Predicting Secondary School Students' Performance Utilizing a Semi-supervised Learning Approach

Educational data mining constitutes a recent research field which gained popularity over the last decade because of its ability to monitor students' academic performance and predict future progression. Numerous machine learning techniques and especially supervised learning algorithms have been...

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Bibliographic Details
Published inJournal of educational computing research Vol. 57; no. 2; pp. 448 - 470
Main Authors Livieris, Ioannis E., Drakopoulou, Konstantina, Tampakas, Vassilis T., Mikropoulos, Tassos A., Pintelas, Panagiotis
Format Journal Article
LanguageEnglish
Published Los Angeles, CA SAGE Publications 01.04.2019
Subjects
Academic Achievement
Data Processing
Foreign Countries
Mathematics
Predictor Variables
Secondary School Students
Student Behavior
Teaching Methods
Greece
student's evaluation system
self-training
Yet Another Two Stage Idea
educational data mining
semisupervised methods
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Summary:Educational data mining constitutes a recent research field which gained popularity over the last decade because of its ability to monitor students' academic performance and predict future progression. Numerous machine learning techniques and especially supervised learning algorithms have been applied to develop accurate models to predict student's characteristics which induce their behavior and performance. In this work, we examine and evaluate the effectiveness of two wrapper methods for semisupervised learning algorithms for predicting the students' performance in the final examinations. Our preliminary numerical experiments indicate that the advantage of semisupervised methods is that the classification accuracy can be significantly improved by utilizing a few labeled and many unlabeled data for developing reliable prediction models.
ISSN:0735-6331
1541-4140
DOI:10.1177/0735633117752614