The post‐COVID‐19 future of digital learning in higher education: Views from educators, students, and other professionals in six countries
Bibliographic record
Abstract
Abstract Predictions about the post‐pandemic future of digital learning vary among higher education scholars. Some foresee dramatic, revolutionary change while others speculate that growth in educational technology will be buffeted both by modest expansion and unevenness. To this debate we contribute evidence from four groups across six countries on four continents: college and university educators ( n = 281), students ( n = 4243), senior administrators ( n = 15), and instructional design specialists ( n = 43). Our focus is on the future of digital learning after the pandemic‐induced pivot to emergency remote instruction. Using data from interviews and self‐administered questionnaires, our findings reveal a high degree of congruency between respondent groups, with most envisioning more blended/hybrid instruction post‐pandemic and some modest increases in fully online courses. Student opinion is more sceptical about future change than within the other groups. Among respondents in all groups there is little expectation for a full‐blown, revolutionary change in online or digital learning. Practitioner notes What is already known about this topic Digital learning has been growing in higher education, although a digital disconnect continues whereby the availability of educational technology exceeds its application to learning. Expectations regarding technology‐mediated learning post‐COVID‐19 are mixed, hampering planning for the future. Hesitancy about teaching or taking courses with some or full online components persists. What this paper adds A strong majority of respondents in higher education foresee the most growth in blended/hybrid forms of digital learning post‐COVID‐19. A solid percentage, between about two‐thirds and three‐quarters of faculty and students, envision learners and instructors taking or teaching more fully online courses post‐pandemic. A strong congruency exists between faculty, students, senior administrators, and instructional design professionals in their ranking of scenarios for the future of digital learning. Implications for practice and/or policy Educational technology in higher learning will not return to a pre‐COVID‐19 normality—if a pre‐COVID‐19 ‘normal’ could even be defined. As post‐pandemic institutional planning unfolds, it is important to reflect experiences and incorporate insights of instructors, students, and instructional designers. Successfully building on these insights, where more blended/hybrid learning is foreseen, requires a thoughtful integration of face‐to‐face learning and educational technology.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".