Multidisciplinary Higher Education Strategies in Small Groups of Health and Social Sciences
Bibliographic record
Abstract
The Small Group Learning (SGL) permeates throughout higher education fields of study. Thus, our aim is to assess the influence of a planned activity of SGL on a variety of competences appraisals through a multidisciplinary perspective. To that end, cooperative learning activities implemented under the SGL approach were programmed for six different degrees. For each activity, students were provided with instructions about what the activity was about and how to make it. Two surveys were scheduled before and after the completion of the SGL. Our findings are presented in a descriptive and quantitative analysis, using surveys with which we examine the pre and post differences in students’ self-reports. As a result, self-perceptions on oral and written expression and bibliography competence increased after the practice of the Small Group Learning (SGL) strategy in students from Social Science degrees as well as in students from Psychology degree. In addition, receiving feedback showed an improvement for the whole sample after doing SGL. Our results confirm that, in order to achieve an excellent quality education, SGL could be applied equally in different fields of study: both Health and Social Sciences. The novelty of this study is that it has been conducted in six different academic degrees and has focused on higher education skills in order to improve future undergraduate’s employability.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".