Educational Data Mining Rule based Recommender Systems

Ghadeer Mobasher, Ahmed Shawish, Osman Ibrahim

2017

Abstract

Educational Data Mining (EDM) is an emerging multidisciplinary research area, in which data mining techniques are deployed to extract knowledge from educational information systems to help decision makers to improve the learning process and enhance the academic performance of the students. The available studies mainly focused on predicting the academic performance based on demographic and study related attributes. Most of the previous work adopted the decision trees as one of the most famous data mining techniques to predict rather than extracting real knowledge that reveals the reasons behind student’s dropout. On the other hand, there were other studies in the psychological track to measure the mental health score based on the educational environment. This paper proposes a complete EDM framework in a form of a rule based recommender system that is not developed to analyze and predict the student’s performance only, but also to exhibit the reasons behind it. The proposed framework analyzes the students’ demographic data, study related and psychological characteristics to extract all possible knowledge from students, teachers and parents.Seeking the highest possible accuracy in academic performance prediction using a set of powerful data mining techniques. The framework succeeds to highlight the student’s weak points and provide appropriate recommendations. The realistic case study that has been conducted on 200 students proves the outstanding performance of the proposed framework in comparison with the existing ones.

Paper Citation

in Harvard Style

Mobasher G., Shawish A. and Ibrahim O. (2017). Educational Data Mining Rule based Recommender Systems . In Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-239-4, pages 292-299. DOI: 10.5220/0006290902920299

in Bibtex Style

@conference{csedu17,
author={Ghadeer Mobasher and Ahmed Shawish and Osman Ibrahim},
title={Educational Data Mining Rule based Recommender Systems},
booktitle={Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2017},
pages={292-299},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006290902920299},
isbn={978-989-758-239-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Educational Data Mining Rule based Recommender Systems
SN - 978-989-758-239-4
AU - Mobasher G.
AU - Shawish A.
AU - Ibrahim O.
PY - 2017
SP - 292
EP - 299
DO - 10.5220/0006290902920299