An Ontology and a Software Framework for Competency Modeling and Management
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
The importance given to competency management is well justified. Acquiring new competencies is the central goal of any education or knowledge management process. Thus, it must be embedded in any software framework as an instructional engineering tool, to inform the runtime environment of the knowledge that is processed by actors, and their situation toward achieving competency-acquisition objectives. We present here some of our results in the last 10 years that have led to an ontology for designing competency-based learning and knowledge management applications. Based on this ontology, we present a software framework for ontology-driven e-learning systems. Keywords Ontology-driven e-learning system, Competency acquisition A search on the Internet is sufficient to show the importance given to competency profiles in human resource management and education. Ministries of education, school boards, and teacher training institutes use competency profiles to define school programs or teachers ’ required qualities, especially in the use of technologies in education. Consulting companies present their expertise by enumerating competencies, marketing their services in this way. Other companies offer services or computerized tools to help their prospective customers define or manage the
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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 | 0.001 |
| 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