Integration of ICTS and Digital Skills in Times of the Pandemic Covid-19
Bibliographic record
Abstract
In these times of global tragedy due to the pandemic that caused COVID-19, distance learning relies on the resources of the digital field, as well as on the management of ICT and the development of digital skills. Therefore, this research has been aimed at corroborating the existing links between the integration of ICT and digital skills pandemic times. A study with a quantitative, non-experimental, cross-sectional, correlational approach was developed. The sample consisted of 168 students from a public university in Lima, Peru. Two tools were adapted: 1) integration of ICT, 18 items and 2) digital skills, 30 items, with reliability coefficients by Cronbach's Alpha of 0.976 and 0.889, respectively. The questionnaires were developed and taken through Google forms. The results showed that the level of integration of ICT was high (89.9%) as well as digital skills (86.9%). Spearman's Rho correlation analysis concluded that there was a positive and high relationship between integration of ICT and digital skills (0.761, p < 0.05). Finally, discussions were raised about the development of aspects related to ICT during the current pandemic.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".