Paper id 26201451

International Journal of Research in Advent Technology, Vol.2, No.6, June 2014 E-ISSN: 2321-9637

Performance Analysis of Primary School Students Using Data Mining Techniques Manmohan Singh1, Dr. Anjali Sant2 1

2

Research Scholar , HOD Department of Mathematics 1 Department of Computer Science, Mewar University Rajasthan –India 2 Bhopal Institute of Tech. and Science, Bhopal-India

Abstract- Educational Datamining (EDM) is an emerging dicipline, concerned with developing methods for exploring the unique types of data that come from educational system and using those methods to better understand students, and the system which they learn in. The performance analysis of a student is a great concern to the primary education managements. The main objective of this study is to model the primary school student academic performance using data mining technique. In the present investigation, a survey cum experimental methodology was adopted to generate a database and it was constructed from a primary and a secondary source. In this paper, we design or develop a classification model from the training data of students and then test that model from the existing student data. This model may be useful for taking decision to teachers and students for improving the performance of students.

Keywords- Educational Data Mining, classification model, student performance.

1. INTRODUCTION Estimation of academic performance of students is challenging since students’ academic performance based on some factors like personal, socio-economic, and psychological and other environmental variables.

The aim of this paper is to calculate the student marks and what are the factors that influence the performance of the students.

Education has always been important but perhaps never more so in man's history than today, Research suggests that pre-primary education is very important for the development of young children before they enter formal school (Kaul, 2002). It helps in cognitive development of children at the early grades of primary education and it has strong b earring on attendance and participation of children once they enter primary school. Pre-primary education is considered to be very important for the child as it is the first step towards entering the world of knowledge as well as a healthy and purposeful life. Pre-primary education helps children become more independent and confident as well as promoting the all-round development of the children(Ramachandran et al., 2003).Children who have been to pre-primary school stand to learn more rapidly through an organized curriculum, learning aids and by interacting with other children the main purpose of pre-primary education is to prepare children physically, emotionally, socially and mentally for formal schooling and to prevent poor performance and early drop out. It also helps Older children, particularly girls, to attend their schools making them free from responsibility of sibling care. Thus it can be said that preprimary education is Necessary for all children of 3-6 years

old irrespective of their socio-economic background (Ernesto Pathros Ibarra García, Pablo Medina Mora, 201)1With increasing numbers of nuclear families and a lack of family support, preprimary school education is gaining importance. Availability of quality preprimary education will promote inclusive education and meaningful access to school education by increasing enrolment and reducing the vulnerability of children.

This study is more useful for identifying weak students and the identified students can be individually assisted by the educators so that their performance is better in future. This study investigates the accuracy of some classification techniques for analyzing performance of a student.

In Betul district is chosen for the study because district is comprises of Tribal as well as nontribal blocks. According to the study conducted by researchers in the Malaysian environment, it is indicated that the Data mining techniques are widely used in primary educational system in order to increase the effectiveness and efficiency of the traditional method and as a guideline to improve their decision making processes.

The main objectives of this study are: • To examine performance in Primary Examinations in the region for the last and to map out a trend and possible influences.

• To provide descriptive correlates identified factors and poor performance.

Leaving 34 years “driver” between

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International Journal of Research in Advent Technology, Vol.2, No.6, June 2014 E-ISSN: 2321-9637 • To examine findings for critical factors upon which to provide recommendations for Intervention and further investigations.

• To mine the clean dataset using data mining techniques.

• To design or develop a classification model create a classification model from the training data of students and then test those model from the existing student data.

• This model may be useful for taking decision to teachers and students for improving the performance of students.

2. LITERATURE REVIEW All the developing countries are facing acute problem of UEE. Woman education in general and education of woman of S.T., S.C. Classes of remote rural areas is a real challenge. Some studies have been conducted (Jayaraman, Simroth and Vericourt 2010) Intake of school going children in rural Madhya Pradesh, by 50% to 100%.

Using a difference in-difference estimation strategy that relies on a staggered roll out across schools, attendance rates for girls are estimated to increase by 12 percentage points in rural Madhya Pradesh (Afridi, 2010) and 5 percentage points overall in Delhi (Afridi, Barooah and Somanathan, 2010). However, these papers do not study the impact of mid-day meals on test scores. Use data from thirteen states to construct triple-difference estimates using private schools as a control group and find that the mid-day meal program is associated with a 6.8% increase in enrolment, but had no impact on test scores.

[Brijesh Kumar Bhardwaj and Saurabh Pal, 2011] Described that the hidden patterns, associations and anomalies which are discovered by some Data mining techniques can be used to improve the effectiveness, efficiency and the speed of the processes. As a result this improvement may bring a lot of advantages to the higher educational system such as maximizing educational system efficiency, decreasing student’s drop-out rate, increasing educational improvement ratio, increasing student’s success and reducing the cost of system processes. In order to achieve the above quality improvement, we need a Data Mining system that can provide the needed knowledge and insights for the decision makers in the higher education system. The main objective of higher education institutions is to provide quality education to its students. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of

regarding non enrolment of girls, low enrolment and low retention of girls, dropout among girls at primary stage, dropouts of Scheduled Castes/Tribes etc. and many others. All these studies have been conducted in different parts of our country by the participants of Diploma in Educational Planning and Administration organized by National Institute of Educational Planning and Administration, New Delhi. Some prestigious institutions like NCERT, NIEPA and UNESCO have also conducted studies in this field.

A number of reviews pertaining to not only the diverse factors like personal, socio-economic, psychological and other environmental variables that influence the performance of students but also the models that have been used for the performance prediction are available in the literature and a few specific studies are listed below for reference.

abnormal values in the result sheets of the students, prediction about students’ performance.

(J. A. Moriana, F. Alos, R. Alcala, M. J. Pino, J. Herruzo, and R. Ruiz, 2006) present experimental evidence on the impact of a school grant program that stipulated that the funds should be spent on inputs directly used by students.

The program was implemented over two years in the major categories of spending were books, stationery, and writing materials (~50%); workbooks and practice books (~20%); and classroom materials (~25%) with similar patterns of expenditure in both years of the program.

They find that this program had significant positive impact on student test scores at the end of the first year, but that the impact in the second year Was close to zero, with the cumulative two-year effect being positive but not significant. They show the most likely mechanisms of this result is that households considerably reduce their own spending on their child's education in the second year of the program A global calculation of Wastage and stagnation indicates that of every 100 children who are admitted into grade 1, about 45 reach grade IV and 37 reach grades V. Relatively larger number of girls' dropout and more than two-thirds dropout before reaching grade II. The Indian scene is on less disquieting. It was admitted only recently in parliament that 60 % of all children enrolled in primary school dropout by the time they reach class V. It is an accepted fact that the problem of dropouts has been the single largest factor which adversely affects the programmes of U.E.E. (Osman N. Darcan and Bertan Y. Badur, 2012) study the impact of a program Implemented by Parham in partnership with the state governments of Uttarakhand and Bihar that attempted to scale up remedial instruction in public schools, and find that summer camps conducted by regular teachers

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International Journal of Research in Advent Technology, Vol.2, No.6, June 2014 E-ISSN: 2321-9637 transacting the learning-appropriate remedial materials were effective in raising test scores.

However, they find that there was no impact of other models that attempted to incorporate this pedagogy in the regular school day. The authors interpret their findings as suggesting that the remedial pedagogy was successful, but that it was difficult to get teachers to implement new curriculums during school hours.

3. METHODOLOGY By formal search of the literature and discussion with experts on student performance, a number of factors that are considered to have influence on the performance of a student were identified. These influencing factors were categorized as input variables. The output variables on the other hand represent some possible grades. The primary data were collected from the primary school students and the secondary data were collected from the schools and from internet.

The primary data was collected using a questionnaire which includes questions related to several personal, socioeconomic, psychological and school related variables that were expected to affect student performance. The questionnaire was reviewed by the professionals and tested on a small set of 50 students in order to get a feedback. The final version contained 50 questions and it was answered by more than 1000 students. Latter a sample of 500 were selected from the whole. All 500 questionnaires were filled with the response rate of 100% out of which 315 were females and 185 were males. a.

District Specific Features Affecting Education According to District specific features (like population, weather, economic background, etc.) we analyze which features affect the education are enumerated here under block wise literacy shown in Table 1.

For this study, recent real world data were collected from primary school students. Ten schools were randomly selected from Betul district in state of M.P. A sample of 1000 students was taken from a group of schools. Students were grouped in a classroom where they were briefed clearly about the questionnaire and it took on average one an hour to fill the questionnaire. Selection of students was at random.

Block Name

Total

Table1. Block wise literacy Literacy Rate Urban Literates

Rural Literates

Literates

Male

Female

Total

Male

Female

Male

Female

Betul

217290

57.30

42.70

64.45

78605

61124

45895

31666

Chicholi

35519

60.51

39.49

48.09

0

0

21493

14026

Ghodadongri

129436

51.16

48.84

66.16

35877

20585

30579

42394

Bhainsdehi

61446

59.43

40.57

52.63

6161

4776

30359

20150

Athner

51066

58.96

41.04

54.99

0

0

30108

20958

Bhimpur

45228

63.47

36.53

36.98

0

0

28708

16520

Shahpur

38963

64.94

35.06

40.62

0

0

25302

13661

Multai

94006

57.22

42.78

65.29

8720

7005

45070

33211

Prabhatpattan

76628

57.03

42.97

62.57

0

0

43704

32924

Amla

92784

58.79

41.21

57.42

12581

9530

41965

28708

Overall District

842366

58.88

41.12

54.92

141944

103020

343183

254218

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International Journal of Research in Advent Technology, Vol.2, No.6, June 2014 E-ISSN: 2321-9637 3.2 Description of Preschool student dataset Database has collected by filling the questionnaires by concerning student or teacher or student parent.

The survey was designed to gather information pertaining to the perceived educational status of

parents and demographic information of student such as name, address, age, sex, education. Table 2 shows the description of each attributes of database.

Table 2. Description of datasets S. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Attribute name School_Code Name_Place Name_Block District Scholer_Number Name_Student Student_Father_Name Student_Mother_Name Age Sex Class Category School_type Location_School No_teachers family_size

17 18 19 20 21 22 23 24 25

Living_zone Father_edu Father_occup Mother_edu Mother_occu Family_income Private_tuision Attendence_School Previous_result

26

Grade_Previous_Result

4. TOOLS AND TECHNIQUES The information gain with respect to a set of examples is the expected reduction in entropy that results from splitting a set of examples using the values of that attribute. This measure is used in Decision Tree induction and is useful for identifying those attributes that have the greatest influence on classification. The aim of data preprocessing is to improve the quality of the data which will help in improving “the accuracy and efficiency of the subsequent mining process” (Han and Kamber 2007). Often, outliers decrease the accuracy and efficiency of the models. Data preprocessing allows transforming the original data into a suitable shape to be used by a particular mining algorithm. So, before applying the data mining algorithm, a number of general data preprocessing tasks have to be addressed (V.Ramesh, at all 2011,). Normally in data mining process preprocessing is one of the

Description School dice code Place of school Name of block Batul (M.P.) Student scholar number Name of student Student father name Mother’s name Age of student (06-10 years) Gender (M, F) (III, IV, V) Category (SC, ST, Gen, OBC) (Govt., Private) Rural of Urban Number of Teacher’s in school Number of members of in a student family.

Residential area of student Father’s Education Occupation of student father Mother; Education Occupation of Student’s Mother Family income Are student take private tusion?

Attendence of student’s in a class Previous year result of student in Percentage.

Previous year result of student

important stages where relevant data’s are grouped and cleaned, this can be done with any of the classification algorithms and in this study we take J48 classifier with the help of WEKA software.

In the data preparation phase we selected the relevant attributes from the available data, created meaningful groups within the attributes and derived new attributes from our knowledge of the domain.

The next step is to build the classification model using the decision tree method. The decision tree is a very good and practical method since it is relatively fast and can be easily converted to simple classification rules. The decision tree method depends mainly on using the information gain metric which determines the attribute that is most useful. The information gain depends on the entropy measure.

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International Journal of Research in Advent Technology, Vol.2, No.6, June 2014 E-ISSN: 2321-9637

Fig. 1 View of class attributes

5. CONCLUSION In this paper, the classification task is used on student database to predict the students division on the basis of previous database. To mine the clean dataset, we are using data mining techniques here. For designing a classification model we are using dataset [Table 2] of primary school student and then test that model from the existing student data. This model may be

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