Analyzing Curriculum Mapping Data: Enhancing Student Learning through Curriculum Redesign
Bibliographic record
Abstract
Curriculum mapping (CM) is “a process in which the learning outcomes, teaching and learning strategies, and assessment processes for each course in a program can be represented to create a summary of the learning plan for an entire program of study so that the relationships between the components of the program can be observed” (University of Calgary, 2013, p. 3). Rather than seeing individual courses in isolation, curriculum mapping provides an opportunity to visualize the curriculum as an integrated whole (Spencer et al., 2012). Analyzing the resulting data can lead to meaningful discussions about the curriculum, what is working well, and what changes might be implemented in a curriculum redesign to enhance student learning experiences (Sumsion & Goodfellow, 2004; Uchiyama & Radin, 2009). In this hands-on workshop participants will examine and analyze curriculum mapping data outputs in large and small groups. We will collaboratively interpret curriculum mapping data, identifying program strengths and opportunities for improvement, and explore various ways in which CM data can be presented. By the end of the session, participants should be able to: • Interpret data from three different curriculum maps used as examples in the session • Identify strengths and opportunities for improvement in a curriculum redesign of the example program • State the benefits and drawbacks of three different data representations of curriculum mapping data, given their particular context The session will be of interest to people who are involved in program-level curriculum review, redesign and/or renewal.
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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 | 0.022 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".