USING A MIDTERM WARNING SYSTEM TO IMPROVE STUDENT PERFORMANCE AND ENGAGEMENT IN AN INTRODUCTORY STATISTICS COURSE: A RANDOMIZED CONTROLLED TRIAL

Authors

  • NOOSHIN ROTONDI Ontario Tech University
  • DAVID RUDOLER Ontario Tech University
  • WILLIAM HUNTER Ontario Tech University
  • OLAYINKA SANUSI Ontario Tech University
  • CHRIS COLLIER Ontario Tech University
  • MICHAEL ROTONDI York University

DOI:

https://doi.org/10.52041/serj.v22i3.406

Keywords:

Statistics education research, Student engagement, e-mail nudges, Introductory statistics, Randomized controlled trial

Abstract

This article reports on an  evaluation the effectiveness of e-mailed grade “nudges” on students’ performance and engagement in an introductory statistics course for undergraduate health science students. In 2020–2021, 358 students were randomized to an e-mail (n = 178) or no e-mail (n = 180) group. The intervention e-mail contained information on each student’s predicted final grade (grade nudge). Using two-sample t-tests, the statistical analysis of final grades in the course showed a higher compatibility with a model of no mean difference for students in the e-mail (73.5%) vs. no e-mail (72.1%) group. Comparison of the distributions of final grades between the two groups, however, suggested the e-mailed nudges may be related to slight improvements in final grades. Specifically, the median final grade was higher in the e-mail group (74.6 vs. 72.4); the Q1 value in the e-mail group was also higher, and the interquartile range was similar: no e-mail group (15.8) vs. e-mail group (14.2). Students also completed the Scale of Student Engagement in Statistics (SSE-S). Total engagement, affective and cognitive subscale scores of the SSE-S were higher in the e-mail group, resulting in low compatibility with a model of no difference in engagement scores. Overall, the results showed there is potential for our midterm warning system to be used to improve outcomes, particularly given that it is simple to implement, cost-effective, and easily scalable.

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Published

2023-11-30

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Section

Regular Articles