Pedagogy in the Time of Pandemic
Bibliographic record
Abstract
Pandemic—the global spread of an initially local disease like COVID-19—bluntly forces us to stop. How do we respond in higher education to such stopping? Whether at the government level, for our universities, or in our classrooms, no existing playbook prescribes the pathway for dealing with a global pandemic of this magnitude, even now as we emerge from total lockdown to the potential for a new tomorrow. What we have done at this juncture is to capture anecdotal responses within and across countries that may highlight trends for later consideration. Unlike a globalised response that would adopt one approach internationally, our study considers adaptations for local differences in a glocalised set of responses in an attempt to identify new paradigms that reconceptualise not only teaching and learning, but also assessment. Our responses to the pandemic require leadership—from all of us—to leverage a firm and steady presence, care and compassion for each other, and prudent decision-making. Moreover, identified issues indicate shared threads across the seven institutions of higher education in this research. From a localised perspective emerge responses at the curricular, institutional, and technological levels. First, changes to courses and curricula must respond to emotional needs of students when transitioning from face-to-face (or hybrid) to online delivery; nevertheless, faculty must ensure that academic rigour is not sacrificed in the process. Second, the mission and value of higher education must indicate that institutions will recommit to faculty support beyond emergency remote teaching; furthermore, a sense of campus community needs to be nurtured. Third, the needs of students and faculty must drive the choices of technology—not the reverse—when determining how to transition to online deliveries; in short, administrators must reprioritise factors used in decision-making. Moreover, a glocalised synthesis of responses across all institutions and levels identifies four clustered themes. First, the disruption of the pandemic may lead to innovations in higher education. Second, the role of faculty is becoming redefined beyond content-specific disciplines. Third, educational models must expand to include individuals other than traditional students. Fourth, rigorous pedagogical scholarship, including leadership, will point to new educational insights. Overall, we stand at the crossroads. Rather than being defined by the pandemic, let us seize the opportunity to transform higher education from a paradigm that has been to the paradigm of what might be.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".