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

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  • Ioannis E. Livieris
    Department of Computer and Informatics Engineering (DISK Lab), Technological Educational Institute of Western Greece, Patras, Greece
  • Konstantina Drakopoulou
    Department of Mathematics, University of Patras, Patras, Greece
  • Vassilis T. Tampakas
    Department of Computer and Informatics Engineering (DISK Lab), Technological Educational Institute of Western Greece, Patras, Greece
  • Tassos A. Mikropoulos
    The Educational Approaches to Virtual Reality Technologies Lab, University of Ioannina, Ioannina, Greece
  • Panagiotis Pintelas
    Department of Mathematics, University of Patras, Patras, Greece

Description

<jats:p> 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. </jats:p>

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