Investigating the Impact of Online Homework Reminders Using Randomized A/B Comparisons
Bibliographic record
Abstract
Procrastination by students may lead to adverse outcomes such as a focus on completion rather than learning or even a failure to complete learning tasks. One common method for motivating students and reducing procrastination is to send reminders with hints and study strategies, but it's not clear if these messages are effective or when is the best time to send them. Randomized A/B comparisons could be used to try different reminders or alternative ideas about how best to get students to start work earlier and, crucially, to measure the impact of these interventions on behaviour. This paper describes an A/B comparison of reminder emails set in a large CS1 course at a research-focused North American university. We found evidence that the email interventions caused a higher proportion of students to attempt the online homework but did not see evidence that these particular emails got students to start early, irrespective of changes to the timing of the reminder. More broadly, these findings illustrate how to use A/B comparisons in educational settings to test ideas about how to help students, and demonstrate the value of using randomized A/B comparisons, even when evaluating actions that seem obviously beneficial, such as reminder emails.
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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 | 0.065 | 0.118 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".