Banking on adaptive questions to nudge student responsibility for learning in general chemistry





Citation of Original Publication

Penniston, Tara Carpenter, John Fritz, Thomas. “Banking on Adaptive Questions to Nudge Student Responsibility for Learning in General Chemistry.” In Data Analytics and Adaptive Learning. Routledge, 2023.


This is an Accepted Manuscript of a book chapter published by Routledge/CRC Press in Data Analytics and Adaptive Learning on 2023, available online:
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In this case study from the University of Maryland, Baltimore County (UMBC), we explore if and how students can be nudged to take responsibility for their learning through one of the university’s largest courses, CHEM 102 “Principles of Chemistry II.” To do so, Dr. Tara Carpenter leveraged the campus’ Blackboard learning management system (LMS) in Spring 2021, and then added the RealizeIt adaptive learning platform in Fall 2021 and Spring 2022 to implement a pedagogy of “spaced practice,” in which students have time to study, forget, reacquire, and reorganize new knowledge or content. Specifically, Carpenter leveraged large pools or “banks” of questions to guide students in their “time on task” practice and application of key concepts needed to perform well on high-stakes, summative exams. Overall, in comparing CHEM 102 final grade data between Fall 2020 and Fall 2021, we see there is not a statistically significant relationship between the treatment (i.e., course design) and reduced DFW (drop, fail, or withdraw) rates. However, if we disaggregate final grade data, we see there is an overall statistically significant increase in As (p<.01) and decrease in Cs (p <.05) and Ds (p<.05). Notably, all of this gain from increasing As appears to be from students of color (SOC), who demonstrate a nearly 4x advantage over their non-redesigned course peers in attaining this grade (p<.001), while White students demonstrated no statistically significant gain in this area. Based solely on whether students used the spaced practice environment Carpenter designed, we also see that a model predicting final grades after only 14 days into the semester is 83% accurate.