Personalized learning path planning and optimization methods of vocational education combined with DQN

Abstract

Personalized learning path (PLP) planning in vocational education faces significant challenges, including static designs and an inability to efficiently process complex state spaces or provide real-time feedback. To address these limitations, this paper introduces the deep Q network (DQN) algorithm, enabling dynamic and intelligent PLP planning that adapts to learners’ evolving needs and enhances learning outcomes. The study begins by extracting multi-dimensional features of learners from historical learning data, personal background information, learning interests, and progress feedback. These features are then integrated with learning modules and goals to construct a comprehensive state space for PLP. Leveraging the DQN algorithm, the model optimizes learning paths through reinforcement learning’s reward mechanism, dynamically adjusting PLPs based on real-time learner feedback and feature changes. The results demonstrate the superiority of the DQN algorithm in achieving personalized learning. On average, the DQN algorithm adjusts each learner’s plan 7.8 times, significantly outperforming traditional methods such as collaborative filtering (CF) and particle swarm optimization (PSO). Compared to CF and PSO, the DQN algorithm increases the average number of plan adjustments by 2.4 and 2.0 times, respectively. This highlights its ability to provide real-time, adaptive solutions tailored to individual learners’ needs. By overcoming the static nature of conventional PLP approaches, the DQN-based model not only improves learning efficiency and engagement but also sets a new benchmark for intelligent educational systems. This research underscores the transformative potential of AI-driven methodologies in vocational education, paving the way for more adaptive, scalable, and effective learning solutions.