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Web usage mining for predicting final marks of students that use Moodle courses
Author(s) -
Romero Cristobal,
Espejo Pedro G.,
Zafra Amelia,
Romero Jose Raul,
Ventura Sebastian
Publication year - 2013
Publication title -
computer applications in engineering education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.478
H-Index - 29
eISSN - 1099-0542
pISSN - 1061-3773
DOI - 10.1002/cae.20456
Subject(s) - computer science , educational data mining , machine learning , classifier (uml) , decision tree , web mining , data mining , rule induction , artificial neural network , the internet , artificial intelligence , discretization , world wide web , web page , mathematical analysis , mathematics
This paper shows how web usage mining can be applied in e‐learning systems in order to predict the marks that university students will obtain in the final exam of a course. We have also developed a specific Moodle mining tool oriented for the use of not only experts in data mining but also of newcomers like instructors and courseware authors. The performance of different data mining techniques for classifying students are compared, starting with the student's usage data in several Cordoba University Moodle courses in engineering. Several well‐known classification methods have been used, such as statistical methods, decision trees, rule and fuzzy rule induction methods, and neural networks. We have carried out several experiments using all available and filtered data to try to obtain more accuracy. Discretization and rebalance pre‐processing techniques have also been used on the original numerical data to test again if better classifier models can be obtained. Finally, we show examples of some of the models discovered and explain that a classifier model appropriate for an educational environment has to be both accurate and comprehensible in order for instructors and course administrators to be able to use it for decision making. © 2010 Wiley Periodicals, Inc. Comput Appl Eng Educ 21: 135–146, 2013