Interpersonal Communication Instruction During COVID-19: Challenges and Opportunities
Bibliographic record
Abstract
[Introduction]: "The outbreak and spread of COVID-19 have disrupted higher education worldwide. On March 11, 2020, the WHO declared COVID-19 as a global pandemic. In Canada, the declaration expedited the introduction of preventive measures by governments at different levels to curb the spread of the virus, including the closure of universities. Consequently, in-person courses were frantically switched to “emergency remote teaching” (ERT). At the time of writing (January 2021), countries across the northern hemisphere are undergoing the second wave of climbing COVID-19 cases. Accordingly, ERT is expected to continue at many postsecondary institutions over the next few months. With ERT becoming the new norm of higher education, there are growing concerns among educators about its impacts on instructors and students. On Facebook, for instance, relevant conversations have taken place in groups like “pandemic pedagogy.” As summarized by Schwartzman (2020), the “pandemic pedagogy” group’s founder and lead moderator, such conversations shed light on several challenges novice online instructors have encountered, notably the erosion of autonomous time and space, the relative merits of synchronous and asynchronous content, and the balance between rigor and accommodation. Echoing the educator concerns expressed on Facebook, recently published case studies on education during COVID-19 have explicated the limits of ERT. For example, Barton (2020) survey of 117 U.S. postsecondary instructors whose courses including field activities found that the abrupt shift to ERT has presented unique challenges for achieving learning outcomes typically associated with face-toface field activities. For disciplines such as ecology, environmental studies, and geography, instructors were forced to either substantially reduce field-related learning outcomes or substitute them with instructor-centered remote activities."
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 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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".