Pandemic designs for the future: perspectives of technology education teachers during COVID-19
Bibliographic record
Abstract
Purpose The disruption caused by the pandemic declaration and subsequent public health measures put in place have had a substantial effect on teachers’ abilities to support student engagement in technology education (TE). The purpose of this paper is to explore the following research question: How do TE teachers see emergency remote teaching (ERT) transitions to blended learning into the next academic year affecting their profession? Design/methodology/approach A snowball and convenience sampling design was used to recruit specialist teachers in TE through their professional organization and were asked to respond to the question: What are your concerns about the future of teaching TE remotely? The qualitative data collected from the participants (N = 42) was analyzed thematically (Braun and Clarke, 2006). Findings The analysis revealed that the switch to ERT impacted the teachers’ ability to support hands-on competency development owing to inequitable student access to tools, materials and resources, all of which affected student motivation and engagement. As a result, teachers raised questions about the overall effectiveness of online learning approaches and TE’s future and sustainability if offered completely online. Originality/value This research is the first of its kind exploring the experiences of TE teachers during the COVID-19 pandemic. In answer to the challenges identified by teachers, the authors offer a blended learning design framework informed by pandemic transformed pedagogy that can serve as a model for educators to use when designing blended instruction.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.019 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".