In Survival Mode: Adult Education Teachers’ Experience of COVID-19 and Their Use of Digital Technologies
Bibliographic record
Abstract
The COVID-19 health crisis that began in March of 2020 led to a significant increase in the use of digital technologies for teaching and learning in Quebec’s adult education network. This research used a multiple case study design to explore the experience of eight adult education teachers in Quebec’s English-speaking community during the pandemic, focusing on their shifts in their use of digital technologies and the disruptions they have faced. \n \nThrough semi-structured interviews and Socratic Wheels, each teacher reflected on their use of digital technologies pre-COVID and during COVID. Within-case and cross-case analysis of interview transcripts revealed patterns for both time periods in terms of digital tools and activities, obstacles and barriers to technology use, and teacher professional development. Socratic Wheel results indicated noteworthy increases in the use of digital technologies for formative assessment and feedback as well as for collaboration with colleagues. Additionally, teachers expressed a strong interest in continuing to use learning management systems to share learning resources with their students. \n \nThis study includes clear implications for the English-speaking adult education community in terms of improved centre preparedness, personalized professional development for teachers, opportunities for teacher networking, and flexible learning options for students. Further research is needed to expand on the limited representation of this sample and on the data collected during this study.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".