assessment, quantitative reasoning requirement, learning analytics
We present two models for assessment of a large and diverse quantitative reasoning (QR) requirement at the University of Michigan. These approaches address two key challenges in assessment: (1) dissemination of findings for curricular improvement and (2) resource constraints associated with measurement of large programs. Approaches we present for data collection include convergent validation of self-report surveys, as well as use of mixed methods and learning analytics. Strategies we present for dissemination of findings include meetings with instructors to share data and best practices, sharing of results through social media, and use of easily accessible dashboards. These assessment approaches may be of particular interest to universities with large numbers of students engaging in a QR experience, projects that involve multiple courses with diverse instructional goals, or those who wish to promote evidence-based curricular improvement.
Wright, Mary C. and Howard, Joseph E.
"Assessment for Improvement: Two Models for Assessing a Large Quantitative Reasoning Requirement,"
1, Article 6.
Available at: http://scholarcommons.usf.edu/numeracy/vol8/iss1/art6
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Additional FilesWright and Howard, Numeracy 8(1), Appendix 1.pdf (50 kB)