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 in:Journal of educational computing research 2019-04, Vol.57 (2), p.448-470
Main Authors: Livieris, Ioannis E., Drakopoulou, Konstantina, Tampakas, Vassilis T., Mikropoulos, Tassos A., Pintelas, Panagiotis
Format: Article
Language:English
Subjects:
Academic Achievement
Data Processing
Foreign Countries
Mathematics
Predictor Variables
Secondary School Students
Student Behavior
Teaching 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