Semantic web innovations for higher education
Bibliographic record
Abstract
Advances in artificial intelligence in the area of knowledge representation and reasoning have allowed the Web of documents to evolve into the Semantic Web. This technology enriches Web resources with formal semantic information to give them meaning and to allow software agents to exploit them in a more intelligent way. The Semantic Web provides a huge amount of open free datasets, annotated with ontologies or formal shared representations, as well as innovative applications to exploit them. In this paper we discuss some of the main advantages of using Semantic Web in different activities involved in Higher education such as course and programs planning, personalization of learning, ontology use by students and teachers, support in learning resource search and in autonomous life-long learning and in learning communities and we present the results of more than a decade of research and development in Semantic Web enhanced learning in Higher Education both at T´el´euniversit´e du Quebec in Canada and at the Universidad de Los Andes in Colombia.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".