What question did this study set out to answer?

This research aims to enhance the adaptiveness and personalization of digital teaching methods in tourism management education using deep reinforcement learning.

March 15, 2026Discover Artificial IntelligenceOpen Access

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

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XWXiali WangLuoyang Normal University

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Implication

Research explores adaptive digital teaching methods in tourism management, suggesting improved learning outcomes through advanced techniques.

Key Points

This research aims to enhance the adaptiveness and personalization of digital teaching methods in tourism management education using deep reinforcement learning.
Utilized a dataset containing student interaction features like attendance, test scores, and participation.
Applied min–max normalization for consistent feature scaling.
Developed the Intelligent Lévy Flight Distribution-driven Double Deep Q-Network for analysis of learning behaviors.
Employed Double DQN to predict optimal teaching interventions and provide targeted feedback.
Achieved accuracy of 98.23% and precision of 98.33% with the ILFD-Double DQN model.
Maintained low prediction errors ranging from 0.032 to 0.057.
Significantly reduced the time needed for personalized guidance over traditional teaching methods.

Cite This Study

Xiali Wang (2026) studied this question.

synapsesocial.com/papers/69b64d5cb42794e3e660e2f0 https://doi.org/10.1007/s44163-026-01110-9

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Implication

Research explores adaptive digital teaching methods in tourism management, suggesting improved learning outcomes through advanced techniques.

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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