It takes more than metadata and stories of success: understanding barriers to reuse of computer-facilitated learning resources.
Bibliographic record
Abstract
A national study in Australia in the late 1990s explored barriers to the adoption and reuse of computer-facilitated learning (CFL) in Australian universities. These barriers will be summarized. One of these barriers is that it is hard to find information on courseware that is educationally sound; usually such courseware is expensive to produce and so reuse is especially desirable. However, even when information and access to electronic courseware exists, reuse may still not occur. Two cases will be described to illustrate the complexity of reuse. These cases are: 1) a collection of 169 plastic surgery websites; and 2) an international consortium of veterinary microbiology resources based on a well-evaluated case study design. Some strategies for improving reuse are suggested. Just what is this paper about? Keywords reuse; electronic media content; barriers One of the conference themes is the ‘Navigators ’ log’. The suggestions there for paper orientations are:
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 | 0.025 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.004 |
| 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".