Reuse as heuristic: from transmission to nurture in learning activity design
Bibliographic record
Abstract
In recent years a combination of ever more flexible and sophisticated Web technologies and an explosion in the quantity of online content has sparked learning technologists around the world to pursue the promise of the ‘reusable learning object’ or RLO with the idea that RLOs could be reused in different educational contexts, thereby providing greater overall flexibility and return on investment. In 2002 the ACETS Project undertook a three‐year study in the UK to investigate whether RLOs worked in practice and how the pursuit of reuse affected the teacher and their teaching. Teachers working in healthcare‐related subjects in Higher and Further Education were asked to create an original learning design or activity from third‐party digital resources and to reflect both on the process and its outcomes. The expectation was that teachers would be the ones selecting and reusing third‐party materials. This paper describes how one of the ACETS exemplifiers reinterpreted this remit, challenged the anticipated transmissive model of learning, and instead, gave their students an opportunity to create their own original learning designs and learning activities from third‐party digital resources. By describing the educational enhancements, the resulting heightened levels of critical thinking, and sensitivity to patient needs, ‘reuse’ will be shown to be an effective heuristic for student self‐direction and professional development.
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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 | 0.021 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.032 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".