A study on the digital competence of teachers in the use of YouTube as a teaching resource according to gender, age, and years of teaching experience
Bibliographic record
Abstract
The purpose of this study was to describe the teacher's self-perceived level digital competence in the use of YouTube as a learning resource. As specific objectives, the level of competence was compared across the variables gender, age, and years of experience. For this, a non-experimental, quantitative, and ex post facto design was used. Data collection was carried out in the last quarter of 2022, with a sample of 2,157 respondents. The results showed that the self-perceived level was high in relation to the ability to search and share information, although it was medium-to-low for the creation of content. Regarding gender, significant differences were found in favor of the male teacher. Regarding age and years of teaching, significant and negative correlations were found, with an inverse relationship between the increase in age and experience and the decrease in digital competence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".