Abstract
Student dropout occurs quite often in universities providing distance education. The scope of this research is to study whether the usage of machine learning techniques can be useful in dealing with this problem. Subsequently, an attempt was made to identifying the most appropriate learning algorithm for the prediction of students’ dropout. A number of experiments have taken place with data provided by the ‘informatics’ course of the Hellenic Open University and a quite interesting conclusion is that the Naive Bayes algorithm can be successfully used. A prototype web based support tool, which can automatically recognize students with high probability of dropout, has been constructed by implementing this algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aha, D.: Lazy Learning. Kluwer Academic Publishers, Dordrecht (1997)
Burges, C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 1–47 (1998)
Chyung, Y., Winiecki, D.J., Fenner, J.A.: A Case Study: increase enrollment by reducing dropout rates in adult distance education. In: Annual Conference on Distance Teaching and Learning, USA (1998)
Domingos, P., Pazzani, M.: On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 29, 103–130 (1997)
Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97, 273–324 (1997)
Long, J.: Regression models for categorical and limited dependent variables. Sage, Thousand Oaks (1997)
Mitchell, T.: Machine Learning. McGraw Hill, New York (1997)
Murthy, S.: Automatic Construction of Decision Trees from Data: A Multi- Disciplinary Survey. Data Mining and Knowledge Discovery 2, 345–389 (1998)
Parker, A.: A study of variables that predict dropout from distance education. International Journal of Educational Technology 1, 1–10 (1999)
Platt, J.: Using sparseness and analytic QP to speed training of support vector machines. In: Kearns, M.S., Solla, S.A., Cohn, D.A. (eds.) Advances in neural information processing systems 11. MIT Press, MA (1999)
Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufmann, San Francisco (1993)
Salzberg, S.: On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach. Data Mining and Knowledge Discovery 1, 317–328 (1997)
Schaffer, C.: A conservation law for generalization performance. In: Proceedings of the 1994 International Conference on Machine Learning, pp. 153–178. Morgan Kaufmann, Ca (1994)
Shin, N., Kim, J.: An exploration of learner progress and drop-out in Korea National Open University. Distance Education - An International Journal 20 (1999)
Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Mateo (2000)
Xenos, M., Pierrakeas, C., Pintelas, P.: A survey on student dropout rates and dropout causes concerning the students in the course of informatics of the Hellenic Open University. Computers & Education 39, 361–377 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
� 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kotsiantis, S.B., Pierrakeas, C.J., Pintelas, P.E. (2003). Preventing Student Dropout in Distance Learning Using Machine Learning Techniques. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2774. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45226-3_37
Download citation
DOI: https://doi.org/10.1007/978-3-540-45226-3_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40804-8
Online ISBN: 978-3-540-45226-3
eBook Packages: Springer Book Archive