Evolving attitudes toward online education in Peruvian university students: A quantitative approach
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic has accelerated universities' adaptation process toward online education, and it is necessary to know the students' attitudes toward this online education. Objective: To describe the evolution of the attitude toward online education among social science students at a public university in Peru in the academic year 2020, in the context of the COVID-19 pandemic. Methods: The study uses a quantitative approach, a descriptive level, a non-experimental design, and a longitudinal trend. The sample consisted of 1063 students at the beginning of the class period, 908 during the classes, and 1026 at the end of the class period. The questionnaire for data collection was the Attitude scale toward online education for university students during the COVID-19 pandemic. The data was collected using Google Forms. Results: -value <0.05). Conclusion: The evolution of the attitude towards online education in the sample had a non-significant positive trend. In the initial and process stages, a weak negative attitude prevailed due to the institution's inexperience and poor digital infrastructure; in the end, the attitude became weak and positive due to the adaptation and need for online education.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".