The multiple dimensions of curriculum mapping: designing a comprehensive outcomes-based framework
Bibliographic record
Abstract
In curriculum design processes, the principle of constructive alignment represents an effective tool for aligning curricula, pedagogy and assessments to make curriculum content explicit. Yet there remain gaps in the achievement of constructive alignment in higher education. Curriculum mapping processes attempt to map the vertical and horizontal alignment between modules and courses; however, as we argue in this article, there may be gaps in these processes such that full constructive alignment is not adequately achieved. In this article, we present a framework that identifies all connecting relationships between module and course learning outcomes as required for comprehensive constructive alignment. The framework serves to highlight where these gaps (or what we call fracture points) may occur that are not adequately addressed by curriculum mapping processes. The utility of this framework comes not only in offering insight into the contributory roles of learning outcomes and constructive alignment processes (and thus providing the opportunity to reflect and address disparities that may lead to curricular misalignment in higher education); it also provides more coherence in approaches to understanding the why of learning outcome design.
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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 | 0.093 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| 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".