Disruption Caused by the COVID-19 Pandemic in Peruvian University Education
Bibliographic record
Abstract
The objective of the research was to analyze the effects of the disruption that Peruvian university education has suffered due to COVID-19 in 2020. The type of research was basic under the design of grounded theory (De la Espriella & Gómez Restrepo, 2020). The study scenario was university education in the first semester of 2020, considering more than twenty academic texts; between articles, texts and reports related to university educational disruption. The technique implemented was that of documentary analysis. And in the procedure for collecting information, the inclusion and exclusion criteria were taken into account. The results show that disruption is the break, abrupt or sudden interruption caused within a current paradigm. Therefore, the paradigm of face-to-face Peruvian university education due to the COVID-19 pandemic has suffered a sudden interruption; so much so that article 47 of University Law N° 30220 was modified; The modalities of face-to-face, semi-face-to-face and distance or non-face-to-face teaching were established, introducing university higher education in the new paradigm of online education. For this reason, universities should be required to leave their comfort zone and incorporate information and communication technologies as a possibility and opportunity for academic development.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 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".