Behavioral Consequences of Reminder Emails on Students’ Academic Performance: a Real-world Deployment
Bibliographic record
Abstract
Post-secondary institutions have experienced a continuous trend of student procrastination on course work, thus leading to lower academic performance and potentially worse knowledge retention and other longer-term impacts. Sending reminders about deliverables is a simple approach, but it has the potential to be a valuable tool to mitigate such issues and assist students with time management. This paper will introduce specific processes of conducting such experiments, especially email designs and randomization, to help instructors and researchers conduct similar experiments or field-deploying reminders. To evaluate homework reminder messages, we designed and deployed a real-world randomized A/B experiment at a North American university in a CS1 course where students were randomly assigned to either receive these reminder messages or not. Our findings suggest that students who received the reminder messages have a higher homework completion rate (p < .05) and performed significantly better (p < .01) on the following midterm test than students who did not receive the reminder message. Finally, we discuss how a homework reminder can improve student behaviours, as well as how this type of reminder can be enhanced for future interventions.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".