通过认知表现优化改进问题嵌入知识追踪知识追踪
IEEE transactions on cybernetics
|October 8, 2025
概括
本研究介绍了用于知识追踪的认知表达优化 (CRO-KT) 模型. 它通过优化认知表征和计算学习数据中的干扰因素来改善学生绩效预测.
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:
- 现有的知识跟踪 (KT) 模型使用历史记录预测学生的表现,但往往忽略了像滑落和猜测这样的分心因素.
- 当前的KT模型中的静态认知表示是有限的,可能不准确地反映学生的动态理解.
- 这可能导致学生学习数据的协同作用和协调问题.
研究的目的:
- 提出一个新的认知表征优化知识追踪 (CRO-KT) 模型.
- 提高预测学生知识状态和未来绩效的准确性.
- 解决静态认知表征的局限性,并纳入分散注意力的影响.
主要方法:
- 使用动态编程算法来优化基于炼难度的认知表示的结构.
- 采用协同优化算法,通过考虑协同相关的练习来完善认知表征.
- 福斯从双分线图中学习了关系嵌入,并优化了记录表示,以获得更丰富的认知表达.
主要成果:
- CRO-KT模型在优化知识追踪的认知表征方面表现出有效性.
- 在三个公共数据集上的实验验证证证了该模型的卓越性能.
- 该方法通过整合各种优化技术来增强学生认知的表达.
结论:
- 拟议的CRO-KT模型在知识追踪方面取得了重大进展.
- 优化认知表征和考虑分心因素,可以更准确地预测学生的表现.
- 这项研究为理解和建模学生学习过程提供了更强大的框架.
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