Adapt Learning Path by Recommending Problems to Struggling Learners

Authors

  • Youssef Jdidou Abdelmalek Essaâdi University, Faculty of Science
  • Souhaib Aammou Abdelmalek Essaâdi University, Ecole Normale Supérieure
  • Mohamed Khaldi Abdelmalek Essaâdi University, Ecole Normale Supérieure

DOI:

https://doi.org/10.3991/ijet.v16i20.24283

Keywords:

Recommender System, Collaborative Filter, Learning path, Edx platform

Abstract


The objective of this work is the creation of a resource recommendation ap-plication in Python integrated into the code of the virtual edX platform, which appears as an additional tab in each course. By selecting this tab, learners will have access at any time to their recommended issues for this course, and so they can adapt their learning path. In this article, we present a recommendation algorithm that will be responsible for proposing these prob-lems according to the scores obtained in the problems already performed by the learner. By calculating the similarity with the rest of the classmates, an estimate of the most practical problems for the learner will be made. We also present the different functions and parameters to implement it.

Author Biographies

Youssef Jdidou, Abdelmalek Essaâdi University, Faculty of Science

Youssef Jdidou is the General Manager of TEACHIUM, a global workplace learning company that helps businesses improve their performance through learning and technology. In research, his current interests include: E-learning, Adaptive Hypermedia Systems, MOOCs, and RECOMMENDATION SYSTEMS. He is the President of the Association of Scientific Research, Innovation and Technology, Founder of Tetuan International Conference on Education and Technology and Co-Founder of TEDxCapeSpartel. He has been involved in several projects like MOOCMAROC, SMARTER and chess for everyone. His a strong believer that social media can be used to educate society , he stand for hope and the power of believing.

Souhaib Aammou, Abdelmalek Essaâdi University, Ecole Normale Supérieure

is a Professor at Ecole Normale Supérieure, Abdelmalek Essaadi University, Tetuan, Morocco; member of research group of Computer sciences and universi-ty educational engineering. His research interests include Knowledge representa-tion and reasoning, Semantic networks, Educational Recommendation Systems, Human computer interaction (HCI) theory and educational technologies for learning. Author and co-author of more than 20 publications in international peer-reviewed journals. Reviewer in several refereed journals (IRRODL, iJIM …)

Mohamed Khaldi, Abdelmalek Essaâdi University, Ecole Normale Supérieure

is a full Professor at Ecole Normale Supérieure, Abdelmalek Essaâdi University, Tetuan, Morocco. Member of research group of Computer sciences and universi-ty educational engineering. His research interests include educational technolo-gies for learning, MOOCs, Adaptive Hypermedia Systems. Author and co-author of more than 50 publications in international peer-reviewed journals.

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Published

2021-10-25

How to Cite

Jdidou, Y., Aammou, S., & Khaldi, M. (2021). Adapt Learning Path by Recommending Problems to Struggling Learners. International Journal of Emerging Technologies in Learning (iJET), 16(20), pp. 163–178. https://doi.org/10.3991/ijet.v16i20.24283

Issue

Vol. 16 No. 20 (2021)

Section

Papers