Enhancement of prosthetics and orthotics learning and teaching through e-Learning technology and methodology
Bibliographic record
Abstract
A Write-Once Publish-Everywhere model was used to create and deliver on-line clinical training and education for undergraduate prosthetics and orthotics students. This project consisted of three phases: developing multimedia learning and teaching tools, integrating these tools into the curriculum (combination of e-Learning and live practical sessions), and evaluating the outcomes. Video-based multimedia contents were captured and integrated with graphic, audio and text into a PowerPoint presentation software format. The web-based content was integrated into the WebCT platform for course management. Questionnaires were used to obtain student feedback on this e-Learning approach. Results were compared within the prosthetics and orthotics (P&O) programme, with other Health Sciences programmes, and overall with the University. P&O student responses were significantly higher than other groups for career relevance and problem solving. Qualitative feedback indicated that students appreciated the easy access, integrated and interactive approach of the text materials, concise PowerPoint presentation, demonstration video and the on-line case discussion via the WebCT platform. Educators appreciated the ability easily to maintain contents and publish the modules across multiple media without recreating the contents.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".