Classification and Prediction Based Data Mining Algorithms to Predict Slow Learners in Education Sector

Educational Data Mining field concentrate on Prediction more often as compare to generate exact results for future purpose. In order to keep a check on the changes occurring in curriculum patterns, a regular analysis is must of educational databases. This paper focus on identifying the slow learners...

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Bibliographic Details
Published in:Procedia computer science Vol. 57; pp. 500 - 508
Main Authors: Kaur, Parneet, Singh, Manpreet, Josan, Gurpreet Singh
Format: Journal Article
Language:English
Published: Elsevier B.V 2015
Subjects:
Attribute Evaluator
Classification
Educational Data Mining
Knowledge Discovery
Educational Data Mining
Attribute Evaluator
Knowledge Discovery
Classification
ISSN:1877-0509, 1877-0509
Online Access:Get full text
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Summary:Educational Data Mining field concentrate on Prediction more often as compare to generate exact results for future purpose. In order to keep a check on the changes occurring in curriculum patterns, a regular analysis is must of educational databases. This paper focus on identifying the slow learners among students and displaying it by a predictive data mining model using classification based algorithms. Real World data set from a high school is taken and filtration of desired potential variables is done using WEKA an Open Source Tool. The dataset of student academic records is tested and applied on various classification algorithms such as Multilayer Perception, Naïve Bayes, SMO, J48 and REPTree using WEKA an Open source tool. As a result, statistics are generated based on all classification algorithms and comparison of all five classifiers is also done in order to predict the accuracy and to find the best performing classification algorithm among all. In this paper, a knowledge flow model is also shown among all five classifiers. This paper showcases the importance of Prediction and Classification based data mining algorithms in the field of education and also presents some promising future lines.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2015.07.372