Effect of Personalized Email-Based Reminders on Participants’ Timeliness in an Online Education Program: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Postsecondary students need to be able to handle self-regulated learning and manage schedules set by instructors. This is particularly the case with online courses, as they often come with a limited number of social reminders and less information directly from the teacher compared to courses with physical presence. This may increase procrastination and reduce timeliness of the students. Reminders may be a tool to improve the timeliness of students' study behavior, but previous research shows that the effect of reminders differs between types of reminders, whether the reminder is personalized or general, and depending on the background of the students. In the worst cases, reminders can even increase procrastination. OBJECTIVE: The aim of this study was to test if personalized email reminders, as compared to general email reminders, affect the time to completion of scheduled online coursework. The personalized reminders included information on which page in the online material the participants ought to be on at the present point in time and the last page they were on during their last session. The general reminders only contained the first part of this information: where they ought to be at the present point in time. METHODS: Weekly email reminders were sent to all participants enrolled in an online program, which included 39 professional learners from three East African countries. All participants in the Online Education for Leaders in Nutrition and Sustainability program, which uses a question-based learning methodology, were randomly assigned to either personalized or general reminders. The structure of the study was AB-BA, so that group A received personalized reminders for the first unit, then general reminders for the rest of the course, while group B started with general reminders and received personalized reminders only in the third (and last) unit in the course. RESULTS: In total, 585 email reminders were distributed, of which 390 were general reminders and 195 were personalized. A Bayesian mixed-effects logistic regression was used to estimate the difference in the probability of being on time with one's studies. The probability of being on time was 14 percentage points (95% credible interval 3%-25%) higher following personalized reminders compared to that following general reminders. For a course with 100 participants, this means 14 more students would be on time. CONCLUSIONS: Personalized reminders had a greater positive effect than general reminders for a group of adults working full-time while enrolled in our online educational program. Considering how small the intervention was-adding a few words with the page number the student ought to be on to a reminder-we consider this effect fairly substantial. This intervention could be repeated manually by anyone and in large courses with some basic programming.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".