Didactic Use of the Publisher Processor to Enhance Meaningful Learning in Peruvian Secondary School Students
Bibliographic record
Abstract
The coronavirus pandemic has changed the course of education from face-to-face to remote. In response to this context, we investigated the positive effect of the didactic use of the Publisher processor to enhance meaningful learning in the competence "explain the physical world based on scientific knowledge about matter and energy, biodiversity, earth and universe" of the science and technology area in fifth grade students of secondary education of a Public Educational Institution of Chiclayo (Peru). For this purpose, a pre-experimental design with pre- and post-test was used with a sample of 102 students selected in a non-probabilistic way. A program based on activities incorporated in learning experiences proposed by the Ministry of Education (Peru) was applied. As a result, 72% of the students increased their academic performance after the application of the program, improving up to five (5) points compared to the initial test. Thus, the development of didactic material using the Publisher processor strengthens learning, which highlights the importance of information technologies in the achievement of active learning and the need to use them in the processes of mediation, reinforcement and teacher feedback in virtual and/or face-to-face scenarios.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".