Bibliographic record
Abstract
Recent studies show that programs offering structured, one-on-one coaching and tutoring tend to have large effects on the academic outcomes of both high school and college students. These programs are often costly to implement and difficult to scale, however, calling into question whether making them available to large student populations is feasible. In contrast, interventions that rely on technology to maintain low-touch contact with students can be implemented at large scale and minimal cost but with the risk of not being as effective as one-on-one, in-person assistance. In this paper, we test whether the effects of coaching programs can be replicated at scale by using technology to reach a larger population of students. We work with a sample of over four thousand undergraduate students from a large Canadian university, randomly assigning students into one of the following three interventions: (i) a one-time online exercise designed to affirm students' values and goals; (ii) a text messaging campaign that provides students with academic advice, information, and motivation; and (iii) a personal coaching service, in which students are matched with upper-year undergraduate coaches. We find large positive effects from the coaching program, as coached students realize a 0.3 standard deviation increase in average grades and a 0.35 standard deviation increase in GPA. In contrast, we find no effects from either the online exercise or the text messaging campaign on any academic outcome, both in the general student population and across several student subgroups. A comparison of the key features of the text messaging campaign and the coaching service suggests that proactively and regularly initiating conversations with students and working to establish trust are important design features to incorporate in future interventions that use technology to reach large populations of students.
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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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".