Towards an Adaptive Learning Model using Optimal Learning Paths to Prevent MOOC Dropout

Authors

DOI:

https://doi.org/10.3991/ijep.v13i7.40075

Keywords:

Adaptive Learning, MOOC, ACO, PCA, Drop out

Abstract


Currently, massive open online courses (MOOCs) are experiencing major developments and are becoming increasingly popular in distance learning programs. The goal is to break down inequalities and disseminate knowledge to everyone by creating a space for exchange and interaction. Despite the improvements to this educational model, MOOCs still have low retention rates, which can be attributed to a variety of factors, including learners’ heterogeneity. The paper aims to address the issue of low retention rates in MOOCs by introducing an innovative prediction model that provides the best (optimal) learning path for at-risk learners. For this purpose, learners at risk of dropping out are identified, and their courses are adapted to meet their needs and skills. A case study is presented to validate the effectiveness of our approach using classification algorithms for prediction and the ant colony optimization (ACO) algorithm to optimize learners’ paths.

Author Biographies

El Miloud Smaili, IBN TOFAIL University

El Miloud Smaili  is a PhD student in the field of machine learning/deep learning through the prediction of learners at risk of dropping out in MOOC and the proposal of adaptive learning optimization solutions to improve the quality of MOOC. Holder of a state engineering degree in computer engineering at ENSAO, senior state engineer at the Faculty of Human and Social Sciences - University Ibn Tofail, Kenitra, Morocco (Email: miloud.smaili@uit.ac.ma).

Mohamed Daoudi, IBN TOFAIL University

Mohamed Daoudi is a Database administrator at Ibn Tofail University, Kenitra, Morocco since 2011. Holder of a state engineer's degree in computer science at ENSAO. Currently a PhD student in the field of mobile learning and learning analytics. The main goal of the research conducted is to improve student engagement and success by using Smartphones in learning.

Ilham Oumaira, IBN TOFAIL University

Ilham Oumaira is an Associate Professor at the National School of Applied Sciences-Kenitra. She is a member of the Laboratory of Engineering Science at Ibn Tofail University, Morocco. Since 2014, she has been in charge of the Center for Educational and Digital Innovation. Her current research focuses on MOOCs and more specifically on learning analytics. The ultimate goal is to improve student success, personalize learning and maximize teaching effectiveness.

 

Salma Azzouzi, IBN TOFAIL University

Salma Azzouzi is a Professor at Ibn Tofail University - Faculty of Science (Computer Science Department). She is a member of the LaRI laboratory. She is also the general co-chair of the International Conference on Electronics, Control, Optimization and Computing (ICECOCS) and guest editor of a special issue of the journal SOIC. Her main areas of interest are: Distributed testing, e-learning, optimization. (Email: salma.azzouzi@uit.ac.ma).

Moulay El Hassan Charaf, IBN TOFIL University

Moulay El Hassan Charaf is a Professor at Ibn Tofail University - Faculty of Sciences (Department of Computer Science). He is a member and adjoint director of the LaRI laboratory. He is also the general co-chair of the International Conference on Electronics, Control, Optimization and Computing (ICECOCS) and guest editor of the special issue on Intelligent Control for Future and Complex Systems in the International Journal of Modeling, Identification and Control (IJMIC). His main areas of interest are: Control, optimization, adaptive learning, distributed testing. (Email: my.charaf@uit.ac.ma).

Cover iJEP – Vol 13, No 7, 2023

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Published

2023-10-25

How to Cite

Smaili, E. M., Daoudi, M., Oumaira, I., Azzouzi, S., & Charaf, M. E. H. (2023). Towards an Adaptive Learning Model using Optimal Learning Paths to Prevent MOOC Dropout. International Journal of Engineering Pedagogy (iJEP), 13(7), pp. 128–144. https://doi.org/10.3991/ijep.v13i7.40075

Issue

Vol. 13 No. 7 (2023)

Section

Papers

License

Copyright (c) 2023 El Miloud Smaili, Mohamed Daoudi, Ilham Oumaira, Salma Azzouzi, My El Hassan CHARAF

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