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首页> 外文期刊>CBE Life Sciences Education >GenBio-MAPS as a Case Study to Understand and Address the Effects of Test-Taking Motivation in Low-Stakes Program Assessments
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GenBio-MAPS as a Case Study to Understand and Address the Effects of Test-Taking Motivation in Low-Stakes Program Assessments

机译：Genbio-Maps作为理解和解决在低赌注计划评估中测试动机的影响的案例研究

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摘要

The General Biology–Measuring Achievement and Progression in Science (GenBio-MAPS) assessment measures student understanding of the Vision and Change core concepts at the beginning, middle, and end of undergraduate biology degree programs. Assessment coordinators typically administer this instrument as a low-stakes assignment for which students receive participation credit. While these conditions can elicit high participation rates, it remains unclear how to best measure and account for potential variation in the amount of effort students give to the assessment. To better understand student test-taking motivation, we analyzed GenBio-MAPS data from more than 8000 students at 20 institutions. While the majority of students give acceptable effort, some students exhibited behaviors associated with low motivation, such as low self-reported effort, short test completion time, and high levels of rapid-selection behavior on test questions. Standard least-squares regression models revealed that students’ self-reported effort predicts their observable time-based behaviors and that these motivation indices predict students’ GenBio-MAPS scores. Furthermore, we observed that test-taking behaviors and performance change as students progress through the assessment. We provide recommendations for identifying and filtering out data from students with low test-taking motivation so that the filtered data set better represents student understanding.
机译：科学（Genbio-Maps）评估的一般生物学衡量成就和进展措施衡量学生对大学生生物学学位计划开始，中间和结束时的愿景和改变核心概念。评估协调员通常将本乐器作为低赌注分配管理，学生接受参与信贷。虽然这些条件可以引起高度参与率，但仍然不清楚如何最佳衡量标准和占学生捐赠评估金额的潜在变化。为了更好地了解学生测试的动机，我们在20个机构分析了Genbio-Maps数据从8000多名学生。虽然大多数学生提供了可接受的努力，但一些学生表现出与低动力相关的行为，例如低自我报告的努力，短暂的测试完成时间和高水平的测试问题的快速选择行为。标准最小二乘回归模型显示，学生的自我报告的工作预测了他们可观察的时间的行为，并且这些动机指数预测学生的GenBio-Maps得分。此外，随着学生通过评估的进步，我们观察到考虑的行为和性能变化。我们提供了建议，用于识别和过滤从测试拍摄动机的学生识别和过滤的数据，以便过滤数据集更好地代表学生了解。

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