Utah K-12 Teachers’ Perspective: Challenges and Changes with Technology Integration during COVID-19 Pandemic
Bibliographic record
Abstract
In March 2020, the global health emergency caused by the COVID-19 pandemic brought about significant changes in classrooms around the world. This paper is part of a larger study that investigated how Utah teachers across the state adapted to technology integration during that period. Specifically, we present interview findings from ten teachers in Utah, which we analyzed using open and axial coding. The study identified four distinct challenges that teachers faced at the onset of the COVID-19 outbreak: increased stress, difficulties in transitioning to digital formats, Technological Pedagogical Content Knowledge (TPACK) dissonance, and students’ lack of technological knowledge. As a result, teachers began to reassess their pedagogical approaches and incorporate greater care for themselves and their students. In terms of technology, teachers reported an increased willingness to utilize technology and videoconferencing, as well as a shift toward digital formats and platforms.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".