LearningMapR: A Prototype Tool for Creating IMS-LD Compliant Units of Learning
Bibliographic record
Abstract
Commentary on: Chapter 22: A Learning Design Worked Example. (Gorissen & Tattersall, 2005) Abstract: This article demonstrates and discusses a model to help instructors select appropriate designs from learning design repositories for courses they are developing. We describe the LearningMapR: A prototype pedagogical design tool being developed as a first step toward an IMS-LD-compliant authoring system. This tool's output is a Unit of Learning [UOL] containing storyboards, placeholders for content, and IMS-LD compliant templates and exemplars that are chosen from an illustrative set developed for the project. Based on collaborative work with the University of Oxford and using tools such as Reload as the base, we intend to create a 'teacher-friendly' tool for instructors to create UOLs. Editors: Colin Tattersall and Rob Koper.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.014 |
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".