Dynamic optimization algorithm for personalized Japanese learning path driven by reinforcement learning

Abstract

Personalized learning aims to tailor learning content to individual learners based on their performance, conduct, and interests. Conventional approaches lack adaptability in dynamic decision-making and don’t handle uncertainty in learner behaviour, leading to less-than-optimal learning paths. This research aims to develop a Personalized Optimization Engine for Reinforced Sequencing of Japanese Learning Activities (PERSOJAPAN), capable of adapting to individual learner profiles and providing adaptive personalized learning performance. To overcome these shortfalls, introduce a proposed method, an intelligent framework integrating Partially Observable Markov Decision Process (POMDP), Duelling Deep Q-Network (DDQN), and Synergistic Ant Colony Optimization (SACO) for optimal learning path personalization. The model is learned and tested with the Japan Learning Dataset from Kaggle containing learner activity and performance information. The dataset is pre-processed with Z-score normalization and then feature extracted with Principal Component Analysis (PCA) to improve computational efficiency. POMDP models the probabilistic aspect of learner states, DDQN discovers optimal learning actions by applying deep reinforcement learning, and SACO optimizes the path personalization with bio-inspired optimization. This combined framework facilitates adaptive, dynamic learning that is appropriate for every student’s changing requirements. Performance is measured through recall of 86.1%, F1-score of 87.4%, and AUC of 0.978, wherein the designed method performs better than current baselines, reflecting enhanced accuracy, decision-making quality, and personalization strength. Overall, the framework successfully addresses the limitations of traditional rule-based and static systems by introducing a scalable, data-driven approach that adapts to individual learner needs, enhances engagement, supports adaptive personalized learning feedback, and continuously evolves to improve personalized learning outcomes effectively.