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arXiv physics/0702111physics.ed-ph
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基于术语联想时间的物理学生概念知识结构测量与建模

Measuring and modeling physics students' conceptual knowledge structures through term association times

Ian D. Beatty, William J. Gerace, Robert J. Dufresne

摘要:

传统的以问题为基础的考试不是评估物理学生概念知识的“结构”或向学生和教师提供诊断性详细反馈的有效工具。我们提出了自由术语条目任务,候选人的评估工具,探索概念之间的联系,在学生的理解一个主题。在这个任务中,学生被给予一个一般的主题区域,并要求在给定的时间内回答尽可能多的主题区域的术语;每个术语输入事件之间的“思考时间”与响应术语一起记录。这项任务是给两个不同的物理导论班的学生做的。反应术语的思考时间被发现与两个概念之间的关联强度相关。此外,从任务的思考时间集显示不同的,有特色的模式,这可能被证明是有价值的学生评估。我们提出了一个定量的动力学模型,称为矩阵行走模型,它是能够匹配的观测数据的许多方面。数据的一个特殊特征--一个明显的“尖峰”叠加在大多数思维时间集的对数正态分布上--并没有被拟合。尖峰,在数据中观察到的其他模式,以及提出的现象学模型都可以从认知理论的基础中受益。

英文摘要:

Traditional problem-based exams are not efficient instruments for assessing the "structure" of physics students' conceptual knowledge or for providing diagnostically detailed feedback to students and teachers. We present the Free Term Entry task, a candidate assessment instrument for exploring the connections between concepts in a student's understanding of a subject. In this task, a student is given a general topic area and asked to respond with as many terms from the topic area as possible in a given time; the "thinking time" between each term-entry event is recorded along with the response terms. The task was given to students from two different introductory physics classes. Response term thinking times were found to correlate with the strength of the association between two concepts. In addition, sets of thinking times from the task show distinct, characteristic patterns which might prove valuable for student assessment. We propose a quantitative dynamical model named the Matrix Walk Model which is able to match many aspects of the observed data. One particular feature of the data -- a distinct "spike" superimposed on the otherwise log-normal distribution of most thinking time sets -- has not been fit. The spike, other patterns observed in the data, and the proposed phenomenological model could all benefit from a grounding in cognitive theory.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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