Open AccessAdapt Learning Path by Recommending Problems to Struggling Learners
Author(s) -
Youssef Jdidou,
Souhaib Aammou,
Mohamed Khaldi
Publication year - 2021
Publication title -
international journal of emerging technologies in learning/international journal: emerging technologies in learning
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.454
H-Index - 24
eISSN - 1868-8799
pISSN - 1863-0383
DOI - 10.3991/ijet.v16i20.24283
Subject(s) - python (programming language) , computer science , path (computing) , similarity (geometry) , resource (disambiguation) , program code , multimedia , code (set theory) , virtual learning environment , artificial intelligence , world wide web , programming language , computer network , set (abstract data type) , image (mathematics)
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.