The crucial role of the learning management system in a spiral curriculum
Bibliographic record
Abstract
Background: Students in the MD program at the University of Calgary, Cumming School of Medicine beginning in 2023 experienced of a novel curriculum and structure. A core feature of this new curriculum is the spirality across the pre-clerkship. Methods: To explore how the Learning Management System (LMS) could be leveraged as a tool to surface spirality for students, we performed a literature search to look for research that could help navigate the conceptual space between the curriculum as implemented and the curriculum as experienced by the students (Lowry 1993). Results: The literature supports cluing students more into the spirality both in awareness and action. This enables students to contextualize their learning in a greater context. If students are not aware of the spirality it may be at risk. There have been some positive changes, including search and checkbox functions that make it easier to follow the spiral. Enablement of multidirectional access to content within a spiral curriculum may facilitate self-directed learning, in alignment with institutional goals. Discussion: Re-framing LMS feature development around pillars of student experience and research could balance student needs against administrative and faculty feature development.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".