Reworking exam problems to incentivize improved performance in upper-division electrodynamics

Andrew J. Mason, J. Colton
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Abstract

: A previous study showed that incentivizing students to correct mistakes on unit exam problems within an upper-division quantum mechanics course improved students’ problem-solving efforts on those same problems in a final exam environment, relative to a comparison group of students who were not incentivized. We attempt to replicate the quantitative portion of this study within a first-semester upper-division electromagnetism course, specifically examining students’ invoking correct concepts and applying those concepts correctly. A statistical comparison of students who accepted the offer to rework unit exam problems for partial credit, versus students who declined the offer, demonstrates a better improvement for students who chose to rework relative to students who declined. As the results suggested that unit exam performance might provide a covariate within the comparison of choice to rework between groups, the results were analyzed using ANCOVA; to understand the effect size, a pre-post normalized gain comparison was also made; statistical results were consistent across both measurements. Results additionally appear to show that incentivization works more specifically for invoking correct concepts on a primarily conceptual problem, and more specifically for applying concepts correctly on a primarily algorithmic problem. Future plans include a more complete analytical framework using think-aloud protocol interviews for students from the sample, as well as more statistical detail to determine the interaction between unit exam score and choice to rework problems.
修改考试题目以激励学生在高年级电动力学方面的表现
先前的一项研究表明,在高年级量子力学课程中,鼓励学生纠正单元考试中的错误,与没有受到激励的学生相比,可以提高学生在期末考试中解决相同问题的能力。我们试图在第一学期的高年级电磁学课程中复制本研究的定量部分,特别是检查学生调用正确概念并正确应用这些概念的情况。接受返工单元考试问题以获得部分学分的学生与拒绝返工的学生的统计比较表明,选择返工的学生比拒绝返工的学生有更好的进步。由于结果表明,单元考试成绩可能在组间返工选择比较中提供协变量,因此使用ANCOVA对结果进行分析;为了了解效应大小,还进行了前后归一化增益比较;两种测量的统计结果是一致的。结果还表明,激励更具体地适用于在主要概念问题上调用正确的概念,更具体地适用于在主要算法问题上正确应用概念。未来的计划包括一个更完整的分析框架,使用对样本学生的有声思考协议访谈,以及更多的统计细节,以确定单元考试成绩和选择返工问题之间的相互作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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