发表机构
School of Education, Iowa State University; Department of Physics, Montana State University; School of Engineering Education, Purdue University; College of Education, Purdue University（爱荷华州立大学教育学院; 蒙大拿州立大学物理系; 普渡大学工程学院; 普渡大学教育学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对入门物理中固定长度评估的回顾性问题，提出力学认知诊断（MCD），利用证据中心设计和DINA模型，基于现有RBA构建14个学习目标的题库，实现教学过程中细粒度掌握情况的实时诊断。
AI 中文摘要
物理课程使用基于研究的评估（RBA），如力概念量表（FCI）、力和运动概念评估（FMCE）以及能量和动量概念调查（EMCS），来衡量入门力学中的学习效果，但这些评估的固定长度、前测-后测设计使其具有回顾性：后测分数总结了已完成的教学，并在课程结束后才得出。我们正在开发力学认知诊断（MCD），这是一种认知诊断计算机化自适应测试，可在教学过程中报告学生对细粒度学习目标（LOs）的掌握情况。采用以证据为中心的设计，我们从入门力学教科书和AP物理标准中定义了14个学习目标，使用Q矩阵将FCI、FMCE和EMCS项目映射到这些目标上，并使用确定性输入、噪声“与”门（DINA）模型，利用来自79所机构807门课程中24,394名学生的后测反应（通过LASSO）来优化映射。FCI和EMCS达到了良好的DINA模型拟合；FMCE显示出边缘拟合。大多数学习目标的分类准确性达到或超过了低风险形成性评估的基准。RBA项目虽然并非为学习目标层面的诊断而开发，但能可靠地支持这一诊断，为MCD提供了一个基于物理课程已使用的RBA构建的、包含14个学习目标的可用题库。随着数据的积累，我们可以修改或淘汰薄弱的学习目标和项目，并通过在线校准添加新项目，而无需中断测试。我们计划将MCD扩展到35个学习目标，每周两个，以覆盖典型的入门力学课程。
英文摘要
Physics courses use research-based assessments (RBAs) such as the Force Concept Inventory (FCI), Force and Motion Conceptual Evaluation (FMCE), and Energy and Momentum Conceptual Survey (EMCS) to measure learning in introductory mechanics, but their fixed-length, pretest-posttest design makes them retrospective: posttest scores summarize completed instruction and arrive after a course ends. We are developing the Mechanics Cognitive Diagnostic (MCD), a cognitive diagnostic computerized adaptive test that reports students' mastery of fine-grained learning objectives (LOs) throughout instruction. Using evidence-centered design, we defined 14 LOs from introductory mechanics textbooks and AP Physics standards, mapped FCI, FMCE, and EMCS items onto them with a Q-matrix, and refined the mapping with the deterministic inputs, noisy "and" gate (DINA) model, using posttest responses from 24,394 students in 807 courses across 79 institutions through LASSO. The FCI and EMCS achieved good DINA model fit; the FMCE showed marginal fit. Classification accuracy for most LOs met or exceeded benchmarks for low-stakes formative assessment. RBA items, though not developed for LO-level diagnosis, support it reliably, giving the MCD a working 14-LO item bank built from RBAs that physics courses already use. As data accumulate, we can revise or retire weak LOs and items and add new items through online calibration without interrupting testing. We plan to expand the MCD to 35 LOs, two per week, to cover a typical introductory mechanics course.
Comments16 pages, 9 tables, and 3 figures. This is a preprint intended for submission to Physical Review Physics Education Research
