Grappling with the Complexity of Undergraduate Degree Program Reform: Critical Barriers and Emergent Strategies
Bibliographic record
Abstract
This article examines the complexity of undergraduate degree program reform in multinational, institutional, and disciplinary settings using data collected over a 10-year period.Developing learning-centred curricula that are responsive to the needs and circumstances of the pedagogical context is clearly a complex and multifaceted process.Data from this research suggest that inherent challenges of undergraduate degree program reform can be addressed through learning-centred strategies specific to the context of curriculum, as well as the iterative processes of curriculum development, implementation, and evaluation.In particular, scholarly approaches to curriculum practice engage curriculum learning communities in critical issues pertaining to resource commitments, student engagement, curriculum integration, and the extent to which the curriculum is effective in achieving its goals.Integral to this paper is an example of a digital media recording of an educational development workshop with multidisciplinary faculty members at Aston University (UK).The aim of this workshop was to engage faculty to compare and contrast critical barriers and emerging strategies for undergraduate degree program reform with faculty experiences in a Canadian context (see Appendix A).
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 | 0.071 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| 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".