#x2018;ĽĚ&#x2026; =5.66) which is highly positive while the lowest is on Engineering (đ?&#x2018;ĽĚ&#x2026; =5.16) which is positive. It expressed that the students mostly agree that the STEM disciplines and career is an interesting option to consider in choosing a course in college or for a lifetime profession.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Table 16. Descriptive statistics on students’ perception on STEM disciplines and career. Qualitative Interpretation and Range Values
Science
Tech
Eng’g
STEM Career
Math
f
%
f
%
f
%
f
%
f
%
Extremely Positive 6.11 – 7.00
92
29.02
130
41.01
70
22.08
101
31.86
101
31.86
Highly Positive 5.26 – 6.10
94
29.65
65
20.50
73
23.03
75
23.66
68
21.45
Positive 4.41 – 5.25
70
22.08
68
21.45
88
27.76
70
22.08
52
16.40
Fair 3.56 – 4.40
54
17.03
49
15.46
63
19.87
60
18.93
59
18.61
Negative 2.71 – 3.55
7
2.21
5
1.58
15
4.73
8
2.52
19
5.99
Highly Negative 1.86 – 2.70
0
0.00
0
0
7
2.21
3
0.95
7
2.21
Extremely Negative 1.00 – 1.85
0
0.00
0
0
1
0.32
0
0.00
11
3.47
Overall Mean
5.47
5.66
5.16
5.44
5.53
SD
1.01
1.08
1.13
1.12
1.14
QI
Highly Positive
Highly Positive
Positive
Highly Positive
Highly Positive
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Basing on the percentages, more students are extremely positive in their perceptions towards Technology (41%), Math (32%) and in the STEM Career (32%). Whereas, a collective 60% of the students perceive Science highly to extremely positive. None reported highly to extremely negative perception in Science and Technology. There is very negligible percentage who perceived negatively on the two disciplines. Moreover, only a fraction of students, jointly less than 10%, who have negative perceptions on the other disciplines and in STEM Career. These results may give a startling implication that the students have highly positive perceptions on the STEM disciplines and career while the CHED (2010) enrolment data on STEM-related degrees is very low. Probable reasons to this phenomenon would include the following: (1) STEM courses are expensive in higher education institutions; (2) the perception that college education are not as easy as in high school; and (3) their actual physics results are low. In this vein, according to the Global Science Forum (OECD, 2006) and the STEM Strategic Steering Committee of Purdue University (2013), there is a sharp difference between the positive opinion of the students towards STEM and their actual wish to pursue STEM careers. Although STEM disciplines and professions continue to generate great interest among youth in developing countries, this is no longer the case for industrialized countries. Many young people have a negative perception of these careers and lifestyles. Incomes are perceived as low relative to the amount of work involved and the difficulty of the required studies. Few students have a full or
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY accurate understanding of STEM careers, and many are largely unaware of the range of career opportunities opened up by STEM disciplines and studies. Student decisions about study and career paths are primarily based upon interest in a particular field, and on their perception of job prospects in that field (OECD, 2006). Imperative to this, educational content and curricula are important in raising and maintaining studentsâ&#x20AC;&#x2122; interest in STEM disciplines. On one hand, positive experiences with science and technology at an early age can have a long-lasting impact. While negative experiences at school, due to uninteresting content or poor teaching, are often very detrimental to future choices. Furthermore, curriculum structures can also play an important role in averting high school students from pursuing their natural preferences. Accurate knowledge about STEM disciplines and career prospects are key elements of orientation, but are currently concerned with stereotypes and incomplete information. Science and technology face increasing competition for good students from new, more fashionable degrees in higher education. Image and motivation surveys show that the perception of science and technology remains largely positive among young people (OECD, 2006). Science and technology are considered important for society and its evolution despite concerns in specific areas, such as the negative environmental and societal consequences of science, technology and engineering. Scientists are also among the professionals the public trusts most, even though their prestige has declined. Higher management or government positions are rarely held by scientists or engineers unlike in state colleges
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY and universities (SUCs), and media reports on STEM events do not focus on the researchers themselves, who are thus very rarely known by name.
4.1.7. Relationships among Physics Self-Efficacy, Physics Learning Results, and Career Interest in STEM Chiou and Liang (2012) purported that self-efficacy exerts a positive effect on learnerâ&#x20AC;&#x2122;s goals, academic achievements and career choices. Correlation statistics was used to ascertain the associations of physics self-efficacy of the respondents, their physics learning results, and career interest to STEM degrees; and to test the null hypotheses stated in Chapter 1 with the alpha level set at 0.05. Table 17 presents the correlation matrix among the subscales on physics selfefficacy, physics learning results and STEM career interest. Generally, it is gathered that most relationships are of moderate strength and most correlations are significant even at 99% significance level. The highest correlations that are significant among the data is on Math and STEM Career (r=0.610, p=0.00) and in Science and STEM Career (r=0.606, p=0.00). These two subjects are most associated by the students in their perceptions towards STEM professions. The other two disciplines, Engineering and Technology are not far in strength and significance in associating with STEM Career (both has r=0.550, p=0.000). Therefore, all the STEM disciplines are necessary among the students in their awareness and interest to STEM careers. One association that is not significant is on Engineering and physics learning results (r=0.099, p=0.08). The association is negligible in magnitude and perhaps this
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Table 17. Correlations among the scales on physics self-efficacy, physics learning results, and career perception in STEM degrees. Science
Math
Eng’g
Tech
STEM Career
Math
0.512**
Eng’g
0.428**
0.445**
Tech
0.573**
0.460**
0.394**
STEM Career
0.606**
0.610**
0.550**
0.550**
PSE
0.261**
0.196**
0.263**
0.124*
0.219**
PLR
0.132**
0.109*
0.099NS
0.139*
0.184**
PSE
0.183**
*p<.05 **p<.01 NS – not significant
infers that the students do not see the relationship of the items in the Physics Knowledge Content Test to the field of engineering. The students at this point of their education may have not yet understood the very nature of engineering such that they cannot seem to associate physics to it. With regards to the students’ physics learning results and their perception in STEM career, a low but a very significant correlation is established (r=0.184, p=0.001). Increasing the students’ competence in physics would infer higher interest for them to
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY consider STEM as a profession. The STEM Strategic Steering Committee of Purdue University (2013) reported that pre-college students with strong academic preparation enrolls and sustains in STEM. Academic success in high school will provide a good foundation in career choices. The relationship between students’ physics self-efficacy and their perception in STEM career is low but highly significant (r=0.219, p=0.00). The same is true with the students’ physics self-efficacy and their physics learning results (r=0.183, p=0.00). Their confidence towards physics learning is associated with their physics achievement and in their preferential interest to STEM career. Therefore, the null hypothesis that states there are no significant relationships among physics selfefficacy, physics learning results and career perception in STEM degrees is rejected. These results are substantiated by the social cognitive theory to which according to Chiou and Liang (2012), self-efficacy exerts a positive effect on the students’ learning goals, academic achievements and career choices. It is not only one-way; Bandura (1997) postulated that the relationship between self-efficacy and performance is reciprocal and on-going. A student’s successful task improves his or her self-efficacy leading to the adoption of more difficult goals. Successful performance with the new, more difficult, goal will, in turn, lead to even greater selfefficacy and thus the cycle continues. Moreover, the outcomes of Zhu (2007) and Rittmayer and Beier (2009) reinforce the ideology being established in this study that self-efficacy is a positive correlate of students’ course-taking. Physics self-efficacy predicts academic
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY performance beyond one’s ability or previous achievement because confident students are motivated to succeed. In the same manner, students with high physics self-efficacy set more challenging goals and work harder to accomplish those goals such as deciding and working on STEM-related courses.
4.2. Structural Equation Modeling (SEM) on Students’ Conceptions of Learning Physics, Approaches to Learning Physics, and Physics SelfEfficacy The SEM as an approach was utilized to examine the patterns of relationships among conceptions of learning physics, approaches to learning physics and physics self-efficacy. A two-step process was used to assess the goodness of fit of the measurement model and the structural model. Joint criteria for acceptable fit (Hu and Bentler, 1999) have been adopted in this study. This criteria requires a CFI>0.90 together with a RMSEA<0.05 or with an AGFI>0.90. Before the test of the structural model, the measurement model was tested for construct reliability and validity. The measurement model hypothesized a priori that: (1) conceptions of learning physics (CLP) can be explained by six factors: memorizing, testing, calculating and practicing, increasing one’s knowledge, applying, and understanding; (2) approaches to learning physics (ALP) can be explained by four factors: deep motive, deep strategy, surface motive, and surface strategy; (3) each subscale measure has a nonzero loading on the factor that is designed to measure (target loading), and zero loadings on all other factors (non-target loadings); (4) the
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY factors, consistent with the theory, are correlated; and (5) error variables associated with each measure are uncorrelated.
4.2.1. Fitness of Measurement Model on Conceptions of Learning Physics Confirmatory factor analysis was applied to validate the subscales used in this present study to ensure that the six subscales were suitable for use on conceptions of learning physics (see Figure 11). Model estimation through maximum likelihood technique showed satisfied data-model fitness result from the test of the initial measurement model. Comparative fit indices of CFI and AGFI indicated 1.000 and 0.980 respectively. Both manifested almost perfect model fit.
Figure 11. Standardized solutions of the measurement model of conceptions of learning physics.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY The RMSEA value which is 0.000 perfectly met the criteria of fitness evaluation which is <0.050. The ratio of chi-square to the degree of freedom is less than 3 which means model fit. All these details (see Table 19 on page 121) suggest that the measurement model on conceptions of learning physics achieve parsimony and fitness of the model.
4.2.2. Fitness of Measurement Model on Approaches to Learning Physics Confirmatory factor analysis was also applied to validate the subscales in the measurement model on approaches to learning physics as can be seen in Figure 12. Maximum likelihood technique reveals satisfied data-model fitness result in testing the measurement model. All fit indices satisfactorily met the criteria for model fit testing. CFI is 0.994 (>0.90), AGFI is 0.973 (>0.90) and RMSEA is 0.047 (<0.05).
Figure 12. Standardized solutions of the measurement model of approaches to learning physics.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Chi-square value for this measurement model is low at 3.402 (df=2). According to Li (2005), the lower the Chi-square, the better the model fits. The ratio of Chi-square to its degree of freedom is less than 3 which is ideally accepted for model fitness. These fit indices are also shown in Table 19. This measurement model of approaches to learning physics likewise gathered parsimony and fitness of the model. The generated results for the two measurement models of conceptions of learning physics and approaches to learning physics warranted the analyses of the structural model in the next stage.
4.2.3. Fitness of Structural Model on Studentsâ&#x20AC;&#x2122; Conceptions of Learning Physics, Approaches to Learning Physics, and Physics Self-Efficacy A structural model specifies the hypothesized causal structure among latent variables which is indicated as a path or arrow connecting the two variables. For this study, Figure 1 on page 12 shows the network of factors on studentsâ&#x20AC;&#x2122; conceptions of learning physics, approaches to learning physics, physics self-efficacy and the error variables. In estimating the model, maximum likelihood technique showed dissatisfied data-model fitness result. Table 18 presents that the initial RMSEA index is 0.065 which did not meet the fitness criterion of <0.05. Although other fit indices met the fitness
evaluation
satisfactorily
đ?&#x153;&#x2019; 2 /df ratio which is less than 3.
(CFI=0.960;
AGFI=0.919)
as
well
as
the
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY In this case, model modification was done to achieve best fit and parsimony of the structural model. The use of modification indices and Lagrange multiplier test were performed to determine if any error covariance parameters should be added to the model to improve the model fitness. Results indicated that the first potential respecification came from an error covariance parameter e2, e10. The re-specification was made by allowing e2 and e10 to co-vary. The new measurement model, with e2, e10 added, was retested using maximum likelihood technique and yielded a lesser chi-square value of 87.333. It is an improvement over the initial model but it is still not acceptable because of the RMSEA criterion of <0.05. Following the same modification processes, one more error covariance parameter was added into the model (see Table 18). The second error covariance parameter added to the model was e7, e10. After this re-specification, the RMSEA lowered down to 0.054, but it is still not enough. For the sake of model parsimony, it was decided to stop adding more error covariance parameters into the measurement model. However, another post hoc review of the construct items was performed to see if there were any paths that needed to be dropped (or trimmed). It was seen that the regression weight for conceptions of learning physics in the prediction of physics selfefficacy is not significantly different from zero at the 0.05 level (see Appendix K for complete AMOS output). This gives substantial basis to drop the CLP, PSE path. At this point, the RMSEA values went down to 0.052. In the same justification, the null hypothesis that states that there is no significant direct effect of the physics learning
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY continuum to physics self-efficacy is hereby not rejected. Only the variable on approaches to learning physics has direct effect on the studentsâ&#x20AC;&#x2122; physics self-efficacy.
Table 18. Summary of re-specification of structural model for conceptions of learning Physics, approaches to learning Physics, and Physics self-efficacy. Process
đ?&#x153;&#x2019;2
df
CFI
AGFI
RMSEA
1 Run initial structural model.
97.354
42
0.960
0.919
0.065
2 Add e2, e10.
87.333
41
0.966
0.924
0.060
3 Add e7, e10.
76.354
40
0.973
0.931
0.054
4 Drop CLP, PSE.
76.528
41
0.974
0.933
0.052
5 Add MEMORIZING, PSE.
70.413
40
0.978
0.938
0.049
Note: Criteria for model fit testing: CFI>=0.90, AGFI>0.90 and RMSEA<0.05 (Hu & Bentler, 1999). CFIComparative Fit Index; AGFI- Adjusted Goodness of Fit Index; RMSEA- Root Mean Square Error of Approximation.
During the post hoc analyses, it was observed that adding a path between memorizing and physics self-efficacy will improve the overall model fit. Such that repeating the analysis treating the regression weight for using memorizing to predict physics self-efficacy as a free parameter, the discrepancy will fall by at least 4.685. The results led to improved fit indices (đ?&#x153;&#x2019; 2 =70.413, df=40; CFI=0.978; AGFI=0.938; RMSEA=0.049). The đ?&#x153;&#x2019; 2 /df ratio is 1.75 (<3). The series of model modifications
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY improved the structural model by looking into the fit indices every after each respecification.
Table 19. Fit indices of measurement and structural models for conceptions of learning Physics, approaches to learning Physics, and Physics self-efficacy. Model
đ?&#x153;&#x2019;2
df
CFI
AGFI
RMSEA
Measurement Model on CLP
8.302
9
1.000
0.980
0.000
Measurement Model on ALP
3.402
2
0.994
0.973
0.047
Hypothesized Structural Model on CLP, ALP and PSE
97.354
42
0.960
0.919
0.065
Modified Structural Model on CLP, ALP and PSE
70.413
40
0.978
0.938
0.049
Note: Criteria for model fit testing: CFI>0.90, AGFI>0.90 and RMSEA<0.05 (Hu & Bentler, 1999). CFIComparative Fit Index; AGFI- Adjusted Goodness of Fit Index; RMSEA- Root Mean Square Error of Approximation.
Table 19 condenses the fit indices of measurement and structural models (both the hypothesized and the modified) which met the joint criteria of Hu and Bentler (1999). With this, to reject the null hypothesis that states the hypothesized structural model on conceptions of learning, approaches to learning, and physics self-efficacy does not show satisfactory degree of fit to the observed data is not supported.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Retaining the revised specifications, a modified version of the structural model is made. Figure 13 below shows the modified structural model of conceptions of learning, approaches to learning, and physics self-efficacy.
Figure 13. Standardized solutions of the structural model of conceptions of learning, approaches to learning, and physics self-efficacy. This modified model has the best fit (đ?&#x153;&#x2019; 2 =70.413, df=40; CFI=0.978; AGFI=0.938; RMSEA=0.049).
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY From the maximum likelihood estimate results of the structural model, standardized pathway coefficients were examined and drawn conclusions with about the specific model relations (e.g., direct effects and covariances). Standard path coefficients represented the strength of the relationships among the measured and latent factors (see Table 20). The higher a path coefficient is the stronger effect the casual factor has on the dependent variable (Jie, 2005). All the path coefficients of conceptions of learning physics (CLP) on its subscales are all positive. CLP had a coefficient of 0.830 on understanding, and it explained 68.9% of the variance of understanding (R-square value of path coefficient). CLP also had a coefficient of 0.789 on applying, and accounted for 62.2% of the variance of applying. Path coefficient between CLP and increasing oneâ&#x20AC;&#x2122;s knowledge was 0.715, and CLP explained 51.5% of the variance of increasing oneâ&#x20AC;&#x2122;s knowledge. These three are the highest path coefficients of CLP and these are on high-level conceptions of learning physics. The subscales on high-level conceptions of learning physics denote constructive-oriented learning and more sophisticated (Chiou & Liang, 2012; Tsai, 2004). It can be recalled that these students have higher mean on this particular level (đ?&#x2018;ĽĚ&#x2026; =3.77) than on the low-level conceptions of learning physics (đ?&#x2018;ĽĚ&#x2026; =3.72). Moreover, the path coefficients of approaches to learning physics (ALP) on its subscales are all positive also. ALP had a coefficient of 0.873 on deep strategy, and ALP accounted for 76.2% of the variance of deep strategy. ALP had a coefficient of 0.666 on surface motive, and ALP described 44.3% of the variance of surface motive.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY The combination of these two, deep strategy and surface motive is very possible among the public high school students. As Chiou and Liang (2012) put it, there is no dichotomy between the deep and surface approaches. Although approaches to learning physics is task-dependent, a student can use meaningful ways to learn physics but is motivated by surface reasons such as impressing the teacher and his or her family.
Table 20. Regression weights on the interaction among conceptions of learning Physics, approaches to learning Physics, and Physics self-efficacy. From
To
Path Coefficient
Multiple R-square
CLP
Memorizing
0.654**
0.427
CLP
Testing
0.593**
0.352
CLP
Calculating and Practicing
0.678**
0.459
CLP
Increasing Oneâ&#x20AC;&#x2122;s Knowledge
0.715**
0.511
CLP
Applying
0.789**
0.622
CLP
Understanding
0.830**
0.689
ALP
Surface Motive
0.666**
0.443
ALP
Surface Strategy
0.285**
0.081
ALP
Deep Motive
0.606**
0.367
ALP
Deep Strategy
0.873**
0.762
Memorizing
Physics Self-Efficacy
0.143*
ALP
Physics Self-Efficacy
0.510**
**p<.01 **p<.05
0.357
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY ALP had a coefficient of 0.510 on physics self-efficacy and memorizing had a coefficient of 0.413 on the same construct. Collectively, ALP and memorizing (a subscale of CLP) explained a mere 35.7% of the variance of physics self-efficacy. Other possible variables can account the remaining 64.3% of the variance of physics self-efficacy. These other predictors are represented by the error variable, e11, which can represent any and all influences on physics self-efficacy that are not shown elsewhere in the path diagram. Error variable, e11, represents not just an error of measurement in physics self-efficacy but also the students’ socioeconomic status, family set-up, the physical learning environment, teacher’s methodologies and every other variable that might directly influence physics self-efficacy but does not appear in the model.
4.3. Structural Equation Modeling (SEM) on Students’ Physics SelfEfficacy, Physics Learning Results, and STEM Career Interest The SEM was also used to establish patterns of correlation and/or covariance of PSE, PLR and students’ career interest to STEM degrees. Before the test of the structural model, the measurement model for STEM career interest was tested for construct reliability and validity. The measurement model hypothesized a priori that: (1) STEM career interest can be explained by five factors: science, technology, engineering, mathematics, and STEM career; (2) each subscale measure has a nonzero loading on the factor that is designed to measure (target loading), and zero loadings on all other factors (non-target loadings); (3) the factors, consistent with the
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY theory, are correlated; and (4) error variables associated with each measure are uncorrelated.
4.3.1. Fitness of Measurement Model on STEM Career Interest Maximum likelihood technique through confirmatory factor analysis of the measurement model (see Figure 14) discloses dissatisfied data-model fitness result. Table 21 shows that the initial RMSEA index of 0.654 is too high to meet the fitness criterion of <0.05. Other fit indices met the fitness evaluation (CFI=0.989; AGFI=0.954) as well as the đ?&#x153;&#x2019; 2 /df ratio which is 2.4 (<3), but the joint criteria must be satisfied (Hu & Bentler, 1999).
Figure 14. Standardized solutions of the measurement model of STEM Career Interest.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Model modification was completed to achieve best fit and parsimony of the measurement model. After the use of modification indices and Lagrange multiplier test, results showed that the only potential re-specification is to allow e13 and e14 to co-vary. In effect, e13, e14 was added to the initial measurement model. Maximum likelihood technique generated perfect fit indices for CFI and RMSEA, 1.000 and 0.000 respectively. A lesser chi-square value of 0.573 and a higher AGFI index of 0.954 were also generated in the process. It is a precise development over the initial model since the indices fittingly met the joint criteria of Hu & Bentler (1999). Table 21 below summarizes this process of model modification.
Table 21. Summary of re-specification of measurement model for STEM career interest. Process
đ?&#x153;&#x2019;2
df
CFI
AGFI
RMSEA
1 Run initial structural model.
11.63
5
0.989
0.954
0.654
2 Add e13, e14.
0.573
4
1.000
0.997
0.000
Note: Criteria for model fit testing: CFI>0.90, AGFI>0.90 and RMSEA<0.05 (Hu & Bentler, 1999). CFIComparative Fit Index; AGFI- Adjusted Goodness of Fit Index; RMSEA- Root Mean Square Error of Approximation.
The generated results of the measurement model of STEM career interest were consistently used in the analyses of the structural model in the next phase. It dealt on
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY the testing of the theorized structural model on the studentsâ&#x20AC;&#x2122; physics self-efficacy, physics learning results, and STEM career interest.
4.3.2. Fitness of Structural Model on Studentsâ&#x20AC;&#x2122; Physics Self-Efficacy, Physics Learning Results, and STEM Career Interest The structural model as shown in Figure 2 on page 13 shows the network of factors on the studentsâ&#x20AC;&#x2122; physics self-efficacy, physics learning results, STEM career interest, and the error variables. Like with the preceding processes of model estimation, maximum likelihood technique for this structural model, showed dissatisfied data-model fitness result.
Table 22. Summary of re-specification of structural model of studentsâ&#x20AC;&#x2122; physics selfefficacy, physics learning results, and STEM career interest. Process
đ?&#x153;&#x2019;2
df
CFI
AGFI
RMSEA
1 Run initial structural model.
431.787
13
0.335
0.309
0.319
2 Add e18.
13.865
12
0.997
0.972
0.022
Note: Criteria for model fit testing: CFI>0.90, AGFI>0.90 and RMSEA<0.05 (Hu & Bentler, 1999). CFIComparative Fit Index; AGFI- Adjusted Goodness of Fit Index; RMSEA- Root Mean Square Error of Approximation.
It can be gleaned in Table 22 that in the initial run of the structural model, it produced fit indices that are distant to meet the fitness criteria (CFI=0.335;
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY AGFI=0.309; RMSEA=0.319). The đ?&#x153;&#x2019; 2 /df ratio is 33 which is way above than the acceptable index of 3. Prior to the procedures on the use of modification indices and Lagrange multiplier test to perform model modification, a pop-up command from Amos software suggested inclusion of an error parameter on STEM career interest being an endogenous variable. Technically and theoretically, the prediction of the STEM disciplines and careers to STEM career interest is not perfect, thus the addition of an error variable is necessary (Arbuckle, 2010). Error variable represents much more than random fluctuations in STEM career interest due to measurement error. It can embody other possible predictors. The presence of an error variable is very essential because the path diagram is supposed to show all the variables that influence STEM career interest. Without this error variable, the structural model makes the implausible claim that STEM career interest is an exact linear combination of the science, technology, engineering and math disciplines and the STEM career. Therefore, the only change made was adding an error variable, e-18. The new structural model, with e18 added, was retested using maximum likelihood technique and yielded a lesser chi-square value of 13.865 and degrees of freedom of 12 to have a ratio of 1.17 (<3). All the fit indices met the joint criteria satisfactorily (CFI=0.997; AGFI=0.972; RMSEA=0.022). This modified structural model of studentsâ&#x20AC;&#x2122; physics selfefficacy, physics learning results, STEM career interest projected parsimony and fitness of the model.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Table 23 summarizes the fit indices of the hypothesized and the modified measurement and structural models. The null hypothesis that states the hypothesized structural model on physics self-efficacy to physics learning results and STEM career interest does not show satisfactory degree of fit to the observed data is not rejected.
Table 23. Fit indices of measurement and structural models for Physics self-efficacy, Physics learning results, and STEM Career Interest. Model
đ?&#x153;&#x2019;2
df
CFI
AGFI
RMSEA
Measurement Model on STEM Career Interest
0.573
4
1.000
0.997
0.000
Hypothesized Structural Model on PSE, PLR and STEM Career Interest
24.866
13
0.981
0.954
0.054
Modified Structural Model on PSE, PLR and STEM Career Interest
13.865
12
0.997
0.972
0.022
Note: Criteria for model fit testing: CFI>0.90, AGFI>0.90 and RMSEA<0.05 (Hu & Bentler, 1999). CFIComparative Fit Index; AGFI- Adjusted Goodness of Fit Index; RMSEA- Root Mean Square Error of Approximation.
After the model re-specification, Figure 15 on the next page shows the modified structural model of for Physics self-efficacy, Physics learning results, and STEM Career Interest. Standardized path coefficients were examined and drawn
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY conclusions with about the structural model relations basing from the maximum likelihood estimate results.
Figure 15. Standardized solutions of the structural model of Studentsâ&#x20AC;&#x2122; Physics SelfEfficacy, Physics Learning Results, and STEM Career Interest. This modified model has the best fit (đ?&#x153;&#x2019; 2 =13.865, df=13; CFI=0.997; AGFI=0.972; RMSEA=0.022).
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY The path coefficient of physics self-efficacy on physics learning results as shown in Table 24 is positive but very low (β=0.182). It only explained 33% of the variance of physics learning results. More other predictors can better explain the variance of physics learning results, such as but not limited to test preparations, actual testing area, studentâ&#x20AC;&#x2122;s physical and emotional well-being, testing anxieties, and language difficulties.
Table 24. Regression weights on the interaction among conceptions of learning Physics, approaches to learning Physics, and Physics self-efficacy. From
To
Path Coefficient
Multiple R-square
Physics Self-Efficacy
Physics Learning Results
0.182**
0.330
Physics Self-Efficacy
STEM Career Interest
0.262**
Physics Learning Results
STEM Career Interest
0.146*
STEM Career Interest
Science
0.708**
0.501
STEM Career Interest
Technology
0.636**
0.404
STEM Career Interest
Engineering
0.633**
0.401
STEM Career Interest
Mathematics
0.711**
0.506
STEM Career Interest
STEM Career
0.859**
0.738
0.103
**p<.01 **p<.05
It can be recalled that these students have high physics self-efficacy but they have beginning level of mastery in their physics learning results. The path and regression analyses results in this study firm up the concept that their confidence
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY towards physics learning is not a solitary and end-all basis for their physics achievement. Albeit the direct effect seems minimal, it should not be totally disregarded because it is significant (p=0.001). With this, the null hypothesis that states there is no significant direct effect of physics self-efficacy to physics learning results is rejected. Both physics self-efficacy and their physics learning results had low path coefficients on the students’ STEM career interest (β=0.182 and β=0.182 respectively). Together, physics self-efficacy and their physics learning results explained a mere 10.3% of the variance of STEM career interest. Other possible variables that can account most of the variance of STEM career interest may include family and teacher’s influence, peer pressure, socioeconomic factors, and societal demands. Nevertheless, the path coefficients are significant at 0.05 alpha level which still infers that the effects of students’ physics self-efficacy and their physics learning results have substantive bearing to their interest towards STEM career. So, the null hypotheses that state there is no significant direct effect of physics self-efficacy to STEM career interest and there is no significant direct effect of physics learning results to STEM career interest are both rejected. The construct, STEM career interest, influences differently on the STEM disciplines: science (β=0.708), technology (β=0.0.636), engineering (β=0.633) and math (β=0.711). Math was the highest influenced among the STEM disciplines, followed by science. All of the path coefficients are significant at 0.01 level of significance and this recognizes that the student’s interest to STEM careers are
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY essential in the increase of their perception in all the disciplines. It can be recollected that the perception levels of the students are highly positive to all STEM subjects. Lastly, STEM career interest had a high coefficient of 0.859 on the students’ perception of STEM careers, and it explained 73.8% of the variance of the students’ perception of STEM careers. This is a manifestation that the students’ interest has direct effect to how they perceive about having a STEM career in the future. These students held highly positive perception on STEM careers. With these information, it can only be yearned that these students will actually consider to pursue STEM careers.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY
Chapter 5 SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS
This chapter presents the summary and findings, the conclusions, the generated theory, and recommendations for the benefit to what this research study is intended for.
5.1. Summary and Findings This study explored the structure of Physics self-efficacy and investigated its relationships with conceptions of learning Physics, approaches to learning Physics, Physics learning results and career interest to STEM degrees among the senior public high school students. Specifically, it sought to (1) determine the students’ level of conceptions of learning Physics in the following: memorizing, testing, calculating and practicing, increasing one’s knowledge, applying, and understanding; (2) identify the students’ approaches to learning Physics whether on surface motive and/or strategy, or deep motive and/or strategy; (3) ascertain the students’ level of Physics self-efficacy; (4) establish if there are significant relationships among conceptions of learning, approaches to learning, and Physics self-efficacy; (5) determine the students’ proficiency level in Physics learning; (6) identify the students’ career interest level in science and technology, engineering and mathematics (STEM) disciplines and
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY careers; (7) establish if there are significant relationships among students’ Physics self-efficacy, Physics learning results and STEM career interest; (8) come up with structural equation models that would best fit the structure of students’ physics selfefficacy; (9) test if there are significant direct effects of students’ conceptions of learning physics and approaches to learning physics to physics self-efficacy; and (10) test if the students’ physics self-efficacy has significant direct effects to physics learning results and STEM career interest. This study employed quantitative methods of research. It is descriptivecorrelation in design which involved a survey to 317 students in the public secondary schools in the East II district of the division of Cagayan de Oro City for school year, 2013-2014. Structural equation modeling (SEM) was also done for further relational analyses. Based on the gathered and treated data, the major findings of this study are presented in the following:
1. The participants of this study have high levels in their conceptions of learning physics, namely: (1) memorizing; (2) testing; (3) calculating and practicing; (4) increasing one’s knowledge; (5) applying; and (6) understanding. 2. The same respondents have high levels in their approaches to learning physics, specifically on: (1) deep motives; (2) deep strategies; (3) surface motives; and (4) surface strategies.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY 3. There is a significant relationship between students’ conceptions of learning physics with their approaches to learning physics in a physics learning continuum. 4. The participants of this study have high physics self-efficacy. 5. There are significant relationships among students’ conceptions of learning physics, approaches to learning physics, and physics self-efficacy. 6. These physics students have a proficiency level of beginning in their physics learning results. 7. The same respondents are highly positive in their perceptions towards science, technology, engineering and math disciplines and careers. 8. There are significant relationships among students’ physics self-efficacy with their physics learning results and their career interest to STEM. 9. The modified structural model would best fit the structure of students’ physics self-efficacy with approaches to learning physics as the main predictor. 10. There is a significant direct effect of the students’ approaches to learning physics to their physics self-efficacy and the memorizing subscale of the conceptions of learning physics to their physics self-efficacy.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY 11. The modified structural model would best fit the structure of students’ physics self-efficacy as predictor to their physics learning results and STEM career interest. 12. There are significant direct effects of students’ physics self-efficacy to their physics learning results and to STEM career interest. 13. There is a significant direct effect of students’ physics learning results to their STEM career interest.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY 5.2. Conclusions In the light of the findings of this study, the following conclusions were drawn: The dismal situation in physics learning in high schools at present has drawn this study to ascertain how students view and approach physics learning, and on how confident they are in carrying out physics learning tasks. Among many factors that are encouraging to the students, they actually conceive and learn physics in a highly fashion such that they gained high confidence in their capability to learn physics. This outcome gives an impression to education leaders and practitioners that the students are basically organized and set to learn physics. Exploring how physics self-efficacy is formed and structured, the structural equation model manifested that the students’ actual learning experiences has more influence in the development of their confidence toward physics than their conceptions in learning the subject. The more the students are active and practical in understanding physics concepts, the more confident they get in physics learning. It reinforces the idea on constructivist learning approaches where students are engaged to “hands-on, minds-on” way of learning. Along that thought, the strategy for physics learning discourages instructional methods where the students take more of a passive role as it may have adversarial effect on their physics self-efficacy. Contrariwise, the students’ confidence to learn physics - while high and significant - has minor predictive value to achievement. This result does not generally accord to various existing literature that stressed the relationship between self-efficacy and academic achievement. It is nonetheless argued that many other possible
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY predictors can influence on learners’ performance in test results. This rounds up the study that aiming on improving physics learning results does not only rely on looking at the students’ self-efficacy. It rather submits a holistic approach to progress students’ assessment routines and achievement in general. Furthermore, the students’ interest to STEM careers are not solely determined by their physics self-efficacy and their proficiency in physics. These students are selfassured on their capability on the physics subject but other factors may compose to be more influencing on their interest to STEM careers and on their actual choosing of this line of career path. Nevertheless, these results still establish the importance of high school STEM preparation as a requisite to college STEM achievement.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY 5.3. Theory Generation The findings and conclusions of this study drew two general theories concerning on the physics self-efficacy of the students.
First: “The students’ conceptions of learning physics can significantly relate to their approaches to learning physics, which can consequently exert a positive effect in the structure of their physics self-efficacy.”
Second: “There is positive and significant interaction between and among the students’ physics self-efficacy, physics achievement and interest to STEM careers.”
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY 5.4. Recommendations With regard to the conclusions and theories made in this study, the following recommendations are proposed: 1. For physics teachers to provide a supportive learning environment that aims at promoting studentsâ&#x20AC;&#x2122; high-level conceptions, deep learning approaches and high physics self-efficacy. A physics teacher can clearly bring into instructional routine the discussion of the nature of physics learning to the students and encourages them to develop higher conceptions of learning and deep learning strategies, like to treat physics learning as application and understanding instead of rote memorization and calculation. 2. For physics teachers and education leaders to offer a science learning curriculum that reinforces alternative learning approaches to physics. More constructivistsâ&#x20AC;&#x2122;-inspired activities such as project-based or problembased learning can be introduced to actively engage the students to a meaningful and practical learning process and at the same time, cultivate deep motives in physics learning. 3. For physics teachers to give guidance and opportunities for students to constantly appraise their own academic performances and outcomes. Since these students have high self-efficacy, these students are also self-appraising. Ample opportunities may be provided for students so
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY that they can assess their own learning. Students observing the successes of others contribute to the observers’ beliefs about their own capabilities. Such that the students can say, “If they can do it, so can I.” Also, students can see similarities in others and assume that the others’ performance is diagnostic of their own capabilities. To which the students can say, “So all along I was right.” All of these are relevant to the social cognitive theory of Bandura (1997) highlighting the notion that humans learn from experience, then they self-reflect and self-regulate. 4. For STEM-oriented higher education institutions (HEIs) to conduct relevant campaigns and programs to boost students’ STEM interest. While it is ascertained that significant association is present between physics proficiency and STEM career interest, certain academic and promotional activities can be piloted to improve students’ STEM interest level. Like in the University of Massachusetts (2011), this school provides experiential summer camps to high school students applying inspiring practices for the students such as using professional scientific tools and interacting with STEM professionals. Outcomes indicated that students felt they learned a lot, gained experience with scientific skills, and were interested to STEM more. Along this idea, HEIs that are oriented to STEM like Mindanao University of Science and Technology (MUST), Mindanao State University-Iligan Institute of Technology (MSU-IIT), and Misamis
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Oriental State College of Science and Technology (MOSCAT) may conduct similar campaigns and programs. 5. For elementary schools to deliver a culture of promising STEM-related activities in their science and mathematics instruction. Bandura (1977) postulated that one source of self-efficacy is mastery experiences such that consistent exposure to good practices builds up more confidence to oneâ&#x20AC;&#x2122;s capacities and goals. Concepts in STEM disciplines are actually introduced in the elementary education curriculum but strengthening this foundation will give a stronger groundwork for students to appreciate STEM in high school and in college. 6. For future research, qualitative studies can be conducted to confirm the results of this undertaking. A qualitative study that can delve more into deeper level can help education researchers and practitioners to elaborately understand the concept and development of physics selfefficacy among the students. 7. For future research, researchers can explore on more related constructs that may mediate studentsâ&#x20AC;&#x2122; physics learning and their physics self-efficacy. As Bandura (1997) postulated it, self-efficacy is not an isolated trait but a complex belief system. So there may be more variables that can help explain the variance of the constructs investigated by this researcher.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Bibliography Alburo, F. & Abella, D. (2002). Skilled Labor Migration from Developing Countries: Analysis of Impact and Policy Issues. Geneva: International Labor Office. Arbuckle, J. (2012). IBM SPSS Amos 19 Userâ&#x20AC;&#x2122;s Guide. Illinois: Amos Development Corporation. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191-215. _____________ (1984). Recycling Misconceptions of Perceived Self-Efficacy. Cognitive Therapy and Research, 8(3), 231-255. _____________ (1988). Self-efficacy conception of anxiety. Anxiety Research, 1(2), 77-98. doi: 10.1080/10615808808248222 _____________ (1997). Self-efficacy: The exercise of control. New York, NY: Freeman. _____________(2006). Guide for Constructing Self-Efficacy Scales. In F. Pajares & T. C. Urdan (Eds.), Self-efficacy beliefs of adolescents (pp. xii, 367 p.). Greenwich, Conn.: IAP - Information Age Pub., Inc. Bandura, A., & Schunk, D. H. (1981). Cultivating Competence, Self-Efficacy, and Intrinsic Interest through Proximal Self-Motivation. Journal of Personality and Social Psychology, 41(3), 586-598. Baran, M., & A, K. M. (2011). A study of relationships between academic self concepts, some selected variables and physics course achievement. International Journal of Education, 3(1), 1-12. Retrieved from http://search.proquest.com/docview/870646560?accountid=141440 Barry, C. D., Paul, C. B., Purdie, N., Gillian Boulton-Lewis, & al, e. (2000). Students' conceptions of learning, the classroom environment, and approaches to learning. The Journal of Educational Research, 93(4), 262. Retrieved last January 1, 2014 from http://search.proquest.com/docview/204211000?accountid=141440
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Bernardo, A. B. I. (2003). Approaches to Learning and Academic Achievement of Filipino Students. The Journal of Genetic Psychology, 164(1), 101-14. Retrieved last January 1, 2014 from http://search.proquest.com/docview/228450563?accountid=141440 Bernido, C. & Bernido, M. (2012). The CVIF Dynamic Learning Program: Achieving Performance Targets with Strategic and Efficient Learning. Transactions of the Research Center for Theoretical Physics, Central Visayan Institute Foundation, Philippines. _____________ (2009). CVIF Dynamic Learning Program: A Systems Approach to Process-Induced Learning. Proceedings of epiSTEME 4, pp 269-273. _____________ (2008). Learning Physics as One Nation-Physics Essentials Portfolio. Cebu, Philippines: Clavano Printers. _____________ (2004). Science, Culture and Education tor Change, Part I: Innovative Strategies for Secondary Education in the Philippines. Transactions of the National Academy of Science and Technology, 26(2), 243-267, Philippines. Bentler P. & Chou, C. P. (1987). Practical Issues in Structural Modeling. Sociological Methods & research, 16, 78-117. Retrieved last January 2014 at http://www.unt.edu/rss/class/Jon/MiscDocs/BentlerAndChou_1987.pdf Biggs, J. (1993). What Do Inventories of Students’ Learning Processes Really Measure? A Theoretical Review and Clarification. British Journal of Educational Psychology, 63, 1-17. Biggs, J.B., Kember, D., & Leung, D.Y.P. (2001). The Revised Two Factor Study Process Questionnaire: R-SPQ-2F. British Journal of Educational Psychology, 71, 133-149. Bloom B. S. (2001). Taxonomy of Educational Objectives, Handbook I: The Cognitive Domain. New York: David McKay Co Inc. Bouffard-Bouchard, T., Parent, S., & Larivée, S. (1991). Influence of SelfEfficacy on Self-Regulation and Performance among Junior and Senior High-School Age Students. International Journal of Behavioral Development, 14, 153-164.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Browne, M. W. & Cudeck, R. (1993). Alternative ways of assessing model fit. In: IBM SPSS Amos 19 User’s Guide. Illinois: Amos Development Corporation. Byrne, B. M. (1994). Structural equation modeling with EQS and EQS/Windows, basic concepts, applications, and programming. Thousand Oaks London New Delhi: SAGE Publications. Bustos, A. & Espiritu S.C. (1996). Foundation of Education I. Philippines: Goodwill Trading Co., Inc. Causapin, M. G. (2012). Mathematics self-efficacy and its relation to proficiencypromoting behavior and performance. Published dissertation, Columbia University, USA. Cavallo, A. M. (2004). Gender Differences in Learning Constructs, Shifts in Learning Constructs, and Their Relationship to Course Achievement in a Structured Inquiry, Yearlong College Physics Course for Life Science Majors. School of Science and Mathematics, 104.6: 288-300. Accessed at <http://search.proquest.com/pqcentral/docview/195205074/abstract/13F9877 C9A74BB7E036/7?accountid=141440> Center for Educational Measurement. (2011). Institutional Pre-Test Performance Report for Science and Technology IV. Taken from the Department of Education-Division of Cagayan de Oro City. Chan, K. W. (2012). Hong Kong Teacher Education Students’ Epistemological Beliefs and their Relations with Conceptions of Learning and Learning Strategies. The Asia-Pacific Education Researcher, Vol 16:2, pp.200-214. Chiou, G. L. & Liang, J. C. (2012). Exploring the Structure of Science Selfefficacy: A Model Built on High School Students’ Conceptions of Learning and Approaches to Learning in Science. The Asia-Pacific Education Researcher, Vol 21:1, pp.83-91. Corpuz, B. and Salandanan, G. (2007). Principles of Teaching Vol.1. Quezon City: Lorimar Publications. Dart, B., Burnett, P., Purdie, N., Boulton-Lewis, G. (2000). Students' conceptions of learning, the classroom environment, and approaches to learning. The Journal of Educational Research. Retrieved last January 2014 at www.sagepub.com/holt/articles/Burnett.pdf
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Delialioglu, O. & Askar, A. (1999). Contribution of Students' Mathematical Skills and Spatial Abillty to Achievement in Secondary School Physics. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi /6-/7 : 34 - 39 [/999J. Dickie, L. (2003). Approach to Learning, the Cognitive Demands of Assessment, and Achievement in Physics. The Canadian Journal of Higher Education 33.1. Accessed last October 2013 at <http://search.proquest.com/pqcentral/docview/221210412/13F9853690A 5C1068DC/1?accountid=141440> Ding, L. (2013). A Comparative Study of Middle School and High School Students’ Views about Physics And Learning Physics. AIP Conf. Proc. 1513, 118 (2013); doi: 10.1063/1.4789666. Domingo, R. (2012). Brain drain more than doubled in last 12 years. Appeared in Philippine Daily Inquirer. Accessed at <http://globalnation.inquirer.net/43311/brain-drain-more-than-doubled-inlast-12-years#ixzz2ZSufg4nT> Douglas, J. L. (2010). Motivational beliefs and learning strategies as predictors of Academic performance in college physics. College Student Journal, 44(4), 920-927. Retrieved from http://search.proquest.com/docview/848933396?accountid=141440 Fencl, H. & Scheel, K. (2004). Pedagogical Approaches, Contextual Variables, and the Development of Student Selfefficacy in Undergraduate Physics Courses. AIP Conf. Proc. 720, 173 (2004); doi: 10.1063/1.1807282 Ghorban, H. A., Moradi, S., & Gholam, R. D. (2013). The relationship between students' conceptions of learning and their academic achievement. Psychology, 4(1), 44-49. Retrieved last January 1, 2013 from http://search.proquest.com/docview/1318921187?accountid=141440 Hu, L.T., & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structural analysis: conventional criteria versus new alternative. Structural Equation Modeling, 6, 1-55. Kizilgunes, B., Tekkaya, C., & Sungur, S. (2009). Modeling the Relations among Students' Epistemological Beliefs, Motivation, Learning Approach, and Achievement. The Journal of Educational Research 102.4: 243-255,320. Accessed at <http://search.proquest.com/pqcentral/docview/204194920/13F9877C9A74B B7E036/41?accountid=141440>
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Lee, M., Johanson, R. and Tsai, C. (2008). Exploring Taiwanese High School Studentsâ&#x20AC;&#x2122; Conceptions of and Approaches to Learning Science through a Structural Equation Modeling Analysis. Science Education, 92, 191-220. Li, J. (2005). An examination of a structural equation model of readiness to use complementary and alternative medicine among Australian university students. Published dissertation, University of Maryland, USA. Li, S. & Demaree, D. (2013). Physics Learning Identity of a Successful Student: a Plot Twist. AIP Conf. Proc. 1513, 242 (2013); doi: 10.1063/1.4789697 Loehlin, J. C. (1992). Genes and environment in personality development. USA: Sage Publications, Inc. Retrieved last January 2014 at http://projectprime.us/early-stem-path/ Lynch, D. (2010). Motivational Beliefs and Learning Strategies as Predictors of Academic Performance in College Physics. College Student Journal, 44.4: 920-927. Accessed last October 2013 at <http://search.proquest.com/pqcentral/docview/848933396/13F9877C9A7 4BB7E036/38?accountid=141440> Mueller, R. O. (1996). Basic Principles of Structural Equation Modeling, an introduction to LISREL and EQS. New York: Springer. Mulhall, P. (2012). Views About Learning Physics Held by Physics Teachers with Differing Approaches to Teaching Physics. Journal of Science Teacher Education, 23.5 (Aug 2012): 429-449. Accessed last October 2013 at < http://search.proquest.com/pqcentral/docview/1026853291/13F9877C9A7 4BB7E036/12?accountid=141440> Nicolas, I. (2011). Heroes and Heroines from the Homeland: Migration from a Philippine Perspective. Paper presented at the 16th International Metropolis Conference, Migration Futures: Perspectives on Global Changes, Azores Islands. Obrentz, S. B. (2012). Predictors of Science Success: The Impact of Motivation and Learning Strategies on College Chemistry Performance. Educational Psychology and Special Education Dissertations. Retrieved last Jan 2014 at http://scholarworks.gsu.edu/cgi/viewcontent.cgi?article=1078&context=epse_ diss
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY University of Massachusetts. (2011). Increasing Student Interest in Science, Technology, Engineering, and Math (STEM): Massachusetts STEM Pipeline Fund Programs Using Promising Practices. Retrieved last January 2014 at http://www.mass.edu/forinstitutions/prek16/documents/Student%20Interest%2 0Summary%20Report.pdf Urdan, T., & Midgley, C. (2003). Changes in the Perceived Classroom Goal Structure and Pattern of Adaptive Learning during Early Adolescence. Contemporary Educational Psychology, 28, 524-551. Usher, E. L. & Pajares S. (2008). Sources of Self-efficacy in School: Critical review of the literature and future directions. Review of Educational Research, Vol 78, 751-796. Violato, C., & Donnon, T. (2003). Testing competing structural models of approaches to learning in a sample of undergraduate students: A confirmatory factor analysis. Canadian Journal of School Psychology, 18(1), 11-22. Retrieved from http://search.proquest.com/docview/224376596?accountid=141440 Weber, K. (2012). Gender Differences in Interest, Perceived Personal Capacity, and Participation in STEM-Related Activities. Journal of Technology Education Vol. 24 No. 1. Wigfield, A. & Meece, J. (1988). Working memory, math performance, and math anxiety. Journal of Educational Psychology, Vol. 80, No. 2,210-216. Retrieved last January 2014 at http://www.andrews.edu/sed/gpc/facultyresearch/montagano-research/working_memory_math.pdf Wilson, R., Georgakis, S. & Sharma, M. (2012). Approaches to Learning in First Year University Physics. Journal of Social Sciences. 8.2: 216-222. Accessed at <http://search.proquest.com/pqcentral/docview/1288356184/13F984DBD3 249340B57/7?accountid=141440> Wood, T., Knezek, G. & Christensen, R. (2010). Instruments for Assessing Interest in STEM Content and Careers. JI of Technology and Teacher Education. Vol 18, pp 341-363. Accessed at <http://www.iittl.unt.edu/IITTL/itest/msosw_web/pubs/STEMInstruments.pd f>
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY _____________ (2011). Contrasting Perceptions of STEM Content and Careers. Contemporary Issues in Technology and Teacher Education, 11(1), 92-117. Retrieved last January 2014 at http://www.citejournal.org/articles/v11i1general1.pdf Zhu, Z. (2007). Learning Content, Physics Self-Efficacy, and Female Studentsâ&#x20AC;&#x2122; Physics Course-Taking. International Education Journal, 8(2), 204212. Accessed at <http://iej.com.au> Zimmerman, B. J., & Bandura, A. (1994). Impact of Self-Regulatory Influences on Writing Course Achievement. American Educational Research Journal, 31, 845-862. Zimmerman, B. J., & Kitsantas, A. (1997). Developmental Phases in SelfRegulation: Shifting From Process Goals to Outcome Goals. Journal of Educational Psychology, 89, 29-36.
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Vita
Ray Butch D. Mahinay Door 22 Parola, Macabalan, 9000 Cagayan de Oro City (088) 323-0727 (Office); +63.917.555.3987 (Mobile) ray.mahinay@deped.gov.ph Education 2014
Doctor of Philosophy in Educational Planning and Management College of Policy Studies, Education and Management Mindanao University of Science and Technology
2009
Master of Arts in Science Education Major in General Science Bukidnon State University (Main Campus) Thru DepEd-BSU Graduate Fellowship Program
2007
Master of Science in Environmental Science and Technology Mindanao University of Science and Technology Earned units only
2003
Bachelor in Secondary Education Major in General Science Xavier University-Ateneo de Cagayan Thru Valedictorian-Academic Scholarship and CHED Center of Excellence for Teacher Education Scholarship
Employment 2004-present
Secondary School Teacher III Department of Education-Tablon National High School Division of Cagayan de Oro City
2012-present
Associate Professor II College of Education Capitol University
2012-present
Resident Lecturer for LET-Science PEAK Excellence Training Academy and Review Center Cagayan de Oro, Butuan, Valencia and Surigao Centers
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY Research Publications 2014 Structural Equation Model on Conceptions of Learning And Approaches to Learning as Predictors of Physics Self-Efficacy IAMURE: International Journal of Education, Vol. 9 Print ISSN 2244-1476; Online ISSN 2244-1484
2012
Non-traditional Beliefs of Secondary School Teachers as Correlates to Teaching Practices IAMURE: International Journal of Education, Vol. 1, pp 139-148 Print ISSN 2244-1476; Online ISSN 2244-1484 doi: http://dx.doi.org/10.7718/ije.2012.1.1.139148
2009
The Secondary School Teachers’ Science Beliefs as Correlates to Teaching Practices Bukidnon State University’s The Graduate School Research Journal Vol. 22, pp 252-267 Print ISN 0119-1195
Research Presentations 2013 Structural Equation Model on Conceptions of Learning and Approaches to Learning as Predictors of Physics Self-Efficacy Asian Conference on Multidisciplinary Research in Higher Education (ACMRHE 2013) At Manila Marriott Hotel, Pasay City Diamond Awardee (First Place) for Oral Research Presentation
2011
Non-traditional Beliefs of Secondary School Teachers as Correlates to Teaching Practices Asian Conference for Higher Education Research (ACHER 2011) At Pryce Plaza, Cagayan de Oro City Diamond Awardee (First Place) for Oral Research Presentation
172
MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY 2011
Consistency of Studentsâ&#x20AC;&#x2122; Academic Achievement in Physics and the National Career Assessment Examination (NCAE) Results in Scientific Ability DepEd Division In-Service Training of Science Teachers At Regional Science High School, Cagayan de Oro City
Professional Organizations 2013-present IAMURE Multidisciplinary Research Member 2013-present
Philippine Public School Teachers Association Member
2012-present
DepEd Region 10 School Paper Advisers Association Vice President
2010-present
Biology Teachers Association of the Philippines Member
2009-2010
Toastmasters International Member
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MINDANAO UNIVERSITY OF SCIENCE AND TECHNOLOGY