The impact of digital skills on teaching performance in higher education: A meta-analysis
Bibliographic record
Abstract
The rapid progress of TICs has generated different ways to modify and transfer information, which implies the generation of new forms of knowledge. The objective of this study is to establish the relationship between digital competencies and university teaching performance in public higher education in Huancayo. The study is of a basic type with a quantitative approach and correlational level, developed with the partition as a sample of 272 teachers and 387 students who develop teaching-learning activities at the UNCP. The data were analyzed and modeled through structural equations based on PLS. The research arrived at the following results: a value of 0.890 in Spearman's Rho correlation coefficient and a significance level of .000, which shows that there is a high positive relationship between the study variables; likewise, the hypothesis is accepted. general which considers that there is a significant relationship between digital competencies and teaching performance in the classroom.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".