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Authors: Behdad Bakhshinategh 1 ; Gerasimos Spanakis 2 ; Osmar Zaiane 1 and Samira ElAtia 1

Affiliations: 1 University of Alberta, Canada ; 2 Maastricht University, Netherlands

ISBN: 978-989-758-239-4

Keyword(s): Course Recommender Systems, Graduating Attributes, Collaborative Filtering, Multicriteria Ratings.

Abstract: Assessing learning outcomes for students in higher education institutes is an interesting task with many potential applications for all involved stakeholders (students, administrators, potential employers, etc.). In this paper, we propose a course recommendation system for students based on the assessment of their “graduate attributes” (i.e. attributes that describe the developing values of students). Students rate the improvement in their graduating attributes after a course is finished and a collaborative filtering algorithm is utilized in order to suggest courses that were taken by fellow students and rated in a similar way. An extension to weigh the most recent ratings as more important is included in the algorithm which is shown to have better accuracy than the baseline approach. Experimental results using correlation thresholding and the nearest neighbors approach show that such a recommendation system can be effective when an active neighborhood of 10-15 students is us ed and show that the numbers of users used can be decreased effectively to one fourth of the whole population for improving the performance of the algorithm. (More)

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Paper citation in several formats:
Bakhshinategh, B.; Spanakis, G.; Zaiane, O. and ElAtia, S. (2017). A Course Recommender System based on Graduating Attributes.In Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-239-4, pages 347-354. DOI: 10.5220/0006318803470354

@conference{csedu17,
author={Behdad Bakhshinategh. and Gerasimos Spanakis. and Osmar Zaiane. and Samira ElAtia.},
title={A Course Recommender System based on Graduating Attributes},
booktitle={Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2017},
pages={347-354},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006318803470354},
isbn={978-989-758-239-4},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - A Course Recommender System based on Graduating Attributes
SN - 978-989-758-239-4
AU - Bakhshinategh, B.
AU - Spanakis, G.
AU - Zaiane, O.
AU - ElAtia, S.
PY - 2017
SP - 347
EP - 354
DO - 10.5220/0006318803470354

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