Bibliographic record
Abstract
First-year students often struggle to learn the material in university calculus courses. One of the reasons for this may be that students lack a solid grounding in the fundamental mathematical skills required to succeed in these courses. To address this issue, the Math & Statistics Learning Centre at the University of Toronto Scarborough has developed 12 online modules for undergraduate students looking to improve their skills in mathematics fundamentals. Modules 1–8 cover foundational concepts, and Modules 9–12 cover advanced concepts. For upper-level mathematics courses, we have developed a journal called “Math In Action” which provides undergraduates with a platform to share their work. Math In Action could be used for research assignments in senior undergraduate and graduate mathematics courses. The journal will give researchers the opportunity to interact with students and inspire greater interest in mathematical research, with the hope of creating stronger generations of future researchers. This chapter outlines the design and implementation of the various components of these learning modules and Math In Action Journal.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.000 |
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 teacher head, 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".