Bibliographic record
Abstract
Technology's impact on education is undeniable. Advanced image representation is almost a requirement by many institutions and governing bodies in medicine, dentistry, and allied health sciences. Endorsement to use technologies for demonstrative, training, and simulation purposes is almost ubiquitous. Often, efficacy and principles surrounding the use or development of complex visualisation tools is sparse for broad applications, and/or narrow for specific scenarios. This presentation will highlight methods that inform a “learner‐centred” approach to using images. The author will attempt to widen the understanding of complex images on learner behaviour and the underlying physiology of “what” and “how” learners achieve goals. We know learners are different, thus, this presentation will utilize learner spatial ability as the context. Learner differences can be described both by behaviour (extrinsic characteristics) and physiological consequence (intrinsic characteristics) supporting individual learning strategies. Pairing intrinsic and extrinsic information about the learner, pedagogical approaches incorporating complex visualisation can be developed, studied, and incorporated from an informed position. Educators, educational technologists, and industry will be in better position to develop meaningful and directed visualisation environments. Grant Funding Source : N/A
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.002 |
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".