Article: Personalised learning path optimisation in digital English learning environments via multi-factor knowledge tracing and reinforcement learning Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.55 pp.24 - 42 Abstract: Digital English learning environments generate massive interaction data, offering potential for adaptive learning path optimisation. However, many existing approaches treat learning state estimation and learning path recommendation as separate tasks, restricting long-term personalised learning support. This study proposes a framework that integrates a multi-factor fusion knowledge tracing (MFFKT) model with reinforcement learning. It jointly analyses behaviour sequences, item attributes, knowledge structures and temporal features to dynamically capture learners' knowledge states, which serve as environment states for long-term reward-driven path optimisation. Experiments on ASSISTments 2017 and EdNet-KT4 show that MFFKT achieves AUC scores of 0.834 and 0.812, surpassing baseline models. Ablation studies validate the efficacy of multi-dimensional feature fusion. When combined with conservative Q-learning, the methods outperform greedy, rule-based, and random strategies in cumulative reward, completion rate, and efficiency. Overall, the proposed framework enables coordinated modelling of learning states and learning path decisions, providing an effective technical approach for adaptive and personalised English learning within digital learning environments. Inderscience Publishers - linking academia, business and industry through research

Title: Personalised learning path optimisation in digital English learning environments via multi-factor knowledge tracing and reinforcement learning

Authors: Jie Chen

Addresses: Experimental College, Ningbo Open University, Ningbo, 315016, Zhejiang, China

Abstract: Digital English learning environments generate massive interaction data, offering potential for adaptive learning path optimisation. However, many existing approaches treat learning state estimation and learning path recommendation as separate tasks, restricting long-term personalised learning support. This study proposes a framework that integrates a multi-factor fusion knowledge tracing (MFFKT) model with reinforcement learning. It jointly analyses behaviour sequences, item attributes, knowledge structures and temporal features to dynamically capture learners' knowledge states, which serve as environment states for long-term reward-driven path optimisation. Experiments on ASSISTments 2017 and EdNet-KT4 show that MFFKT achieves AUC scores of 0.834 and 0.812, surpassing baseline models. Ablation studies validate the efficacy of multi-dimensional feature fusion. When combined with conservative Q-learning, the methods outperform greedy, rule-based, and random strategies in cumulative reward, completion rate, and efficiency. Overall, the proposed framework enables coordinated modelling of learning states and learning path decisions, providing an effective technical approach for adaptive and personalised English learning within digital learning environments.

Keywords: multi-factor fusion knowledge tracing; MFFKT; RL; digital English learning; personalisation; sequential decision-making.

DOI: 10.1504/IJICT.2026.153797

International Journal of Information and Communication Technology, 2026 Vol.27 No.55, pp.24 - 42

Received: 19 Jan 2026
Accepted: 28 Feb 2026
Published online: 26 May 2026 *

Title: Personalised learning path optimisation in digital English learning environments via multi-factor knowledge tracing and reinforcement learning

Keep up-to-date