USING A MIDTERM WARNING SYSTEM TO IMPROVE STUDENT PERFORMANCE AND ENGAGEMENT IN AN INTRODUCTORY STATISTICS COURSE: A RANDOMIZED CONTROLLED TRIAL
Bibliographic record
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".