An improved constrained learning path adaptation problem based on genetic algorithm

Bibliographic Data

ID21631971
AuthorsOuissem Benmesbah (0000-0003-2403-3002, Badji Mokhtar University, corresponding author), Mahnane Lamia (0000-0003-4050-7183, Badji Mokhtar University), Mohamed Hafidi (0000-0002-4656-7614, Badji Mokhtar University)
Year2023
Volume31
Issue6
Pages3595-3612
Publication date2023-08-18
Open AccessNo
TypeARTICLE
VenueInteractive Learning Environments (JOURNAL)
Journal identifiersISSN: 1049-4820 • E-ISSN: 1744-5191
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/10494820.2021.1937659
OpenAlexW3170985148
LanguageEN
Citations received3
References cited24

Adaptive learning has garnered researchers’ interest. The main issue within this field is how to select appropriate learning objects (LOs) based on learners’ requirements and context, and how to combine the selected LOs to form what is known as an adaptive learning path. Heuristic and metaheuristic approaches have achieved significant progress on personalized and adaptive recommendations, but the operators of some heuristic algorithms are often fixed which decreases the algorithms’ extendibility. This paper reviews existing works and proposes an innovative approach. We model the proposed approach as a constraints satisfaction problem, and an improved genetic algorithm named adaptive genetic algorithm is proposed to solve it. The proposed solution does not only reduce the search space size and increase search efficiency but also it is more explicit in finding the best composition of LOs for a specific learner. As a result, the best personalized adaptive learning resources combination will be found in lesser time

Adaptive Learning · Algorithm · Genetic algorithm · Heuristic · Machine learning · Metaheuristic · Computer Science · Mathematics · Mobile Learning in Education · Online Learning and Analytics · Open Education and E-Learning · Artificial Intelligence

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