Join us for AIM Research, where we host speakers working with data analytics and research to share their knowledge and engage with the University of Michigan research and learning analytics community. The field of learning analytics is a multi- and interdisciplinary field that brings together researchers from education, the learning sciences, computational sciences and statistics, and all discipline-specific forms of educational inquiry. Event details are listed below and registration is required. This virtual event will be hosted on Zoom.
AIM Research – Equity in Assessment for Large Undergraduate Courses
Montserrat B. Valdivia Medinaceli, Doctoral Candidate in Quantitative Research Methodology, Indiana University Bloomington
Bio: Montserrat is a doctoral candidate in the quantitative track of the Qualitative and Quantitative Research Methodology Ph.D. program at Indiana University Bloomington (IUB). She obtained a MS in Statistics in 2020 from IUB. Currently, she works as a graduate researcher at the Office of Analysis and Institutional Effectiveness Research and Analytics. Her main research interest is fairness in assessments when comparing heterogeneous populations. Her dissertation consists of three simulation studies focusing on the fairness and validity of international large-scale assessments (ILSAs) results under the implementation of adaptive testing designs. Her collaborations include studies improving item-by-group fit methods to detect item bias at the group level, and evaluating item bias effects in cognitive diagnostic models.
Abstract: In this presentation, I aim to showcase the importance of using item bias detection methods to improve equity in assessment. Large undergraduate S.T.E.M. and Business courses use assessments as an indicator of progress and achievement, and assessments tend to be highly weighted in the course’s final grade. Therefore, the implications of the results of these assessments are highly impactful on students’ progress and perception of ability, particularly when students belong to minoritized groups. I will present the results of applying various differential item functioning statistics to compare more- and less-privileged groups in a course final exam at a Midwest four-year university. I hope the results of this analysis help initiate a conversation about fairness in assessment in large undergraduate courses.
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