As this academic performance is influenced by many factors, it is essential to develop predictive data mining model for students’ performance so as to identify the slow learners and study the influence of the dominant factors on their academic performance. In the present investigation, a survey cum experimental methodology was adopted to generate a database and it was constructed from a primary. While the primary data was collected from the regular students and irregular student the secondary data was gathered from the school in class 3, 4 and 5 a total of 1000 datasets of the 2014 year from five different schools in three different districts of BETUL state Madhya Pradesh were collected. The raw data was preprocessed in terms of filling up missing values, transforming values in one form into another and relevant attribute/ variable selection. As a result, we had 700 student records, which were used for primary school prediction model construction. A set of prediction rules were extracted from primary school prediction model and the efficiency of the generated student prediction model was found. The accuracy of the present model was compared with other model and it has been found to be satisfactory

Sant, Anjali

Singh, Manmohan

English

Advanced Research Journals

  
 
 Binary Journal of Data Mining & Networking 4 (2014) 10-13 
http://www.arjournals.org/index.php/bjdmn/index 
 
Research Article 
             
Betul Districts Primary School Performance Prediction Model Using Data 
Mining 
Manmohan Singh1, Anjali Sant2 
 
*Corresponding author: 
 
Manmohan Singh 
 
1Research Scholar, Mewar University, 
Chittorgarh, Rajasthan 
2Prof. and HOD, Dept of Mathematics, 
BIST, Bhopal 
 
 
 
 
 
 
A b s t r a c t  
As this academic performance is influenced by many factors, it is essential to develop predictive 
data mining model for studentsÊ performance so as to identify the slow learners and study the 
influence of the dominant factors on their academic performance. In the present investigation, a 
survey cum experimental methodology was adopted to generate a database and it was constructed 
from a primary. While the primary data was collected from the regular students and irregular student 
the secondary data was gathered from the school in class 3, 4 and 5 a total of 1000 datasets of the 
2014 year from five different schools in three different districts of BETUL state Madhya Pradesh 
were collected. The raw data was preprocessed in terms of filling up missing values, transforming 
values in one form into another and relevant attribute/ variable selection. As a result, we had 700 
student records, which were used for primary school prediction model construction. A set of 
prediction rules were extracted from primary school prediction model and the efficiency of the 
generated student prediction model was found. The accuracy of the present model was compared 
with other model and it has been found to be satisfactory. 
Keywords: student performance, Decision Tree, Data Mining, WEKA 
Introduction 
In the present investigation Betul district in M.P is chosen for the 
study because district is comprises of Tribal as well as nontribal 
blocks. There is vast difference in culture of tribal and nontribal 
blocks in a district. The present study reveals that the drop out at 
primary level especially among socially backward and economically 
poor people is due to their peculiar type of culture, which hinders 
educational processes in tribal society of tribal blocks. The 
government has opened some more schools and EGS under SSA 
norms in remote, rural and tribal villages in district so that 
education can be the teachers remain usually absent from their 
schools for a long period and the situation becomes deteriorated 
and suffer student,  
Generation of a data source of predictive variables,  
Identification of highly influencing    predictive Variables on the 
academic performance of higher secondary students 
Construction of a primary school prediction model   in data mining 
on the basis of identified predictive variables. 
Previous Studies 
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. 
Walters and Soyibo [1] conducted a study to determine Jamaican 
high school students' (population n=305) level of performance on 
five integrated science process skills with performance linked to 
gender, grade level, school location, school type, student type, and 
socio-economic background (SEB). The results revealed that there 
was a positive significant relationship between academic 
performance of the student and the nature of the school. 
Khan [2] conducted a performance study on 400 students 
comprising 200 boys and 200 girls selected from the senior 
secondary school of Aligarh Muslim University, Aligarh, India with a 
main objective to establish the prognostic value of different 
measures of cognition, personality and demographic variables for 
success at higher secondary level in science stream. The selection 
was based on cluster sampling technique in which the entire 
population of interest was divided into groups, or clusters, and a 
random sample of these clusters was selected for further analyses. 
It was found that girls with high socio-economic status had 
relatively higher academic achievement in science stream and 
boys with low socio-economic status had relatively higher 
academic achievement in general. 
Hijazi and Naqvi [3] conducted as study on the student 
performance by selecting a sample of 300 students (225 males, 75 
females) from a group of colleges affiliated to Punjab university of 
Pakistan. The hypothesis that was stated as "Student's attitude 
towards attendance in class, hours spent in study on daily basis 
after college, students' family income, students' mother's age and 
mother's education are significantly related with student 
performance" was framed. By means of simple linear regression 
ISSN: 2229 -7170
  
This work is licensed under a Creative Commons Attribution 3.0 License.  
 Singh et al. Binary Journal of Data Mining & Networking 4 (1) 10-13 [2014] 
 
PAGE | 11 |
 
 
analysis, it was found that the factors like motherÊs education and 
studentÊs family income were highly correlated with the student 
academic performance. 
Kristjansson, Sigfusdottir and Allegrante [7] made a study to 
estimate the relationship between health behaviors, body mass 
index (BMI), self-esteem and the academic achievement of 
adolescents. The authors analyzed survey data related to 6,346 
adolescents in Iceland and it was found that the factors like lower 
BMI, physical activity, 
and good dietary habits were well associated with higher academic 
achievement. 
Moriana et al. [8] studied the possible influence of extra-curricular 
activities like study-related (tutoring or private classes, computers) 
and/or sports-related (indoor and outdoor games) on the academic 
performance of the secondary school students in Spain. A total 
number of 222 students from 12 different schools were the 
samples and they were categorized into two groups as a function 
of student activities (both sports and academic) outside the school 
day. Analysis of variance (ANOVA) was used to verify the effect of 
extracurricular actives on the academic performance and it was 
observed that group involved in activities outside the school yielded 
better academic performance. 
Bray [9], in his study on private tutoring and its implications, 
observed that the percentage of students receiving private tutoring 
in India was relatively higher than in Malaysia, Singapore, Japan, 
China and Srilanka. It was also observed that there was an 
enhancement of academic performance with the intensity of private 
tutoring and this variation of intensity of private tutoring depends on 
the collective factor namely socio-economic conditions. 
Modeling of student performance at various levels is discussed in 
[4], Ma, Liu, Wong, Yu, and Lee [4] applied a data mining 
technique based on association rules to find weak tertiary school 
students (n= 264) of Singapore for remedial classes. Three scoring 
measures namely Scoring Based on Associations (SBA-score), 
C4.5-score and NB- score for evaluating the prediction in 
connection with the selection of the students for remedial classes 
were used with the input variables like sex, region and school 
performance over the past years. It was found that the predictive 
accuracy of SBA-score methodology was 20% higher than that of 
C4.5 score, NB-score methods and traditional method. 
Kotsiantis, et al. [5] applied five classification algorithms namely 
Decision Trees, Perceptron-based Learning, Bayesian Nets, 
Instance-Based Learning and Rule-learning to predict the 
performance of computer science students from distance learning 
stream of Hellenic Open University, Greece. A total of 365 student 
records comprising several demographic variables like sex, age 
and marital status were used. In addition, the performance attribute 
namely mark in a given assignment was used as input to a binary 
(pass/fail) classifier. Filter based variable selection technique was 
used to select highly influencing variables and all the above five 
classification models were constructed. It was noticed that the 
Naïve-Bayes algoritm. 
Al-Radaideh, et al. [10] applied a decision tree model to predict the 
final grade of students who studied the C++ course in Yarmouk 
University, Jordan in the year 2005. They used 12 predictive 
variables and a 4-class response variable for the model 
construction. Three different classification methods namely ID3, 
C4.5, and the Naïve Bayes were used. The outcome of their results 
indicated that Decision Tree model had better prediction than other 
models with the predictive accuracy of 38.33% for four-class 
response variable. 
Cortez and Silva [6] attempted to predict failure in the two core 
classes (Mathematics and Portuguese) of two secondary school 
students from the Alentejo region of Portugal by utilizing 29 
predictive variables. Four data mining algorithms such as Decision 
Tree (DT), Random Forest (RF), Neural Network (NN) and Support 
Vector Machine (SVM) were applied on a data set of 788 students, 
who appeared in 2006 examination. It was reported that DT and 
NN algorithms had the predictive accuracy of 93% and 91% for 
two-class dataset (pass/fail) respectively. It was also reported that 
both DT and NN algorithms had the predictive accuracy of 72% for 
a four-class dataset. 
From these specific studies, we observe that the student 
performance could depend on diversified factors such as 
demographic, academic, psychological, socio-economic and other 
environmental factors. It was learnt that the predictive accuracy of 
constructed models ranged between 38.33 to 93%. The variation in 
the predictive accuracy could be correlated with the nature of 
student data set and utilization of number of records, predictive 
variables and class values of response variable.  
Methodology 
All the needed information was available in the school head master 
or self by student and its parents in form of through Questionnaire. 
Information not to be collected from the District Education office or 
the using web side and district project office, specially designed 
questionnaire were used in collecting information and data of 
student . Accuracy and Efficiency.  
Classifier accuracy: predicting class label to be. 
Predictor accuracy: guessing value of predicted attributes and its 
field. 
Speed or Time. 
Time to construct the model or training time. 
Time to use the model by using classification or prediction time. 
Robustness: handling noise and missing values of data. 
Scalability: efficiency in disk-resident databases of student. 
Interpretability: Understanding and insight provided by the model of 
real. 
Survey cum experimental methodology was adopted to generate a 
database for the present study. The basis of this database was 
from the primary and secondary sources. While the primary data 
was collected from the regular students, the secondary data was 
gathered from the school office. 
Data Source 
A detailed questionnaire was prepared with the reference, 
assistance and guidance from the (i) Review of related literature, 
 Singh et al. Binary Journal of Data Mining & Networking 4 (1) 10-13 [2014] 
 
PAGE | 12 |
 
 
(ii) Teachers of diversified schools, (iii) Parents of primary school 
students, (iv) Educational experts in school, colleges and 
Universities. The designed four page A4 size close-ended 
questionnaire was used for the collection of student details. Most of 
the information of the variables was collected directly from the 
students through this questionnaire. Based on this information, a 
few derived variables were generated. While some of the 
information for the variables was extracted from the school records, 
the mark details were collected from thee education office. All the 
predictor and response variables which were derived from the 
questionnaire are given in Table 1 for reference. 
 
Table 1 data Source 
S. No. Attribute name Description 
1 School_Code School dice code 
2 Name_Place Place of school 
3 Name_Block Name of block (three blocks of Batul District)) 
4 District Batul (M.P.) 
5 Scholer_Number Student scholar number 
6 Name_Student Name of student 
7 Student_Father_Name Student father name 
8 Student_Mother_Name MotherÊs name 
9 Age Age of student (06-10 years) 
10 Sex Gender (M, F) 
11 Class (III, IV, V) 
12 Category Category (SC, ST, Gen, OBC) 
13 School_type (Govt., Private) 
14 Location_School Rural of Urban 
15 No_teachers Number of TeacherÊs in school 
16 family_size Number of members of in a student family 
17 Living_zone Residential area of student (Rural, Urban) 
18 Father_edu FatherÊs Education 
19 Father_occup Occupation of student father 
20 Mother_edu Mother; Education 
21 Mother_occu Occupation of StudentÊs Mother 
22 Family_income Family income 
23 Private_tuision Are student take private tusion? 
24 Attendence_School Attendence of studentÊs in a class 
25 Previous_result Previous year result of student in percentage 
26 Grade_Previous_Result Previous year result of student in Grad                
 
Tools and Techniques 
Classification trees are widely used in different fields such as 
medicine, computer science, botany and psychology [11]. These 
trees readily lend themselves to being displayed graphically, 
helping to make them easier to interpret than they would be if only 
a strict numerical interpretation were possible. 
Primary school student [12], which is one of the classification tree 
algorithms, is the name given to one version of the Automatic 
Interaction Detector that has been developed for categorical 
variables. In fact, student is a technique that recursively partitions 
(or splits) a population into separate and distinct segments. These 
segments, called nodes, are split in such a way that the variation of 
the response variable (categorical) is minimized within the 
segments and maximized among the segments. After the initial 
splitting of the population into two or more nodes (defined by 
values of an independent or predictor variable), the splitting 
process is repeated on each of the. 
Conclusion 
We used students' data from database course of betul Distinct. We 
collected all available data including their usage of Model learning 
facility of education. We applied determining techniques to discover 
knowledge. Every I discovered Classification rules based and we 
sorted the rules using lift metric then we visualized than generated 
different rules. The  we discovered classification rules using 
decision tree. Experiments could be done using more data mining 
techniques such as n ural ets, Classification rules, genetic 
algorithms, nearest Neighbor, Naive Bayes, support vector 
machines and others. The used preprocess and data mining 
algorithms could be embedded into learning system so that anyone 
using the system c n benefit from Classification rules the data 
mining techniques. 
 
 
 
 
 
 
 
References 
[1]. Walters YB, and Soyibo K. „An 
Analysis of High School Students' 
Performance on Five Integrated 
Science Process Skills‰, Research in 
Science & Technical Education, 
2001;19(2):133 ă 145.  
[2]. Khan ZN. „Scholastic Achievement of 
Higher Secondary Students in Science 
Stream‰, Journal of Social 
Sciences,2005; 1 (2):84-87.  
[3]. Hijazi ST, and Naqvi RSMM. „Factors 
Affecting StudentÊs Performance: A 
Case of Private Colleges‰, Bangladesh 
e-Journal of Sociology, 2006;3(1).  
[4]. Ma Y, Liu B, Wong CK, Yu PS, and 
Lee SM. „Targeting the Right Students 
Using Data Mining‰, Proceedings of 
KDD, International Conference on 
Knowledge discovery and Data Mining, 
Boston, USA, 2000, 457-464.  
[5]. Kotsiantis S, Pierrakeas C, and 
Pintelas P. „Prediction of StudentÊs 
Performance in Distance Learning 
Using Machine Learning Techniques‰, 
Applied Artificial Intelligence, 2004;18,  
(5):411-426. IJCSI International. 
[6]. Cortez P, and Silva A. „Using Data 
Mining To Predict Secondary School 
Student Performance‰, In EUROSIS, A. 
Brito and J. Teixeira (Eds.), 2008, 5-12.  
 Singh et al. Binary Journal of Data Mining & Networking 4 (1) 10-13 [2014] 
 
PAGE | 13 |
 
 
[7]. Kristjansson A L, Sigfusdottir I G, and 
Allegrante JP. „Health Behavior and 
Academic Achievement Among 
Adolescents: The Relative Contribution 
of Dietary Habits, Physical Activity, 
Body Mass Index, and Self-Esteem‰, 
Health Education & Behavior, (In 
Press).  
[8]. Moriana JA, Alos F, Alcala R, Pino M J 
J. Academic Performance in 
Secondary Students‰, Electronic 
Journal of Research in Educational 
Psychology,2006;4(1):35-46. 
[9]. Bray M. The Shadow Education 
System: Private Tutoring And Its 
Implications For Planners, (2nd ed.), 
UNESCO, PARIS, France, 2007.  
[10]. AI-Radaideh Q A, AI-Shawakfa E M, 
and AI-Najjar MI. „Mining Student Data 
using Decision Trees‰, International 
Arab Conference on Information 
Technology(ACIT'2006), Yarmouk 
University, Jordan, 2006.  
[11]. Camdeviren HA, Yazici AC, Akkus Z, 
Bugdayci R, and Sungur MA. 
„Comparison of Logistic Regression 
Model and Classification Tree: An 
Application to Postpartum Depression 
Data‰, Expert Systems with 
Applications, 2007; 32(4): 987-994.  
[12]. Kass GV. „An Exploratory Technique 
for Investigating Large Quantities of 
Categorical Data‰, Applied Statistic, 
1980; 29:119-127.  
[13]. Witten IH, and Frank E. Data Mining ă 
Practical Machine Learning Tools and 
Techniques (2
nd 
ed.), San Francisco, 
CA: Morgan Kaufmann Publisher, 
2005.  
[14]. Liu H, and Setiono R. „Chi-square: 
Feature Selection and Discretization of 
Numeric Attributes‰, Proceedings of 
IEEE 7th International Conference on 
Tools with Artificial Intelligence, 1995. 
338,  (391)  
[15]. Ganesh S. „Data Mining: Should it be 
Included in the Statistics Curriculum?‰ 
The 6th international conference on 
teaching statistics (ICOTS-6), Cape 
Town, South Africa, 2002.  
[16]. Ramasamy K. „Mother Tongue and 
Medium of Instruction ă A Continuing 
Battle‰, Language in India, Vol. 1, No. 
6, 
[17]. Orlando S, Palmerini P, and Perego R. 
Enhancing the Apriori Algorithm for 
Frequent Set Counting. Proceedings of 
3rd International Conference on Data 
Warehousing and Knowledge 
Discovery. 2001. 
[18]. Grahne G,  Lakshmanan  L,  and Wang  
X.  2000. Efficient mining of 
constrained correlated sets. In Proc. 
2000. Int. Conf. Data Engineering 
(ICDEÊ00), San Diego, CA, pp. 512ă
521.  
[19]. Dong G, and Li J. 1999. Efficient 
mining of emerging patterns: 
Discovering trends and differences. In 
Proc. 1999  Int. Conf. Knowledge 
Discovery and Data Mining (KDDÊ99), 
San Diego, CA, pp. 43ă52. 
[20]. Bayardo RJ. 1998. Efficiently mining 
long patterns from databases. In Proc. 
1998 ACM-SIGMOD Int. Conf. 
Management of Data (SIGMODÊ98), 
Seattle, WA, pp. 85ă93. 
[21]. Cheung D W L, Lee S D, and Kao B. "A 
general incremental technique for 
maintaining discovered association 
rules," in Proceedings of the 15th 
International Conference on Database 
Systems for Advanced Applications, 
pp. 185-194, 1997. 
 
 
 


Betul Districts Primary School Performance Prediction Model Using Data Mining

Binary Journal of Data Mining & Networking

  
 
 Binary Journal of Data Mining & Networking 4 (2014) 10-13 
http://www.arjournals.org/index.php/bjdmn/index 
 
Research Article 
             
Betul Districts Primary School Performance Prediction Model Using Data 
Mining 
Manmohan Singh1, Anjali Sant2 
 
*Corresponding author: 
 
Manmohan Singh 
 
1Research Scholar, Mewar University, 
Chittorgarh, Rajasthan 
2Prof. and HOD, Dept of Mathematics, 
BIST, Bhopal 
 
 
 
 
 
 
A b s t r a c t  
As this academic performance is influenced by many factors, it is essential to develop predictive 
data mining model for studentsÊ performance so as to identify the slow learners and study the 
influence of the dominant factors on their academic performance. In the present investigation, a 
survey cum experimental methodology was adopted to generate a database and it was constructed 
from a primary. While the primary data was collected from the regular students and irregular student 
the secondary data was gathered from the school in class 3, 4 and 5 a total of 1000 datasets of the 
2014 year from five different schools in three different districts of BETUL state Madhya Pradesh 
were collected. The raw data was preprocessed in terms of filling up missing values, transforming 
values in one form into another and relevant attribute/ variable selection. As a result, we had 700 
student records, which were used for primary school prediction model construction. A set of 
prediction rules were extracted from primary school prediction model and the efficiency of the 
generated student prediction model was found. The accuracy of the present model was compared 
with other model and it has been found to be satisfactory. 
Keywords: student performance, Decision Tree, Data Mining, WEKA 
Introduction 
In the present investigation Betul district in M.P is chosen for the 
study because district is comprises of Tribal as well as nontribal 
blocks. There is vast difference in culture of tribal and nontribal 
blocks in a district. The present study reveals that the drop out at 
primary level especially among socially backward and economically 
poor people is due to their peculiar type of culture, which hinders 
educational processes in tribal society of tribal blocks. The 
government has opened some more schools and EGS under SSA 
norms in remote, rural and tribal villages in district so that 
education can be the teachers remain usually absent from their 
schools for a long period and the situation becomes deteriorated 
and suffer student,  
Generation of a data source of predictive variables,  
Identification of highly influencing    predictive Variables on the 
academic performance of higher secondary students 
Construction of a primary school prediction model   in data mining 
on the basis of identified predictive variables. 
Previous Studies 
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. 
Walters and Soyibo [1] conducted a study to determine Jamaican 
high school students' (population n=305) level of performance on 
five integrated science process skills with performance linked to 
gender, grade level, school location, school type, student type, and 
socio-economic background (SEB). The results revealed that there 
was a positive significant relationship between academic 
performance of the student and the nature of the school. 
Khan [2] conducted a performance study on 400 students 
comprising 200 boys and 200 girls selected from the senior 
secondary school of Aligarh Muslim University, Aligarh, India with a 
main objective to establish the prognostic value of different 
measures of cognition, personality and demographic variables for 
success at higher secondary level in science stream. The selection 
was based on cluster sampling technique in which the entire 
population of interest was divided into groups, or clusters, and a 
random sample of these clusters was selected for further analyses. 
It was found that girls with high socio-economic status had 
relatively higher academic achievement in science stream and 
boys with low socio-economic status had relatively higher 
academic achievement in general. 
Hijazi and Naqvi [3] conducted as study on the student 
performance by selecting a sample of 300 students (225 males, 75 
females) from a group of colleges affiliated to Punjab university of 
Pakistan. The hypothesis that was stated as "Student's attitude 
towards attendance in class, hours spent in study on daily basis 
after college, students' family income, students' mother's age and 
mother's education are significantly related with student 
performance" was framed. By means of simple linear regression 
ISSN: 2229 -7170
  
This work is licensed under a Creative Commons Attribution 3.0 License.  
 Singh et al. Binary Journal of Data Mining & Networking 4 (1) 10-13 [2014] 
 
PAGE | 11 |
 
 
analysis, it was found that the factors like motherÊs education and 
studentÊs family income were highly correlated with the student 
academic performance. 
Kristjansson, Sigfusdottir and Allegrante [7] made a study to 
estimate the relationship between health behaviors, body mass 
index (BMI), self-esteem and the academic achievement of 
adolescents. The authors analyzed survey data related to 6,346 
adolescents in Iceland and it was found that the factors like lower 
BMI, physical activity, 
and good dietary habits were well associated with higher academic 
achievement. 
Moriana et al. [8] studied the possible influence of extra-curricular 
activities like study-related (tutoring or private classes, computers) 
and/or sports-related (indoor and outdoor games) on the academic 
performance of the secondary school students in Spain. A total 
number of 222 students from 12 different schools were the 
samples and they were categorized into two groups as a function 
of student activities (both sports and academic) outside the school 
day. Analysis of variance (ANOVA) was used to verify the effect of 
extracurricular actives on the academic performance and it was 
observed that group involved in activities outside the school yielded 
better academic performance. 
Bray [9], in his study on private tutoring and its implications, 
observed that the percentage of students receiving private tutoring 
in India was relatively higher than in Malaysia, Singapore, Japan, 
China and Srilanka. It was also observed that there was an 
enhancement of academic performance with the intensity of private 
tutoring and this variation of intensity of private tutoring depends on 
the collective factor namely socio-economic conditions. 
Modeling of student performance at various levels is discussed in 
[4], Ma, Liu, Wong, Yu, and Lee [4] applied a data mining 
technique based on association rules to find weak tertiary school 
students (n= 264) of Singapore for remedial classes. Three scoring 
measures namely Scoring Based on Associations (SBA-score), 
C4.5-score and NB- score for evaluating the prediction in 
connection with the selection of the students for remedial classes 
were used with the input variables like sex, region and school 
performance over the past years. It was found that the predictive 
accuracy of SBA-score methodology was 20% higher than that of 
C4.5 score, NB-score methods and traditional method. 
Kotsiantis, et al. [5] applied five classification algorithms namely 
Decision Trees, Perceptron-based Learning, Bayesian Nets, 
Instance-Based Learning and Rule-learning to predict the 
performance of computer science students from distance learning 
stream of Hellenic Open University, Greece. A total of 365 student 
records comprising several demographic variables like sex, age 
and marital status were used. In addition, the performance attribute 
namely mark in a given assignment was used as input to a binary 
(pass/fail) classifier. Filter based variable selection technique was 
used to select highly influencing variables and all the above five 
classification models were constructed. It was noticed that the 
Naïve-Bayes algoritm. 
Al-Radaideh, et al. [10] applied a decision tree model to predict the 
final grade of students who studied the C++ course in Yarmouk 
University, Jordan in the year 2005. They used 12 predictive 
variables and a 4-class response variable for the model 
construction. Three different classification methods namely ID3, 
C4.5, and the Naïve Bayes were used. The outcome of their results 
indicated that Decision Tree model had better prediction than other 
models with the predictive accuracy of 38.33% for four-class 
response variable. 
Cortez and Silva [6] attempted to predict failure in the two core 
classes (Mathematics and Portuguese) of two secondary school 
students from the Alentejo region of Portugal by utilizing 29 
predictive variables. Four data mining algorithms such as Decision 
Tree (DT), Random Forest (RF), Neural Network (NN) and Support 
Vector Machine (SVM) were applied on a data set of 788 students, 
who appeared in 2006 examination. It was reported that DT and 
NN algorithms had the predictive accuracy of 93% and 91% for 
two-class dataset (pass/fail) respectively. It was also reported that 
both DT and NN algorithms had the predictive accuracy of 72% for 
a four-class dataset. 
From these specific studies, we observe that the student 
performance could depend on diversified factors such as 
demographic, academic, psychological, socio-economic and other 
environmental factors. It was learnt that the predictive accuracy of 
constructed models ranged between 38.33 to 93%. The variation in 
the predictive accuracy could be correlated with the nature of 
student data set and utilization of number of records, predictive 
variables and class values of response variable.  
Methodology 
All the needed information was available in the school head master 
or self by student and its parents in form of through Questionnaire. 
Information not to be collected from the District Education office or 
the using web side and district project office, specially designed 
questionnaire were used in collecting information and data of 
student . Accuracy and Efficiency.  
Classifier accuracy: predicting class label to be. 
Predictor accuracy: guessing value of predicted attributes and its 
field. 
Speed or Time. 
Time to construct the model or training time. 
Time to use the model by using classification or prediction time. 
Robustness: handling noise and missing values of data. 
Scalability: efficiency in disk-resident databases of student. 
Interpretability: Understanding and insight provided by the model of 
real. 
Survey cum experimental methodology was adopted to generate a 
database for the present study. The basis of this database was 
from the primary and secondary sources. While the primary data 
was collected from the regular students, the secondary data was 
gathered from the school office. 
Data Source 
A detailed questionnaire was prepared with the reference, 
assistance and guidance from the (i) Review of related literature, 
 Singh et al. Binary Journal of Data Mining & Networking 4 (1) 10-13 [2014] 
 
PAGE | 12 |
 
 
(ii) Teachers of diversified schools, (iii) Parents of primary school 
students, (iv) Educational experts in school, colleges and 
Universities. The designed four page A4 size close-ended 
questionnaire was used for the collection of student details. Most of 
the information of the variables was collected directly from the 
students through this questionnaire. Based on this information, a 
few derived variables were generated. While some of the 
information for the variables was extracted from the school records, 
the mark details were collected from thee education office. All the 
predictor and response variables which were derived from the 
questionnaire are given in Table 1 for reference. 
 
Table 1 data Source 
S. No. Attribute name Description 
1 School_Code School dice code 
2 Name_Place Place of school 
3 Name_Block Name of block (three blocks of Batul District)) 
4 District Batul (M.P.) 
5 Scholer_Number Student scholar number 
6 Name_Student Name of student 
7 Student_Father_Name Student father name 
8 Student_Mother_Name MotherÊs name 
9 Age Age of student (06-10 years) 
10 Sex Gender (M, F) 
11 Class (III, IV, V) 
12 Category Category (SC, ST, Gen, OBC) 
13 School_type (Govt., Private) 
14 Location_School Rural of Urban 
15 No_teachers Number of TeacherÊs in school 
16 family_size Number of members of in a student family 
17 Living_zone Residential area of student (Rural, Urban) 
18 Father_edu FatherÊs Education 
19 Father_occup Occupation of student father 
20 Mother_edu Mother; Education 
21 Mother_occu Occupation of StudentÊs Mother 
22 Family_income Family income 
23 Private_tuision Are student take private tusion? 
24 Attendence_School Attendence of studentÊs in a class 
25 Previous_result Previous year result of student in percentage 
26 Grade_Previous_Result Previous year result of student in Grad                
 
Tools and Techniques 
Classification trees are widely used in different fields such as 
medicine, computer science, botany and psychology [11]. These 
trees readily lend themselves to being displayed graphically, 
helping to make them easier to interpret than they would be if only 
a strict numerical interpretation were possible. 
Primary school student [12], which is one of the classification tree 
algorithms, is the name given to one version of the Automatic 
Interaction Detector that has been developed for categorical 
variables. In fact, student is a technique that recursively partitions 
(or splits) a population into separate and distinct segments. These 
segments, called nodes, are split in such a way that the variation of 
the response variable (categorical) is minimized within the 
segments and maximized among the segments. After the initial 
splitting of the population into two or more nodes (defined by 
values of an independent or predictor variable), the splitting 
process is repeated on each of the. 
Conclusion 
We used students' data from database course of betul Distinct. We 
collected all available data including their usage of Model learning 
facility of education. We applied determining techniques to discover 
knowledge. Every I discovered Classification rules based and we 
sorted the rules using lift metric then we visualized than generated 
different rules. The  we discovered classification rules using 
decision tree. Experiments could be done using more data mining 
techniques such as n ural ets, Classification rules, genetic 
algorithms, nearest Neighbor, Naive Bayes, support vector 
machines and others. The used preprocess and data mining 
algorithms could be embedded into learning system so that anyone 
using the system c n benefit from Classification rules the data 
mining techniques. 
 
 
 
 
 
 
 
References 
[1]. Walters YB, and Soyibo K. „An 
Analysis of High School Students' 
Performance on Five Integrated 
Science Process Skills‰, Research in 
Science & Technical Education, 
2001;19(2):133 ? 145.  
[2]. Khan ZN. „Scholastic Achievement of 
Higher Secondary Students in Science 
Stream‰, Journal of Social 
Sciences,2005; 1 (2):84-87.  
[3]. Hijazi ST, and Naqvi RSMM. „Factors 
Affecting StudentÊs Performance: A 
Case of Private Colleges‰, Bangladesh 
e-Journal of Sociology, 2006;3(1).  
[4]. Ma Y, Liu B, Wong CK, Yu PS, and 
Lee SM. „Targeting the Right Students 
Using Data Mining‰, Proceedings of 
KDD, International Conference on 
Knowledge discovery and Data Mining, 
Boston, USA, 2000, 457-464.  
[5]. Kotsiantis S, Pierrakeas C, and 
Pintelas P. „Prediction of StudentÊs 
Performance in Distance Learning 
Using Machine Learning Techniques‰, 
Applied Artificial Intelligence, 2004;18,  
(5):411-426. IJCSI International. 
[6]. Cortez P, and Silva A. „Using Data 
Mining To Predict Secondary School 
Student Performance‰, In EUROSIS, A. 
Brito and J. Teixeira (Eds.), 2008, 5-12.  
 Singh et al. Binary Journal of Data Mining & Networking 4 (1) 10-13 [2014] 
 
PAGE | 13 |
 
 
[7]. Kristjansson A L, Sigfusdottir I G, and 
Allegrante JP. „Health Behavior and 
Academic Achievement Among 
Adolescents: The Relative Contribution 
of Dietary Habits, Physical Activity, 
Body Mass Index, and Self-Esteem‰, 
Health Education & Behavior, (In 
Press).  
[8]. Moriana JA, Alos F, Alcala R, Pino M J 
J. Academic Performance in 
Secondary Students‰, Electronic 
Journal of Research in Educational 
Psychology,2006;4(1):35-46. 
[9]. Bray M. The Shadow Education 
System: Private Tutoring And Its 
Implications For Planners, (2nd ed.), 
UNESCO, PARIS, France, 2007.  
[10]. AI-Radaideh Q A, AI-Shawakfa E M, 
and AI-Najjar MI. „Mining Student Data 
using Decision Trees‰, International 
Arab Conference on Information 
Technology(ACIT'2006), Yarmouk 
University, Jordan, 2006.  
[11]. Camdeviren HA, Yazici AC, Akkus Z, 
Bugdayci R, and Sungur MA. 
„Comparison of Logistic Regression 
Model and Classification Tree: An 
Application to Postpartum Depression 
Data‰, Expert Systems with 
Applications, 2007; 32(4): 987-994.  
[12]. Kass GV. „An Exploratory Technique 
for Investigating Large Quantities of 
Categorical Data‰, Applied Statistic, 
1980; 29:119-127.  
[13]. Witten IH, and Frank E. Data Mining ? 
Practical Machine Learning Tools and 
Techniques (2
nd 
ed.), San Francisco, 
CA: Morgan Kaufmann Publisher, 
2005.  
[14]. Liu H, and Setiono R. „Chi-square: 
Feature Selection and Discretization of 
Numeric Attributes‰, Proceedings of 
IEEE 7th International Conference on 
Tools with Artificial Intelligence, 1995. 
338,  (391)  
[15]. Ganesh S. „Data Mining: Should it be 
Included in the Statistics Curriculum?‰ 
The 6th international conference on 
teaching statistics (ICOTS-6), Cape 
Town, South Africa, 2002.  
[16]. Ramasamy K. „Mother Tongue and 
Medium of Instruction ? A Continuing 
Battle‰, Language in India, Vol. 1, No. 
6, 
[17]. Orlando S, Palmerini P, and Perego R. 
Enhancing the Apriori Algorithm for 
Frequent Set Counting. Proceedings of 
3rd International Conference on Data 
Warehousing and Knowledge 
Discovery. 2001. 
[18]. Grahne G,  Lakshmanan  L,  and Wang  
X.  2000. Efficient mining of 
constrained correlated sets. In Proc. 
2000. Int. Conf. Data Engineering 
(ICDEÊ00), San Diego, CA, pp. 512?
521.  
[19]. Dong G, and Li J. 1999. Efficient 
mining of emerging patterns: 
Discovering trends and differences. In 
Proc. 1999  Int. Conf. Knowledge 
Discovery and Data Mining (KDDÊ99), 
San Diego, CA, pp. 43?52. 
[20]. Bayardo RJ. 1998. Efficiently mining 
long patterns from databases. In Proc. 
1998 ACM-SIGMOD Int. Conf. 
Management of Data (SIGMODÊ98), 
Seattle, WA, pp. 85?93. 
[21]. Cheung D W L, Lee S D, and Kao B. "A 
general incremental technique for 
maintaining discovered association 
rules," in Proceedings of the 15th 
International Conference on Database 
Systems for Advanced Applications, 
pp. 185-194, 1997. 
 
 
 


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Betul Districts Primary School Performance Prediction Model Using Data Mining

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