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<p>The rapid advancement of digital technologies has transformed tourism management education, creating opportunities to enhance teaching effectiveness and student engagement. However, current digital teaching approaches suffer from low adaptiveness and weak personalization. To overcome these limitations, this research explores the optimization of digital teaching methods in tourism management courses through the application of Deep Reinforcement Learning (DRL). A student interaction dataset is used, containing features such as attendance, test scores, and participation, enabling accurate modeling of learning behaviors. Min–max normalization scales these features, ensuring consistency and effective training for the model. The proposed framework, the Intelligent Lévy Flight Distribution-driven Double Deep Q-Network (ILFD-Double DQN) model, is used to analyze student interactions, learning behaviors, and performance metrics in a digital learning environment. Double DQN is employed to analyze student behaviors, predict optimal teaching interventions, and provide targeted feedback for improved learning outcomes. The Intelligent ILFD uses a Quasi Opposition Based strategy that enhances exploration, enabling dynamic adaptation of content, strategies, and resources for effective, real-time adaptive learning. Experimental evaluation demonstrates that the ILFD-Double DQN outperforms traditional digital teaching approaches, achieving accuracy at 98.23% and precision at 98.33%. The model maintains low prediction errors, ranging between 0.032 and 0.057, while significantly reducing the time required for personalized guidance. These findings highlight the potential of DRL to create a responsive, adaptive, and efficient digital learning environment in tourism management education. It provides actionable insights for educators and institutions seeking to integrate intelligent, data-driven strategies into teaching practices, ultimately enhancing student competence, employability, and readiness for industry demands.</p>

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Optimization of digital teaching methods for tourism management based on deep reinforcement learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月13日

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  • Xiaomin Wang

摘要

The rapid advancement of digital technologies has transformed tourism management education, creating opportunities to enhance teaching effectiveness and student engagement. However, current digital teaching approaches suffer from low adaptiveness and weak personalization. To overcome these limitations, this research explores the optimization of digital teaching methods in tourism management courses through the application of Deep Reinforcement Learning (DRL). A student interaction dataset is used, containing features such as attendance, test scores, and participation, enabling accurate modeling of learning behaviors. Min–max normalization scales these features, ensuring consistency and effective training for the model. The proposed framework, the Intelligent Lévy Flight Distribution-driven Double Deep Q-Network (ILFD-Double DQN) model, is used to analyze student interactions, learning behaviors, and performance metrics in a digital learning environment. Double DQN is employed to analyze student behaviors, predict optimal teaching interventions, and provide targeted feedback for improved learning outcomes. The Intelligent ILFD uses a Quasi Opposition Based strategy that enhances exploration, enabling dynamic adaptation of content, strategies, and resources for effective, real-time adaptive learning. Experimental evaluation demonstrates that the ILFD-Double DQN outperforms traditional digital teaching approaches, achieving accuracy at 98.23% and precision at 98.33%. The model maintains low prediction errors, ranging between 0.032 and 0.057, while significantly reducing the time required for personalized guidance. These findings highlight the potential of DRL to create a responsive, adaptive, and efficient digital learning environment in tourism management education. It provides actionable insights for educators and institutions seeking to integrate intelligent, data-driven strategies into teaching practices, ultimately enhancing student competence, employability, and readiness for industry demands.