在学生的开放教学策略中应用基于卷积网络技术的移动学习系统
1Department of Performing Arts and Culture, Catholic University, Bucheon, Kyonggi-do, 14662, Republic of Korea.
Scientific reports
|November 25, 2025
概括
本研究介绍了一个TCN-DCC-RL模型,用于个性化的移动学习资源建议. 智能系统实现了高精度,增强了开放式教学策略.
科学领域:
- 人工智能的人工智能
- 教育技术的教育技术
- 机器学习 机器学习
背景情况:
- 开放式教学策略需要有效的个性化学习资源建议.
- 移动学习系统需要为各种用户需求提供智能支持.
- 现有的推系统往往缺乏个性化和适应性.
研究的目的:
- 设计和开发使用卷积神经网络的移动学习系统.
- 为个性化学习资源推提出一个先进的TCN-DCC-RL模型.
- 用真实世界的学习分析数据来评估模型的有效性.
主要方法:
- 开发一个与卷积神经网络集成的移动学习系统.
- 实施一个时间卷积网络 (TCN) 扩展因果卷积 (DCC) 和强化学习 (RL).
- 在英国开放大学的学习分析数据集上对TCN-DCC-RL模型的评估.
主要成果:
- TCN-DCC-RL模型展示了高性能指标:96.49%的准确性,90.57%的F1得分,92.27%的平均精度和0.938 NDCG@20.
- 在学习资源推的个性化和智能化方面显著增强.
- 在实际教育环境中验证模型的有效性.
结论:
- 拟议的TCN-DCC-RL模型为智能教育系统提供了一种新的技术方法.
- 该系统通过个性化建议有效支持开放式教学策略.
- 未来的智能教育系统可以从这种先进的个性化技术中受益.
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