Using Behaviour Change as a Critical Frame of Reference to Understand the Adoption of Learning Design Methodologies in Higher Education
Bibliographic record
Abstract
This paper introduces the COM-B model (Capabilities-Opportunities-Motivation:Behaviour) and its use in combination with the Behaviour Change Wheel (BCW) in developing an intervention which aims to promote the adoption of learning design methodologies by academic staff working with learning technologists in Higher Education (HE). Qualitative structured interviews were conducted among members of staff from five UK universities based on the COM-B model to identify the main behavioural determinants for the use and implementation of learning design methodologies. The analysis suggests that the implementation of learning design methodologies/frameworks might be more likely to occur if academic staff and learning technologists’ psychological capability, physical and social opportunities, and intrinsic and extrinsic motivations are addressed. The COM-B model and the BCW have been effective in the context of learning design to analyse the behaviour of academic staff and learning technologists when engaging in the design of online/blended learning programmes.
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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 | 0.048 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| 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".