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Authors: Ghadeer Mobasher 1 ; Ahmed Shawish 2 and Osman Ibrahim 1

Affiliations: 1 The British University in Egypt, Egypt ; 2 The British University in Egypt and Ain Shams University, Egypt

Keyword(s): Educational Data Mining, Decision Trees, Reduced Error Pruning and Rule based Recommender System.

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 a nalyzes 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. (More)

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Paper citation in several formats:
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; ISSN 2184-5026, SciTePress, pages 292-299. DOI: 10.5220/0006290902920299

@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},
issn={2184-5026},
}

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
IS - 2184-5026
AU - Mobasher, G.
AU - Shawish, A.
AU - Ibrahim, O.
PY - 2017
SP - 292
EP - 299
DO - 10.5220/0006290902920299
PB - SciTePress

- Science and Technology Publications, Lda.
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