A University Math Help Centre as a Support Framework for Students, the Instructor, the Course, and the Department
Bibliographic record
Abstract
Among many challenges a math department at a post-secondary institution will most likely be faced with the optimization problem of how best to offer out-of-lecture learning support to several thousand first- and second-year university students enrolled in large math service courses within given spatial, scheduling, financial, technological, and manpower resource constraints, and at the same time ease the administrative work of the instructor. This article describes how math workshops, essentially math help centres, are set up in the Department of Mathematics at Simon Fraser University so that they provide the administrative and learning support structure for the students, the instructor, the course, and the department. The roles and responsibilities of the workshop coordinator, instructors, teaching assistants, and students are outlined along with a discussion of the challenges and benefits of this support framework.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".