Tutor training across disciplines: expanding aid and enabling student entrepreneurship
Bibliographic record
Abstract
As learning developers, we frequently find ourselves constrained by institutional structures and disciplinary areas of expertise. At just one of the University of Toronto’s three campuses, we have no fewer than five distinct undergraduate faculties, thereby creating a complex ecosystem that compounds the difficulty of supporting all students, even beyond the issue of understanding the expectations of multiple programs of study. While the Centre for Learning Strategy Support has dispersed members of our team across the St. George campus to focus on distinct student needs, varying funding and staffing structures has led to uneven support for our students in different disciplines, with many turning to external tutoring services of unreliable quality. While learning developers may be limited to their own education and experience, we are uniquely positioned as experts in teaching and learning: we may not always know the ins and outs of what students need to study, but we have valuable insights in how to do so. Over the past several years, we have built a curriculum of modules supporting effective and ethical peer-to-peer learning, based on strategies that are core to a learning developer’s work. Upon completing the University of Toronto Tutor Training Program, or UT3, and after securing the reference of a postsecondary subject matter expert, academically successful students are enabled to bring their discipline-specific knowledge to others as independent contractors on our Tutor Directory. With this combination of training and directory, we have built a new marketplace for all undergraduate students to find trustworthy tutors, and for our trainees to make money by supporting their peers. This presentation spotlighted the development and launch of the UT3, including the initial needs analysis and consultations, the creation of our curriculum, and our progress in training students to enable hundreds of tutoring sessions since our launch.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.004 | 0.039 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".