Using data mining on student behavior and cognitive style data for improving e-learning systems: a case study

DOI

10.1080/18756891.2012.696923How to use a DOI?

Keywords

educational data mining, prediction, students, performance, classification, clustering, Moodle

Abstract

In this research we applied classification models for prediction of students’ performance, and cluster models for grouping students based on their cognitive styles in e-learning environment. Classification models described in this paper should help: teachers, students and business people, for early engaging with students who are likely to become excellent on a selected topic. Clustering students based on cognitive styles and their overall performance should enable better adaption of the learning materials with respect to their learning styles. The approach is tested using well-established data mining algorithms, and evaluated by several evaluation measures. Model building process included data preprocessing, parameter optimization and attribute selection steps, which enhanced the overall performance. Additionally we propose a Moodle module that allows automatic extraction of data needed for educational data mining analysis and deploys models developed in this study.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Milos Jovanovic
AU  - Milan Vukicevic
AU  - Milos Milovanovic
AU  - Miroslav Minovic
PY  - 2012
DA  - 2012/06/01
TI  - Using data mining on student behavior and cognitive style data for improving e-learning systems: a case study
JO  - International Journal of Computational Intelligence Systems
SP  - 597
EP  - 610
VL  - 5
IS  - 3
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2012.696923
DO  - 10.1080/18756891.2012.696923
ID  - Jovanovic2012
ER  -