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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
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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Cell Biology
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8734388/ https://www.ncbi.nlm.nih.gov/pubmed/33797284 http://dx.doi.org/10.1187/cbe.20-10-0243 |
Sumario: | 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. |
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