An Optimized Adaptive Learning Approach Based on Cuckoo Search Algorithm

The rapid expansion of MOOCs (massive open online courses) allows learners to benefit from these courses by removing the barriers that obstruct the right to an open high-quality education. The courses offered on MOOC platforms are often free which has revolutionized this mode of distance learning, e...

Full description

Saved in:
Bibliographic Details
Published in2022 8th International Conference on Optimization and Applications (ICOA) pp. 1 - 4
Main Authors Smaili, El Miloud, Azzouzi, Salma, El Hassan Charaf, My
Format Conference Proceeding
LanguageEnglish
Published IEEE 06.10.2022
Subjects
Adaptive learning
Certification
Computer aided instruction
COVID-19
Cuckoo Search Algorithm
Drop out
Electronic learning
Metaheuristics
MOOC
Pandemics
Online AccessGet full text

Cover

More Information
Summary:The rapid expansion of MOOCs (massive open online courses) allows learners to benefit from these courses by removing the barriers that obstruct the right to an open high-quality education. The courses offered on MOOC platforms are often free which has revolutionized this mode of distance learning, especially with the restrictions imposed by the advent of the COVID-19 pandemic. However, even though the number of registrants to MOOCs is quite considerable, only 10% of the learners complete the MOOC and obtain a certification. This phenomenon leads us to dig deeper to wonder about the means to avoid the high dropout rate of learners in such platforms. For this purpose, we suggest in this paper two complementary systems: a preventive system coupled with a proactive system to personalize the learners' pathways according to their specific needs and prior knowledge. The optimization of the pathways will be handled using a metaheuristic optimization algorithm called: Cuckoo Search Algorithm.
ISSN:2768-6388
DOI:10.1109/ICOA55659.2022.9934280