根据电子学习环境中的模糊云认知诊断框架预测考生表现
Hua Ma1,2, Zhuoxuan Huang1, Haibin Zhu3
1Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, China.
Soft computing
|June 26, 2023
概括
本研究引入了一个模糊云认知诊断框架 (FC-CDF) 来预测学生在电子学习中的表现. 这种新的方法提高了技能能力测量和诊断效率,以实现个性化的教育.
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:
- 认知诊断模型 (CDM) 评估学习者对绩效预测和个性化电子学习的技能.
- 现有的CDM面临着复杂技能,不确定的熟练程度和大数据集的局限性,影响测量和效率.
- 准确预测学习者表现对于有效的电子学习策略至关重要.
研究的目的:
- 提出一种新的模糊云认知诊断框架 (FC-CDF),用于预测电子学习环境中的考生表现.
- 解决现有CDM在测量技能能力和诊断效率方面的局限性.
- 通过准确的绩效预测,增强个人化电子学习指导的决策支持.
主要方法:
- 利用正常云模型 (NCM) 来测量学习者技能熟练度的期望,变化和频率.
- 将NCM转换为间隔模糊数字来表示技能熟练程度的不确定性.
- 综合教育心理学假设与参数估计以确定技能水平和项目滑落/猜测因子.
主要成果:
- 拟议的FC-CDF准确地预测了考生在电子学习中的表现.
- 与现有方法相比,这种方法显著减少了执行时间.
- 证明了用于认知诊断的改进的测量机制和诊断效率.
结论:
- FC-CDF为电子学习中的认知诊断和绩效预测提供了一个强大的框架.
- 这种方法有效地处理技能复杂性和学习者熟练度的不确定性.
- 这些发现支持在数字学习环境中加强个性化的教学和决策.
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