Six ways to get a grip on leveraging the expertise of Instructional Design and Technology professionals
Bibliographic record
Abstract
We underutilize the knowledge and skills of Instructional Design and Technology (IDT) professionals, despite the frequent challenges in implementing learning technologies in medical education. This is largely due to a lack of understanding among stakeholders regarding the expertise of IDT professionals and their role in technology implementation processes. We seek to improve technology implementation outcomes by explaining the IDT field's foundational tenets of a systems perspective and disciplined approach, clarifying the role that IDT professionals can play in educational technology initiatives, and providing guidance on how to foster productive collaborations in pursuit of effective technology-enhanced learning.
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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 | 0.138 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.015 | 0.055 |
| Scholarly communication | 0.042 | 0.051 |
| Open science | 0.005 | 0.034 |
| Research integrity | 0.015 | 0.024 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".