Education's Response to the COVID-19 Pandemic Reveals Online Education’s Three Enduring Challenges
Bibliographic record
Abstract
Closed campuses, working remotely, and physical distancing have changed the way we work, teach, learn, shop, attend conferences, and interact with family and friends. But the Covid-19 pandemic has not changed what we know about creating high-end online education. Two decades of research has shown that online education often fails to fulfill its promise, and the emergency shift to remote instruction has, for many, justified their distrust and dislike of online learning. Low interactivity remains a widely recognized short-coming of current online offerings. Low interactivity results, in part, from many faculty not feeling comfortable being themselves online. The long-advocated for era of authentic assessments is needed now more than ever. Finally, greater support is needed for both underrepresented students and for faculty to move beyond basic online instruction to create a strong continuum of care between the teaching and learning environment and the student support infrastructure. For those who have been long-term champions of online education, it has never been more important to confront the three biggest challenges that continue to haunt online education – interactivity, authenticity, and support. Only by confronting these challenges squarely can instructors, educational developers, and their institutions take huge steps towards better online instruction in the midst of a pandemic and make widespread, high-quality online education permanently part of the “new normal.”
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".