Is Semi-Automatic Authoring of Adaptive Educational Hypermedia Possible?
Bibliographic record
Abstract
Adaptive Hypermedia (AEH) is considered, in principle, superior to regular Hypermedia, due to the fact that it allows personalization and customization. However, the creation of good quality AEH is not trivial. Nowadays, a lot of research concentrates on the authoring challenge in adaptive hypermedia. We previously introduced the LAOS model, a five-layer adaptive hypermedia authoring model that describes AEH in a detailed way, to allow flexible re-composition of its elements, according to the personalization requirements. However, such a detailed structure claims a lot of time to populate with AEH instances. Alternatively, we propose semi-automatic authoring techniques that populate the whole structure based on a small initial subset that has been actually authored by a human. We analyze here the different possible initial subsets, and the resulting structures, based on the LAOS architecture. Moreover, we examine if the flexibility of the whole was in any way affected by the replacement of human authoring with automatic authoring. We see the latter as yet another step towards adaptive hypermedia that 'writes itself'.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".