Psychometric Evidence of the Questionnaire to Evaluate the Didactic Sequence in Digital Environments in University Students
Bibliographic record
Abstract
The recent changes, following the pandemic of COVID-19, have caused a significant transformation in the field of education, millions of students have migrated to digital environments and the use of digital practices. The research aimed to present the results of the process of creating and arranging the psychometric characteristics of an instrument for evaluating the development of online sessions in teaching and learning. For the validity and reliability stage, 379 students from fourteen universities in Peru were taken. The reliability of instruments using the split halves method with Pearson (0.954) and Cronbach's Alpha (0.947) ratified by the Guttman coefficient (0.932). For the exploratory factor analysisthe KMO (Kaiser-Meyer-Olkin) contrast and Bartlett's test, the rotation method for convergence with Varimax with Kaiser normalization were used. Two models were used to compare the best fit indicators as a final result. It is concluded the obtaining of an instrument with characteristics such as frequent or start-up activities, execution of synchronous sessions, follow-up of asynchronous activities and evaluation with evidences or products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".