An evaluation of Crescent School vLearning – an online peer-tutoring program
Bibliographic record
Abstract
Abstract Tutoring is often a useful supplement to traditional classroom teaching in Canada. Cross-age tutoring, which involves a tutor a few years older than a tutee, has been reported to be more effective than same-age tutoring, as it promotes responsibility, empowerment and academic performance. However, the current same-age classroom teaching may act as a barrier to cross-age tutoring because the latter requires plenty of coordination, preparation and organization. At Crescent School, an all-boys independent school in Toronto, Canada, a pilot online cross-age peer-tutoring program was launched in September 2014, named Crescent School vLearning. The purpose of this study was to formally assess the program, and quantitatively gauge its success. Thirty-six questions were randomly selected from the vLearning website, examined for response time and response quality as assessed by students and teachers. The fast response times as well as the high-quality of responses have resulted in the program gaining traction in the school. As vLearning continues to catch-on with students, the team of Upper School tutors will soon need to be expanded to accommodate the increasing volume of questions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".