Influence of an Adaptive Learning System on Exam Performance and Learning Success

Journal of Information Technology and Application in Education Volume 5, 2016 www.jitae.org doi: 10.14355/jitae.2016.05.001

Influence of an Adaptive Learning System on Exam Performance and Learning Success Anne Kersebaum*1, Julia Gillen2, Thomas Rath3 Institute for Horticultural Production Systems, Leibniz Universität Hannover, Herrenhäuser Str. 2, 30419 Hannover, Germany *1

Institute for Vocational and Adult Education, Leibniz Universität Hannover, Schloßwender Str. 1, 30159 Hannover, Germany 2

Biosystems Engineering Laboratory, Osnabrück University of Applied Sciences, Oldenburger Landstr. 24, 49090 Osnabrück, Germany 3

*1

kersebaum@bgt.uni‐hannover.de; 2julia.gillen@ifbe.uni‐hannover.de; 3t.rath@hs‐osnabrueck.de

Abstract Adaptive learning sytems can help to handle heterogenous learning groups in online courses. This study introduces experiences with an adaptive learning system (regarding learning styles) in a blended learning course with adult students from different academic institutions (universities and universities of applied sciences), concerning exam performance and long‐term learning success. Results show that students who work through a lesson adaptively more often benefit from the adaptivity than it is a disadvantage for them. Furthermore, the independence of adaptivity makes it evident that the long‐term learning effect of actively acquired knowledge in a virtual learning environment is higher than that of knowledge from a question catalogue for exam preparation that is acquired under pressure.

Keywords ELearning; Moodle; Adaptivity; Learning Style Inventory

Introduction An adaptive learning system can determine the need of support of the students and adapt its teaching style to the single individual (Issing and Klimsa, 2009). The aim of these systems is the selection and presentation of learning materials, learning content, learning methods and learning objects, depending on relevant known learning characteristics of the student in order to individualize the learning process (Schulmeister, 2006). This approach makes sense if one considers how heterogeneous learning groups in online courses normally are. Differences among the students may occur, when regarding age, sex, computer affinity, motivation, knowledge of the subject and social and cultural environment. A number of learning system developers try to solve the problem of adaptivity by integrating questionnaires for learning preferences, tests for attitude to learning or even motivation and learning style inventories of various origins (Schulmeister, 2006).

Furthermore, Schulmeister (2006) pointed out that the term of adaptive learning systems was understood in various ways and distinguished between different forms of adaptivity: 

Adaptivity of the interface: refers mainly to the external configuration of the learning system (e. g. accessibility, variations in cultural and ethnic diversity, gender issues, layout, switchable tools) and is very easy to implement.



Static learner adaptivity: at the beginning of the course, the system determines the characteristics of the student by a questionnaire or through testing and composes an individual course by selection and sequencing of the content. In the simplest case, this permits a different type of navigation or an alternative sequence of the sessions. At a higher level, the system presents other examples, exercise forms, tasks, chapters or media to the students. However, this adjustment is based solely on the classification from the beginning of the course.



Dynamic learner adaptivity: unlike static learner adaptivity, the peculiarities of the learners themselves in

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the process of learning are determined. Conclusions and methodological consequences are immediately implemented during the learning process in the student model and influence the presentation of the course and its contents.

Other models to describe different types of adaptivity can be found in Leutner (2002), Burgos et al. (2007) and Lehmann (2010).

Brusilovsky (1996) defined an adaptive learning system (he used the term ʺadaptive hypermedia systemʺ) as a collection of documents (pages) connected by links. Each page contains local information and a number of links to other documents. An index page or ʺsitemapʺ with links to all available pages is another example. The content of a single page is adaptable (adaptive presentation) and the links between pages (adaptive navigation support).

Adaptive presentation means to adapt the content of a page to the knowledge, the objectives and other characteristics of an individual user (Brusilovsky, 1996). For example, users who have already a fairly good level of knowledge get more detailed and more sophisticated information than those with little knowledge who receive therefore more explanations. This type of adaptivity can be performed in various ways (Brusilovsky, 1996, Brusilovsky, 2001): 

Adaptive multimedia presentation: information can be provided with various types of media, e. g. text, music, video, spoken language or animation. If different media files deal with the same content, the system selects the medium which is the most relevant for the user at this point.



Adaptive text presentation:



o

Natural language adaptation: uses computational linguistics as a basis for recognition and production of written language.

o

Canned text adaptation: a text is dynamically adapted to the reader by inserting, removing or altering text fragments. Text fragments may also be sorted in different ways or are dimmed. The techniques in this category also include stretchtext, an additional section which is inserted to the text by pressing a button, usually used to go into more detail.

Adaptation of modality: adjusts the presentation of the content to user preferences, skills, learning styles or context.

Adaptive navigation support, on the other hand, aims at helping the user finding his path through the system, by presenting links fitting to the objectives, the knowledge and other characteristics of the user (Brusilovsky, 1996, Brusilovsky, 2001): 

Direct guidance: the system decides which page the user should see next and provides a corresponding link.



Adaptive link sorting: links are sorted by relevance to the user and provided in a corresponding order.



Adaptive link hiding: hides, disables or removes links to pages which are not relevant for the user at this time or which require a higher level of knowledge.



Adaptive link annotation: the user is given additional information to a link. This may be a written comment or a visual clue.



Adaptive link generation: discovers new, useful links between pages and adds them permanently, creates links to related content or recommendates relevant links dynamically.



Map adaption: the structure of the page directory is changed. For this purpose, one or more of the above techniques can be used.

Other possible adaptation techniques are curriculum sequencing, intelligent analysis of student solutions, interactive problem solving support and adaptive collaboration support (Brusilovsky, 1998). In most cases, different learning style models (collected by questionnaire or neural network) serve as a basis of the adaptation.

But also the knowledge of the learner (determined by a knowledge test), combinations of a learning style model and knowledge, or the evaluation of various aspects of user behavior can serve as a basis adjustment (Lehmann, 2010).

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Literature provides a number of examples of the effectiveness of certain adaptive learning systems in terms of improved performance, reduced learning time and higher satisfaction among learners (e. g. Latham et al., 2014, Özyurt et al., 2013, Klasnja‐Milicevic et al., 2011, and Popescu, 2010). On the other hand, there are also some examples were adaptivity failed to generate a significant positive effect (e. g. Brown et al., 2006, Brown et al., 2007 and Mitchell et al., 2004). Moreover, literature also provides general criticism about the subject: Lehmann (2010) for example pointed out that many adaptive learning systems are developed by computer scientists which use complex technical methods with hardly pedagocical justification. Furthermore, an evaluation of effectiveness or acceptance of the adaptive courses compared with conventional courses is often missing. Baumgartner (2011) and Schulmeister (2006) also expressed clear doubts about the topic of learning style models in general and their pedagogically rational use in adaptive learning systems in special.

The following study will show a positive example of the effectiveness of an adaptive learning system and will introduce an evaluation of the learning success of university students influenced by this system. The evaluation is based on examination results and an exam repetition experiment 6 months after the original exam.

Material and Methods Adaptive Learning System The adaptive learning system used in this experiment is a result of a researche team headed by Dr. Sabine Graf from Athabasca University in Canada and is described and presented in detail in Graf et al. (2010). According to the authors, the mechanism behind is an extension of the classical functions of the learning management system Moodle (Moodle, 2015). It uses adaptive link annotation and adaptive link sorting for adaptation of courses to students’ learning styles, based on the learning style model of Felder and Silverman (1988). The adaptive mechanism highlights the most appropriate learning objects for the individual learning style of the student to optimize the learning process. To archieve this, three extensions were added to the original Moodle architecture: 1.

Detecting students’ information: students have to complete the Index of Learning Styles Questionnaire (ILS) of Felder and Soloman (1997) in the registration process of Moodle. The collected data about students’ learning styles are stored in the ʺstudent modelʺ.

2.

Annotating learning objects: the teacher chooses the respective type of learning object from a selection box while he/she creates the learning object. These meta‐data are stored in the ʺexpert modelʺ so that the system can distinguish between different learning object types.

3.

Presenting adaptive courses: the ʺadaptation moduleʺ accesses the learning style information in the ʺstudent modelʺ to calculate the values of each adaptation feature and composes an individual course based on this values from the learning objects in the “expert model”. The composed course gets visible on the user interface of the learning system. Depending on learning style preferences, the order of the offered learning object varies from student to student. Unsuitable learning objects are grayed out, but stay available .

Using the adaptive mechanism is made as easy as possible for teachers. They create learning objects as usual with the authoring tool and simply add information about the type of this learning object. The mechanism is flexible enough to be inserted into existing courses even retrospectively. The decision for the use of the introduced mechanism is made for the following reasons: unlike many other approaches of this kind, the mechanism offers a rational combination of an established learning management system (Moodle) and a learning style adaptive extension. The used mechanism provides great flexibility for the teacher who is not forced to follow a rigid structure, but instead can add learning objects himself or otherwise can use the learning system as usual. Learning objects only need a minimum of meta‐data, so the system can classify them and can use them for adaptive mechanism. As Graf et al. (2010) pointed out, it is not necessary to insert all types of learning objects in a course or in individual sections of the course. This makes the mechanism suitable as well for courses with a theoretical focus as also for courses with a practical focus. Furthermore, the adaptivity in this mechanism is based on the learning style model of Felder and Silverman (1988). This model is considered particularly suitable for the application in eLearning and web‐based learning systems. Last but not least, the support by the developers if setting up the adaptive Moodle server and adapting the system to the needs of the user is another good argument. In the present

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case, a linguistic customization of the user interface and an automated grouping process at registration (non‐ adaptive / adaptive) are meant. Finally, the used adaptive mechanism is based on Moodle version 1.9.7, which is obsolete now. Dr. Graf’s research team has now implemented the mechanism to Moodle version 2.x.

Felder‐Silverman Learning Style Model The Felder‐Silverman learning style model (Felder and Silverman, 1988) is a model often used in the context of adaptive learning systems and originally designed for engineering education. Carver et al. (1999) justified the popularity of the model with its particular suitability for hypermedia courses. A comparison of different learning style models with regard to their suitability in e‐learning and web‐based learning systems later confirms this and recommends Felder and Silverman’s model as the best suitable model in the field (Kuljis and Liu, 2005). Reliability, validity and usability of the ʺIndex of Learning Stylesʺ (ILS) questionnaire can be verified in a meta‐study by Felder and Spurlin (2005). The ILS questionnaire was created by Felder and Soloman (1997) in order to have a tool simple to handle for the identification of the particular learning styles of the students. However, the model assumes that each student has a preference for one of four learning style dimensions: sensing/intuitive, visual/verbal, active/reflective and sequential/global. Each of these dimensions has opposite poles (e. g. visual vs. verbal). An individual usually tends towards one of these poles in its normal behaviour, but its preference may be mild, moderate or strong. The personal learning style of a person can be understood as a combination of preferences of the respective dimensions (Lehmann, 2010). In some cases, an individual acts in contrast to its preferred preference.

Therefore, Felder and Silverman did not work with rigid categories (like many other models), but with a kind of scale along the various dimensions, which are briefly explained as follows: 

Sensing/intuitive: sensing learners prefer facts and examples, while intuitive learners often develop a preference for theories and abstract.



Visual/verbal: visual learners are good at remembering pictures and videos, whereas verbal learners will better memorize a read text or something heard from a teacher.



Active/reflective: active learners like to try everything themselves and are an asset to working groups, while reflective learners learn most effectively alone or together with one other person only.



Sequential/global: sequential learners prefer linear and logically arranged learning materials, whereas global learners prefer getting a general overview at first, then going deeper into the subject of interest.

Design of Experiment Horticulture Science students from two universities (Leibniz Universität Hannover, Technische Universität München) and two universities of applied sciences (Hochschule Osnabrück, Hochschule Weihenstephan‐Triesdorf) participated together in a blended learning course with the topic “product safety and quality management”. The course started as a project within WeGa Kompetenznetz Gartenbau – the horticultural research network in Germany. Three elements were parts of the online course: online lessons, teleteaching with external lecturers and an in‐depth workshop under (offline) classroom conditions. The online lessons had to be processed independently by the students. There were seven lessons, each with a learning objectives test with open questions to complete at the end of the lesson. During the registration process, it was facultative for each participant to complete the ILS questionnaire.

The experiment took place in two academic years in sequence. In the first year of the experiment, 24 students participated on the course, but only 19 of them also took part at the exam (according to examination regulations, it was possible to delay the exam to a later possibility, which couldn’t be considered for this study). The second year of the experiment, we had 41 participants, but not all of them came to the first exam date either, so only the datasets of 33 students are complete. Students were automatically divided into two groups by the system after registration. While the first lesson was non‐adaptive for everyone, lesson 2 to 4 were provided with adaptivity for Group 1 (11 students in first year, 19 students in second year) and non‐adaptive for Group 2 (12 students in first year, 22 students in second year) and vice versa for lessons 5 to 7. In this manner, it should be ensured that no

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student was disadvantaged. Of course, students were informed in detail about the experiment and were asked for their agreement to use their anonymous data for research.

Exam Repetition Experiment In the first year of this study, 8 students of the Leibniz Universität Hannover were additionally asked to repeat the original exam (consisting of catalogue questions and eLearning questions) under examination conditions after six months. For organizational reasons, this repetition was done directly after the exam of another course, which included the same 8 students. The students were informed only a few minutes prior to this repetition exam and had therefore no opportunity for preparations. It is obvious that the results of this repetition had no influence on students’ grades for the online course before.

Data Collection and Analysis Users’ data from the learning management system Moodle were automatically logged in a system‐internal tool and stored in an Excel file. The records consisted of user ID, time, date, action and additional information indicating which page or which learning material was considered in detail. Thus every click on a link with further information was documented.

Examination performance was made up of several parts. For each part, it was noted which grades / how much points the students received: 1.

Learning objective tests: after every lesson, students had to solve an accompanying test online. The tests consisted of several free text questions, which varied from the first to the second year to avoid cheating.

Students could choose the time for processing the test freely. For correction, a single person per lesson was responsible.

2.

eLearning questions: questions of this type were based on the online lessons and previously unknown. In both years of the study, the same questions were asked.

3.

Question catalogue: questions of this type were previously known from a hand‐out with questions for exam preparation. These questions also varied from the first to the second year. Of course, the question catalogue included many more questions than the exam itself. The questions were related to the content of the workshop and the teleteaching events. Therefore, results for this question type could not be influenced by adaptivity and only played a role in the exam repetition experiment.

Questions from the catalogue and eLearning questions together formed the written exam at the end of the course.

The exam started at the same time and day in the different participating institutions. For correction, the exam was divided into equal parts. For every part, a single person was responsible for grading the answers of the students of all institutions. Statistical analysis was performed with t‐test for two‐group comparison (adaptive vs. non‐ adaptive). A pre‐test for normal distribution was conducted in every case. If this pre‐test failed (non‐parametric data), Mann‐Whitney rank sum test was used instead of t‐test. The significance level was set at 5 % (p ≤ 0,05).

Results and Discussion Examination Performances 1) Learning Objective Tests The participants of the first academic year of this study performed generally well in the learning objectives tests (see table 1). The scattering is (with the exception of the non‐adaptive group in lesson 2) relatively low; the average in the adaptive group is 8.7 and in the non‐adaptive group 10.4. The comparison of the two groups in lesson 1 (non‐adaptive for both groups) showed no generell better performance for one of the them in both academic years. It is striking that in three lessons the adaptive group performed better on the average (lesson 2, 4, 5) and in two lessons, the non‐adaptive group had the better performance (lessons 3 and 6). In lesson 7, the performance of both groups was more or less equally good. Only in lesson 4, the difference between the

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adaptive and the non‐adaptive group is statistically significant (p = 0.024); so the adaptive group archieved a 10 % higher score in mean than the non‐adaptive group. Participants of the second academic year of this study also performed well in the learning objective tests (see also table 1). In lessons 4, 5, 6 and 7, the adaptive group performed better in the tests than the non‐adaptive group. For lesson 6 (p = 0.041, adaptive group archieved a 7.8 % higher score) and lesson 7 (p = <0.001, adaptive group archieved a 12.9 % higher score), this difference is also statistically significant. In lesson 2 and lesson 3, the non‐adaptive group had a slightly better average than the adaptive group. However, these differences are not statistically significant.

TABLE 1: STUDENTS PERFORMANCE IN THE LEARNING OBJECTIVE TESTS

Archieved score

Adaptive

Lesson

Mean

Non‐adaptive SD

Mean

SD

N Adaptive/ Non‐ adaptive

p‐value

Year 1

1

88,4 %

10,4 %

86,1 %

4,2 %

0,212

11/12

2

88,6 %

8,1 %

76,0 %

25,5 %

0,557

11/12

3

94,1 %

7,35 %

96,6 %

4,8 %

0,652

11/12

4

93,2 %

8,5 %

83,2 %

10,6 %

0,024

11/12

5

91,3 %

7,3 %

85,7 %

13,4 %

0,474

12/11

6

83,9 %

8,5 %

86,8 %

4,5 %

0,319

12/11 12/11

7

81,6 %

11,1 %

81,6 %

9,7 %

1,000

Year 2

1

88,2 %

10,4 %

90,5 %

7,3 %

0,560

19/22

2

83,4 %

18,2 %

84,7 %

14,7 %

1,000

19/22

3

93,6 %

8,7 %

95,9 %

5,4 %

0,599

18/22

4

86,2 %

8,7 %

82,5 %

11,7 %

0,266

19/22

5

92,5 %

9,0 %

85,7 %

15,3 %

0,209

22/19

6

84,1 %

10,6 %

76,3 %

13,0 %

0,041

22/19

7

81,3 %

12,3 %

68,4 %

10,1 %

<0,001

21/19

There is an overall tendency that students who have worked through a lesson adaptively more often benefit from the adaptivity than it was a disadvantage for them. In this context, the results of Graf et al. (2014) are also of interest: users’ data analyses of the learning objective tests from the first year of this study came to the conclusion that students in adaptive lessons received higher grades than in non‐adaptive lessons “while spending a similar amount of time on both”. Moreover, students on average received the same grades “spending less time on adaptive lessons than on non‐adaptive ones”.

Results of the learning objective tests might turn out more clear if it were possible to exclude that students from the adaptive group unwittingly cooperated in answering the questions with students from the non‐adaptive group (formation of small learning groups). Furthermore, the seven lessons were differently suitable for the creation of adaptive elements regarding their topic, so the quality of this elements was not the same for all lessons. And last but not least, the effect of adaptivity on students performance may also depend on the choice of appropriate test questions.

2) Elearning Questions As table 2 shows, the students of the first year of the study, who worked through the question related online lesson adaptively, performed better on the average than students who worked non‐adaptively through it.

However, scattering is very big in both groups in all three questions so that it is not surprising that no statistically significant difference can be found between the adaptive and the non‐adaptive group. table 2 also shows how the students in second year of this study have performed. While in question 1 the non‐adaptive group has performed even a little better, in questions 2 and 3 the adaptive group performed better. Despite the large scattering the difference between the two groups in question 2 is also significant (p = 0.005, adaptive group archieved a 29 % higher score).

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TABLE 2: STUDENTS PERFORMANCE IN QUESTIONS OF THE TYPE E‐LEARNING

Archieved score

Adaptive

Non‐adaptive

N Adaptive/ Non‐ adaptive

Question

Mean

SD

Mean

SD

p‐ value

Year 1

1

72,2 %

26,4 %

65,0 %

25,4 %

0,551

9/10

2

92,5 %

25,0 %

91,7 %

16,9 %

0,748

10/9 9/10

3

22,0 %

21,1 %

17,8 %

27,4 %

0,862

Year 2

1

76,9 %

21,0 %

78,4 %

19,6 %

0,727

13/20

2

94,4 %

14,4 %

65,4 %

37,6 %

0,005

20/13

3

36,5 %

31,0 %

24,6 %

28,5%

0,251

13/20

Unfortunately, the results in terms of the positive impact of adaptivity on examination performance and learning success are not unique. A problem for the statistical analysis of the e‐learning questions is undoubtedly the enormous scattering. A more differentiated grading might have led to more significant results. However, due to the frequent occurrence of the extremes ʺmissing answerʺ or ʺexcellent answerʺ, this is impossible. Future attempts should therefore focus on a more careful design of suitable questions. Arnold et al. (2015) gave an excellent overview about the particularities of exam situations and the measurement of learning success in the context of eLearning.

Exam Repetition As can be seen in table 3, at exam questions from the known question catalogue students obtained significantly fewer points in the unannounced repetition exam than in the announced original exam. This difference is statistically highly significant (p ≤ 0,001, archieved score was 41.4 % higher in the original exam). The difference the students achieved in the eLearning questions (which based on the online lessons) at original exam and repetition exam was not significant (49 % to 37.5 %; p = 0,239). Generally, students performend better in questions from the catalogue than in questions of the eLearning type in the original exam. In the repetition exam, this difference was minor only.

TABLE 3: STUDENTS PERFORMANCE IN THE EXAM REPETITION EXPERIMENT

Archieved score

Original exam Mean

SD

Repetition exam Mean

SD

p‐value

N

Catalogue

90,6 %

7,6 %

49,2 %

14,9 %

<0,001

8

eLearning

49,0 %

21,5 %

37,5 %

15,3 %

0,239

8

Results indicate that memorized knowledge (question catalogue) is not as long remembered as the actively acquired knowledge of the online lessons (eLearning questions). Those students who were able to answer the eLearning questions in the first round, could still answer them in the second. The correct answer to one or another slightly tricky catalogue question seems eventually to disappear from the students memory. In our opinion, the result of this experiment unmasks the common practice of handing out a question catalogue for exam preparation as an absurdity, because the associated long‐term learning effect is likely to be marginal. Instead, the focus of teaching should be on active knowledge acquisition. Whether this takes place via eLearning or in classroom teaching is of minor importance.

Conclusions In the issue of adaptivity, it is doubtful whether the higher expense for the preparation of appropriate additional learning materials is in proportion to the benefit the students draw from them. This question is not easy to answer.

Generally, this study showes that students really can benefit from a learning style adaptive learning system and from eLearning in general (long‐term learning effect). Adaptive learning content seems to allow a faster and more

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effective acquisition of knowledge and thereby has a positive impact on individual learning success. However, whether this is in proportion to the extra effort is another question and may be judged by the reader.

ACKNOWLEDGMENT

The authors acknowledge the support of the Federal Ministry for Education and Research (BMBF) of the Federal Republic of Germany, the Ministry of Science and Culture of Lower Saxony, the Ministry of Infrastructure and Agriculture of Brandenburg and the Ministry of Science, Research and Art of Bavaria. In addition, authors also thank Dr. Sabine Graf at Athabasca University for providing the adaptive mechanism.

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