Pandemic designs for the future: perspectives of technology education teachers during COVID-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it