Bibliographic record
Abstract
The COVID-19 pandemic caused many schools around the globe to close their doors and relocate learning to virtual environments. Teachers were forced to transition the way they educated – from classroom to computer screen, from co-presence to distance, from hands on to hands off. High school teachers in New Brunswick turned to Microsoft Teams to help safely educate students from a distance. To investigate how Teams uniquely influenced the way teachers constructed, presented and shared knowledge, and how students responded to these approaches, I interviewed eight New Brunswick high school educators who taught in the Teams virtual environment during the 2020–21 school year, the first full school year of the pandemic. This article provides insight into some of the potential impositions and pedagogical constraints Teams placed on teaching practices; in what sense the software guided or limited teacher pedagogy and what challenges and opportunities teachers and students experienced; in what ways Teams might be reshaping ways of thinking, feeling, acting and knowing. As an approach to this investigation, Marshall and Eric McLuhan’s Laws of Media () are employed as an inquiry mechanism by which the generalizable rules, patterns and structures of Teams can be recognized and studied. Through these conversations, I observed the enhancement of anytime/anywhere learning; the obsolescence of the physical classroom; the retrieval of lectures; the reversal of connectedness to disconnectedness. The Laws of Media allow education reformers to gain insight into the effects of using Teams as an educational tool before cultural norms and practices become too entrenched in the system, affording education districts and departments time to understand them and make a judgement on if, how and when Teams will be used.
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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.029 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".