A new competency ontology for learning environments personalization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Competency is a central concept for human resource management, training and education. We define a competency as the capacity of a person to display a generic skill with a certain level of performance when applied to one or more knowledge entities. Competencies, and competency referentials grouping competencies, are essential elements for user models, e-Portfolios, adaptive learning, and personalization in Technology-based learning. But to be processed both by humans and by software tools, competencies should be represented in a formal, non-ambiguous model called an ontology. Moreover, this model should use a shared vocabulary to describe the generic skills and the knowledge entities. Defining and linking shared vocabularies is the purpose of ontologies in the semantic web. The goal of our research is to develop a competency ontology for the semantic web to be used as a shared referential in the description of competencies and competency profiles. We analysed five previous competency models and developed COMP2, a new competency ontology that integrates important elements of previous models and the richness of the semantic web vocabulary. COMP2 provides processing capabilities both to humans and computers. Its graphic model is highly readable by humans for design, evaluation and communication purposes. It also translates, together with its data sets, to standard semantic Web code for machine processing. The ontology is composed of five stages that are interlinked with other ontologies in use within the web of linked open data. We will present an example for the use of the ontology for competency-based personalization in learning environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it