Evaluación de competencias genéricas en la universidad. Estudio comparativo en entorno b-learning y presencial
Bibliographic record
Abstract
The general objective of this research was to design and evaluate a system of Soft skills assessment in b-learning en- vironments of teaching-learning in Higher Education, level committed to the comprehensive and relevant professional training. Specifically, the skills commitment, communication, innovation, leadership and teamwork, which are inherent in the integral formation of the person because they facilitate their proper performance in the personal, social and or- ganizational context. The research was carried out at the Complutense University of Madrid, through a sample of 893 students from the Faculties of Education, Physical Sciences and Sociology, during the 2015-2016 academic year. A pretest-posttest design was used with an experimental group and a non-equivalent control group, in the b-learning and face-to-face context, respectively. Likewise, it relied on the foundations of e-Learning-Oriented e-Assessment (e-EOA), the methodology of problem-based learning (ABP), communication through electronic forums and the wiki as a collabo- ration tool. For the data collection, five scales were applied, before and after a quarter of intervention, with the interest of measuring the level of acquisition of said skills. After the analysis of covariance, the results reveal that teamwork was the Soft skills with the highest level of achievement among the students participating in the b-learning modality, while the commitment, communication and innovation skills developed significantly better in the face-to-face context Regarding the learning of leadership skills, it was verified that the context has no repercussion. \n-
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".