Systemic Shifts in Instructional Technology: Findings of a Comparative Case Study of Two University Mathematics Departments
Bibliographic record
Abstract
This paper reports on the findings of an international case study in which researchers examined two mathematics departments (Canada/UK) in which the sustained use of technology was strategically established in a mathematics degree program. This case study forms part of a larger research initiative which involved an extensive literature review (Marshall, Jarvis, Lavicza and Buteau, 2012) and a national survey of Canadian Mathematicians (Buteau, Jarvis and Lavicza, 2014). Findings from the case study indicate that sustained implementation at the departmental level requires a unique combination of key factors such as: a dedicated core group led by a committed advocate in a position of influence/power; a strong and shared incentive for change; strategic hiring processes; an administration which supports creative pedagogical reform and wellconsidered risk-taking; and, a continuous and determined revisiting of the original vision and purpose. Significant challenges to implementation and sustained program development, with specific examples, are also discussed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".