Predicting Student Performance in Distance Higher Education Using Semi-supervised Techniques

  • Georgios Kostopoulos
  • Sotiris Kotsiantis
  • Panagiotis Pintelas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9344)

Abstract

Students’ performance prediction in distance higher education has been widely researched over the past decades. Machine learning techniques and especially supervised learning have been used in numerous studies to identify in time students that are possible to fail in final exams. The identification of in case failure as soon as possible, could lead the academic staff to develop learning strategies aiming to improve students’ overall performance. In this paper, we investigate the effectiveness of semi-supervised techniques in predicting students’ performance in distance higher education. Several experiments take place in our research comparing to the accuracy measures of familiar semi-supervised algorithms. As far as, we are aware various researches deal with students’ performance prediction in distance learning by using machine learning techniques and especially supervised methods, but none of them investigate the effectiveness of semi-supervised algorithms. Our results confirm the advantage of semi-supervised methods and especially the satisfactory performance of Tri-Training algorithm.

Keywords

Distance higher education Performance prediction Semi-supervised learning Tri-training C4.5 decision tree 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Georgios Kostopoulos
    • 1
  • Sotiris Kotsiantis
    • 1
  • Panagiotis Pintelas
    • 1
  1. 1.Educational Software Development Laboratory (ESDLab), Department of MathematicsUniversity of PatrasPatrasGreece

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