Smart E-Learning Framework for Personalized Adaptive Learning and Sequential Path Recommendations Using Reinforcement Learning

Abstract

Learning activities are considerably supported and improved by the rapid advancement of e-learning systems. This gives students a tremendous chance to participate in learning activities worldwide. The Massive Open Online Courses (MOOCs) platform has emerged as one of the most significant platforms for e-learning as a result of the rapid growth of network information technologies. Due to the increased number of online courses available, it is harder for individual learners to choose the appropriate courses, activities, and learning paths for the actual necessities they want, which reduces their learning performances. Moreover, a sequential Recommender System (RS) can identify the learner’s future interest and suggest the subsequent item or learning content given a sequence of past interactions. This is in contrast to interactive recommendation methods that can create recommendations based on the learner’s feedback via constant interactions. To address these challenges, the goal of this paper is to propose a Reinforcement Learning (RL) based smart e-learning framework with Markov Decision Process (MDP) that has the potential to enhance the learning experience for each student by providing them with a personalized and effective learning path. Applying the MDP and RL-based techniques such as Q-learning for Sequential Path Recommendation (SPR) and learning development is more achievable. This is because the MDP allows for adjusting the recommendations method to find new activities and learning paths based on the learners’ feedback on recommendations results. Experimental findings reveal that the suggested model obtains significant improvements and provides viable performance under different parameters optimization. Furthermore, we also show that the proposed method outperforms a long session (long-term rewards such that they maximize learning progress while minimizing frustration and disengagement). This demonstrates the model’s improvements in simulating the learner’s sequential behavior, learning activities, various learning materials, and learning paths simultaneously. These promising initial results provide a possible solution to assess this challenge further in future work.