基于改进的LSTM神经网络的动态教育推系统
Hadis Ahmadian Yazdi1, Seyyed Javad Seyyed Mahdavi2, Hooman Ahmadian Yazdi3
1Department of Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran.
Scientific reports
|February 22, 2024
概括
这项研究介绍了一种教育推系统,可以跟踪学生在虚拟学习环境中的行为. 该系统通过考虑用户的长期和短期利益,有效地建议资源,改善学习体验.
科学领域:
- 教育技术的教育技术
- 人与计算机的交互
- 人工智能的人工智能
背景情况:
- 虚拟学习环境越来越普遍,需要有效的资源发现.
- 来自人机交互的学生行为数据为学习偏好提供了洞察力.
- 推适当的教育资源是具有挑战性的,因为用户的兴趣是多样化和不断变化的.
研究的目的:
- 设计一个针对个体学习者的兴趣而定制的教育建议系统.
- 应对包括短期利益在内的各种用户偏好的挑战.
- 提高教育资源建议的准确性和相关性.
主要方法:
- 该系统分析了通过人机交互记录的学生行为.
- 它采用了一个考虑用户长期 (历史) 和短期 (当前) 利益的模型.
- 双向长期短期记忆 (BiLSTM) 网络被利用它们的渐进式学习能力来适应行为变化.
主要成果:
- 拟议的推系统实现了0.9978.8的平均准确性.
- 记录了0.0051的低损失值,表明了高性能.
- 与类似的现有工程相比,该系统表现出优异的推性能.
结论:
- 开发的教育推系统通过整合长期和短期用户兴趣建模,有效地建议相关资源.
- 使用BiLSTM网络使模型能够适应学习者行为的动态变化.
- 这种方法通过提供个性化的教育内容来提高虚拟学习环境的有效性.
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