Competency-Based Evaluation in Higher Education—Design and Use of Competence Rubrics by University Educators
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
Competency-based learning requires making changes in the higher education model in response to current socio-educational demands. Rubrics are an innovative educational tool for competence evaluation, for both students and educators. Ever since arriving at the university systems, the application of rubrics in evaluation programs has grown progressively. However, there is yet to be a solid body of knowledge regarding the use of rubrics as an evaluation tool. This study analyzes the use of rubrics by 150 teachers at 5 Spanish universities. The comparative analysis allows us to determine how these rubrics are being used to assess (or not) competencies. This study examines the educators’ intentions and the pedagogical aspects that are considered in the design and application of rubrics. The results and conclusions may lead to suggestions for improvements and strategies that may be implemented by university professors when creating appropriate competency-based scoring rubrics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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