Curriculum Mapping Across the Disciplines: Differences, Approaches, and Strategies
Bibliographic record
Abstract
Curriculum mapping can be used to document, align, visualize, and assess curricular data, such as learning outcomes, assessment materials, instructional techniques, and student pre- and post-testing scores. A cross-disciplinary Curriculum Mapping Initiative currently underway at the University of Toronto Mississauga aims to: (1) develop guidelines for the curriculum mapping process; (2) develop cross-disciplinary curriculum mapping templates and samples to guide departments through the curriculum mapping process; (3) communicate narratives for how to use curriculum mapping to inform curricular change; (4) develop visualization strategies for curricular data; (5) initiate a plan for dissemination and sustainability; and (6) initiate a plan for informing students about how to use curricular maps in their academic experiences. Through this curriculum mapping initiative, we have discovered that discipline-specific differences exist in approaches to curriculum mapping. The purpose of this paper is to communicate these cross-disciplinary similarities and differences in purpose, process, and utilization of curriculum mapping strategies. We found that different departments had some common ground in the curriculum mapping process, but also key differences. The differences could be categorized according to: purpose for initiating the curriculum mapping process; approach to curriculum mapping; dissemination of completed maps; dealing with pedagogical jargon; and faculty buy-in.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.079 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".