Improve Performance of Recommender System in Collaborative Learning Environment based on Learner Tracks

Qing Tang, Marie-Hélène Abel, Elsa Negre

2020

Abstract

Learning with huge amount of open educational resources is challenging, especially when variety resources come from different System of Information Systems (SoIS). How to help learners obtain appropriate resources efficiently in collaborative learning environment is still a rigorous problem of research. This paper proposes a method to calculate learner’s knowledge competency by tracking and analyzing their behaviors in a collaborative learning environment based on SoIS, and combining other basic learner’s information to build a personalized recommender system to help learners select appropriate educational resources to improve their learning efficiency.

Paper Citation

in Harvard Style

Tang Q., Abel M. and Negre E. (2020). Improve Performance of Recommender System in Collaborative Learning Environment based on Learner Tracks. In Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 3: KMIS; ISBN 978-989-758-474-9, SciTePress, pages 270-277. DOI: 10.5220/0010214702700277

in Bibtex Style

@conference{kmis20,
author={Qing Tang and Marie-Hélène Abel and Elsa Negre},
title={Improve Performance of Recommender System in Collaborative Learning Environment based on Learner Tracks},
booktitle={Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 3: KMIS},
year={2020},
pages={270-277},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010214702700277},
isbn={978-989-758-474-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2020) - Volume 3: KMIS
TI - Improve Performance of Recommender System in Collaborative Learning Environment based on Learner Tracks
SN - 978-989-758-474-9
AU - Tang Q.
AU - Abel M.
AU - Negre E.
PY - 2020
SP - 270
EP - 277
DO - 10.5220/0010214702700277
PB - SciTePress