El uso de plataformas virtuales y su impacto en el proceso de aprendizaje en las asignaturas de las carreras de Criminología y Ciencias Policiales, de la Universidad Estatal a Distancia de Costa Rica
Bibliographic record
Abstract
The objective of this study is to determine how the use of Moodle online educational platform helps students of Criminology and Police Sciences to learn. A high percentage of the students from these majors work and study facing adverse conditions. Improvements in mediation, variety of resources, and autonomous learning may turn into meaningful learning opportunities for students. The methodology of this study is based on a quantitative approach by means of a sample survey. A nineteen-item semi-structured questionnaire was administered to fifty students to explore aspects related to working order, academic performance, and online course mediation. Forty-one out of fifty participants responded evaluating two courses from the Cátedra de Ambiente, Política y Sociedad [Chair of Environment, Politics, and Society] and three courses from the Cátedra de Trabajo Social [Chair of Social Work] that were offered during the first quarter of 2016. The gathered information was tabulated to facilitate its analysis. The results indicate that the use of online platforms and technology can be used to improve meaningful learning. However, the resources do not have an incidence without well-structured courses, resources, and the appropriate mediation from instructors.
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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 | 0.024 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".