An Innovative Approach to Prevent Learners’ Dropout from MOOCs using Optimal Personalized Learning Paths: An Online Learning Case Study - SMAILI, El Miloud; Khoudda, Chaimaa; Sraidi, Soukaina; Azzouzi, Salma; Charaf, My El Hassan (Statistics, Optimization & Information Computing, 2022)
Due to the rapid evolution of information technology, distance education has undergone a sustained expansion. Within the scope of open distant learning, MOOCs (Massive Open Online Courses) tend to open up access to education to all by bridging geographical and economic barriers. However, the evolution of this learning mode is facing some challenges such as the low completion rate of the courses as well as the heterogeneity of the learners’ profifiles. In this paper, we aim to personalize the MOOC contents for each learner in order to improve their academic performance and to enhance the online platforms effificiency. The idea is to build a system that takes into account the heterogeneity of learners profifiles and offers each learner a path adapted to their needs through the exploitation of their interactions with the learning environment. To this end, we suggest using the PSO method ”Particle Swarm Optimization” in order to construct the optimal choices of learning paths in the system. Furthermore, we conduct an online learning case study to show the effectiveness of the proposed approach.