COVID-19 Pandemic and Possible Futures of Adult Online Learning in Higher Education : Six Trends That Could Shape the Future
Bibliographic record
Abstract
The COVID-19 pandemic has had and will have, profound effects on adult education (Boeren, Roumell & Roessger, 2020; Kapplinger & Lichte, 2020) and online learning practices. The impact was unprecedented and led to the largest and quickest transformation of pedagogic practice ever seen in contemporary universities (Brammer & Clark, 2020). Although it is too soon for a full assessment, the first step is to gain insight into an understanding of the macro trends taking shape inside and outside the walls of institutions and then explore how these trends may affect the future. Against this background, a question arises: How is the COVID-19 pandemic shaping the future of adult online learning in higher education? Drawing on adult education and higher education scholarly and practitioner literature published over the last year, the purpose of this paper is threefold: (i) in the context of the COVID-19 pandemic, to identify and analyze emerging trends that could shape the future of adult online education in higher education, (ii) to analyze these trends over a longer time span in the literature, and (iii) to explore the possible futures of adult education and online learning in higher education.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".