E-Learning meets the Social Semantic Web
Bibliographic record
Abstract
The social semantic Web has recently emerged as a paradigm in which ontologies (aimed at defining, structuring and sharing information) and collaborative software (used for creating and sharing knowledge) have been merged together. Ontologies provide an effective means of capturing and integrating knowledge for feedback provisioning, while using collaborative activities can support pedagogical theories, such as social constructivism. Both technologies have developed separately in the e-learning domain; representing respectively a teacher-centered and a learner-centered approach for learning environments. In this paper we bridge the gap between these two approaches by leveraging the social semantic Web paradigm, and propose a collaborative semantic-rich learning environment in which folksonomies created from studentspsila collaborative tags contribute to ontology maintenance, and teacher-directed feedback.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.027 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".