Analyzing Curriculum Mapping Data: Enhancing Student Learning through Curriculum Redesign
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.014 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it