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Prediction and assessment of student behaviour in open and distance education in computers using Bayesian networks

Computers & Education Volume 43, Number 4, ISSN 0360-1315 Publisher: Elsevier Ltd


This paper presents a methodological approach based on Bayesian Networks for modelling the behaviour of the students of a bachelor course in computers in an Open University that deploys distance educational methods. It describes the structure of the model, its application for modelling the behaviour of student groups in the Informatics Course of the Hellenic Open University, as well as the advantages of the presented method under conditions of uncertainty. The application of this model resulted in promising results as regards both prediction of student behaviour, based on modelled past experience, and assessment (i.e., identification of the reasons that led students to a given `current' state). The method presented in this paper offers an effective way to model past experience, which can significantly aid in decision-making regarding the educational procedure. It can also be used for assessment purposes regarding a current state enabling tutors to identify mistakes or bad practices so as to avoid them in the future as well as identify successful practices that are worth repeating. The paper concludes that modelling is feasible and that the presented method is useful especially in cases of large amounts of data that are hard to draw conclusions from without any modelling. It is emphasised that the presented method does not make any predictions and assessments by itself; it is a valuable tool for modelling the educational experience of its user and exploiting the past data or data resulting from its use.


Xenos, M. (2004). Prediction and assessment of student behaviour in open and distance education in computers using Bayesian networks. Computers & Education, 43(4), 345-359. Elsevier Ltd. Retrieved November 12, 2019 from .

This record was imported from Computers & Education on January 30, 2019. Computers & Education is a publication of Elsevier.

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