A Prototype of Efficient Learning System for Objective-Driven Learners

Personalized learning path recommendation is widely studied to address the cognitive overload and learning disorientation problem in e-learning platforms. Yet, how to recommend learning materials for learners so that targeting their learning objectives with the highest efficiency has not been addressed. Driven by this problem, this paper proposes a prototype to generate the most efficient learning strategies in the learning process for reaching learning objectives, it takes the learning ability of learners into consideration, and adaptively generates the optimal strategy in the learning process. A case that knowledge concepts have hierarchical relationships based on difficulty in one knowledge topic is studied, which is named series learning in this paper. Specifically, a learning state transition model is designed for selecting the optimal learning strategies in the learning process with the consideration of the changing learning ability of learners. Based on this model, a dynamically programming (DP) based method and a greedy-based method are designed to generate learning strategies with high efficiency. Experiments are conducted in both simulated and real learning scenarios. Results show that the proposed scheme significantly outperforms the baseline method.