Bibliographic record
Abstract
Many Interactive Learning Environments (ILE) produce assessment results. Norms exist to define how these results can be used, obtained and manipulated within ILE. AICC, certainly the most known, defines how to communicate assessment results and more widely, datas from a Learning Object (LO) to a Learning Management System (LMS). However, AICC and the other norms don’t define what an assessment result has to look like. Moreover, assessment results are often semantically poor and expressed in a ILE-specific formalism. A common results model would improve the interoperability between ILE but also, if it is rich enough, clarify the meaning of assessment results. In this paper, such a common results model is introduced. At first its conceptual model is exposed, then its information model is described.
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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 | 0.031 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".