一个针对英语练习的个性化推算法,包含模糊的认知模型和多个注意力机制
1Yangzhou University, Yangzhou, China. yzzhangyx_master@163.com.
Scientific reports
|April 4, 2025
概括
这项研究引入了一种新的算法,用于在高等教育中提供个性化的英语学习建议. 它通过使用认知诊断和注意力机制预测学生表现来改善运动准.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 认知科学 认知科学
背景情况:
- 数字教育在学习系统中面临着资源分配和个性化挑战.
- 现有的运动推系统往往缺乏针对学生的有针对性的指导.
- 高等教育英语学习需要改进的方法来推阅读,写作和翻译练习.
研究的目的:
- 开发一种算法来预测学生在推的英语练习中的表现.
- 解决当前数字学习资源建议中缺乏个性化的问题.
- 提高在高等教育中在线英语学习的有效性.
主要方法:
- 使用学生响应时间,连续响应时间和知识差距构建了一个改进的模糊认知诊断模型.
- 采用多头注意力机制来权衡学生过去的知识状态与当前的知识状态.
- 利用实验数据来验证拟议算法的性能.
主要成果:
- 开发的算法在预测学生在未来练习中的表现方面表现得很好.
- 模糊认知诊断和多头注意力的结合提供了个性化的运动建议.
- 整合了主观和客观方面,以确定英语知识的认知属性.
结论:
- 拟议的算法提供了一个有针对性和个性化的方法来推高等教育中的英语练习.
- 这种方法解决了当前数字教育实践推系统的关键局限性.
- 这些发现有助于推进智能辅导系统和个性化的学习途径.
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