An improved constrained learning path adaptation problem based on genetic algorithm

ABSTRACT

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.

KEYWORDS:

• Learning path adaptation
• learner’s context
• genetic algorithm
• evolutionary algorithms
• intelligent learning environments
• CSP

Acknowledgements

The authors wish to pay their sincere gratitude to Prof. Amer Draa for his expert suggestions and for his assistance.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Ouissem Benmesbah

Ouissem Benmesbah is a PhD student in Computer Science From Badji Mokhtar University – LRS Laboratory, her current research interest is Adaptation in Mobile learning systems, Evolutionary algorithm, Optimization, Ontology.

Mahnane Lamia

Mahnane Lamia received his PhD in computer science from the University of Annaba (Algeria) in 2017. She is currently working as an Associate Professor at the Computer Science Department of Annaba University, Algeria. She has several published papers in various books and international conferences. His current research interests are e-Learning, learning style and adaptive hypermedia.

Mohamed Hafidi

Mohamed Hafidi received his HDR in computer science from the University of Badji Mokhtar Annaba (Algeria, 2018). He is currently working as an Associate Professor at the Computer Science Department of Annaba University, Algeria. He has several published papers in various books and international conferences. His current research interests are M-Learning, Intelligent and adaptive hypermedia, Flipped classroom, Social network, Big Data, Cognitive load, Data Mining, Ontology, Context Awareness, IOT.