概括
本研究引入了用于教育评估的诊断面状况模型 (DFSM),通过分析所有响应选项来增强诊断测试. 新模型准确地识别了学生对学习目标和中间技能的理解.
科学领域:
- 教育测量教育的测量
- 心理测量 心理测量 心理测量
- 认知科学 认知科学
背景情况:
- 现代教育改革需要改进诊断测试.
- 当前的方法往往无法捕捉对学习至关重要的中间理解.
- 对评估结果的准确解释对于有针对性的教学至关重要.
研究的目的:
- 开发一种新的心理测量模型,即诊断面状况模型 (DFSM),用于增强诊断评估.
- 同时模拟目标和中间学生的理解.
- 使用所有响应选项,包括不正确的,以获得更细致的诊断信息.
主要方法:
- 开发了诊断面状况模型 (DFSM),是一种认知诊断模型 (CDM).
- 采用规范期望最大化算法 (REM) 来进行模型估计,并包含对稀疏性的日志惩罚.
- 提出了一个启发式算法,从估计的允许隐性类推断面图.
主要成果:
- 规范期望最大化算法 (REM) 有效估计了模型参数,并在模拟中确定了允许的潜在类.
- DFSM成功地模拟了理解的目标和中间方面.
- 对应答案选择的分析,而不仅仅是对/错,提供了独特的诊断见解.
结论:
- 诊断面状况模型 (DFSM) 提供了一种更细致的教育评估方法.
- 拟议的REM算法对于估计DFSM参数是有效的.
- 该模型证明了对现实世界数据分析的可行性,提高了诊断准确度.
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