ICT Interactions and COVID-19 – A Theorization Across Two Pandemic Waves
Bibliographic record
Abstract
The COVID-19 pandemic instigated the rapid shift to remote work and virtual interactions, constituting a new normal of professional interaction over information and communication technologies (ICT), such as videoconferencing platforms, email, and mobile devices. While ICT may provide many benefits for remote work, such as flexibility, reductions in travel time, and geographical interaction, ICT may also contribute to increases in job strain, reductions in social interactions, and the decline of mental health. While reliance on ICT for remote work interactions is becoming the new normal of organizational activity, scholarly appreciation of the stages in which employees, particularly educators, enact interactions over ICT is limited. Further, the intensity of the pandemic is unlike anything management scholars have studied before, and previous research into ICT use offers little insight into how ICT behaviors evolve over time. Our research explores how educators enact ICT interactions with students throughout the first two waves of the COVID-19 pandemic. We conducted 24 open-ended interviews with educators to learn about their experiences shifting to ICT for virtual classes. We found that ICT interactions between educators and students are enacted through two sequential, interrelated stages:divergent interaction behaviorsandconvergent interaction behaviors, with each stage corresponding to the first and second waves of the pandemic. We delineate the phases within each enacting stage of ICT interaction and conjecture that future use of ICT may include iterative cycles of divergent and convergent interaction behaviors, particularly if educators explore how to leverage ICT in more creative ways. Our research presents a theorization of ICT interaction as an increasingly prevalent form of IT-enabled educational interactions and communication. We provide insight into how educators successfully shifted to ICT use for remote work, and we offer implications for the facilitation of hybrid work arrangements in educational settings using ICT.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| 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".