Percepción de los estudiantes sobre la educación en línea en Nicaragua
Bibliographic record
Abstract
The objective of this study is to evaluate students´ perceptions about different aspects (self-evaluation, co-evaluation, pedagogical mediator, academic content and virtual classroom) related to the implementation of e-learning in National Autonomous University of Leon, Nicaragua as part of the national educational Project called Universidad Abierta en Linea de Nicaragua (University Online Opened of Nicaragua) for the first quarter of 2017, where state universities offer complete online careers and courses for free, under the same educational platform. An online survey of 24 items was applied at the end of the first quarter. Univariate analysis was applied, calculating frequency distribution, central tendency (mean, median and mode) and dispersion (standard deviation). Means of Global and categories were calculated. The global mean of the positive perceptions was 55.9%. The categories with the highest percentage of positive perception were Co-evaluation (61.6%) and Virtual classroom (60.04%); in an intermediate point, the categories Academic content and Pedagogical mediator got a 55.44% and 53.9%, respectively. While the category Self-evaluation got only a 48.7% of positive perceptions. As conclusion, more than 50% of National Autonomous University of Leon, Nicaragua ´s virtual career students have positive perceptions in 4 of 5 aspects of the e-learning´s implementation. The outcomes obtained have contributed to detect weaknesses in the virtual teaching-learning process, from the didactic planning to pedagogical mediation, giving us inputs to the continuous improvement.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".