Bibliographic record
Abstract
By far the most popular specification for learning objects is the IEEE Learning Object Metadata (LOM) standard. In it are outlined 76 different elements that correspond to pedagogical, technical, and administrative aspects of learning objects. This standard, however, has proven to be ineffective for creating computer adapted dynamic courseware. This paper outlines some initialr esear ch we ar e d oing in acquir ing, descr ibing, and using lear ning object metadata. Instead of the IEEE LOM, we ar gue for a mor e flexible appr oach to both defining and associating metadata with lear ning objects. By cr eating domain, educational, and lear ner char acter istic ontologies, content can be dynamically linked to those competencies that ar e o bser ved in ar unning e-lear ning system. This pr ovides for a set of evolutionar y me tadata, wher e softwar e agents can inspect multiple metadata instances for a given lea r ning object andr eason over them for a par ticular goal. As more metadata instances are added to the system, agents are expected to be able to provide more accurate reasoning, eventually leading to the dynamic delivery of personalized course content.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".