An Evaluation of the Impact of “Learning Design” on the Distance Learning and Teaching Experience
Bibliographic record
Abstract
This paper evaluates the implementation of Learning Design on the production of a core FHEQ level 6 (QAA, 2008)[1] unit of study at a UK distance learning institution. By comparing student (n=656) and tutor (n=42) survey data with questionnaire responses (n=9) from the unit of study’s core production team, this paper assesses the impact of incorporating the Open University Learning Design Initiative (OULDI) methodology into curriculum production by looking specifically at barriers and facilitators in the application of Learning Design and its impact on module development, delivery, and the resulting student and tutor learning experience. With a focus on developing and embedding Learning Design into the curriculum planning and production process, the paper explores how lessons learned from this experience have helped to guide and inform the future implementation of Learning Design into module and qualification level frameworks. [1] Frameworks for Higher Education Qualifications in England, Wales and Northern Ireland, produced by the Quality Assurance Agency for Higher Education (2008) – this framework provides a reference point for all Higher Education providers for the setting and assessing of academic standards.
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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 | 0.038 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".