Higher Education in the First Year of COVID-19: Thoughts and Perspectives for the Future
Bibliographic record
Abstract
In the last year a new virus (SARS‑CoV‑2) and the disease caused by it (COVID-19) has quickly spread around the world, leading the World Health Organization to declare a public health emergency and, then, a global pandemic status. The strategies adopted by many countries to reduce the impact of the pandemic were mainly based on social distancing rules and on stay-at-home measures or lockdowns. These strategies had severe disruptive consequences on many sectors, including all levels of education. While the “traditional” (face-to-face) Higher Education (HE) system was unprepared for the lockdown (e.g., no plans for a massive shift to online teaching were available/ready), it reacted in an extremely quick and effective way, replacing face-to-face teaching with online teaching. While COVID-19 has been extremely challenging for education, the experience has undoubtedly provided positive inputs for the digitalization of the HE system. The question is however, if whether after the COVID-19 emergency everything will go back to the previous situation or instead if the pandemic has irreversibly changed HE. While we are still in the middle of the crisis, it is in our view beneficial to start to reflect on the challenges and open issues that emerged during this period and the lessons learned for the “new normal” (as it is often referred to). In this conceptual paper we seek to start this discussion by focusing on the following relevant aspects that should be considered to succeed in the digital transformation: broadband network infrastructure and hardware devices; e-learning software; organization of teaching activities; pedagogical issues; diversity and inclusivity; and a number of other issues. We conclude that the COVID-19 pandemic will irreversibly change HE and probably for the better.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".