Impact of Emergency Remote Teaching and Digital Technology Usage in K-12 Teacher Practice
Bibliographic record
Abstract
Digital technologies are potentially being used more in K-12 classrooms than prior to the COVID-19 pandemic. In efforts to slow the spread of the virus, many schools abruptly transitioned to emergency remote teaching (ERT). Not all teachers and students were familiar with using such tools, but were required to adapt. COVID-19 has resulted in a large amount of research in education; however, studies focused on ERT and its impact on teacher practice is limited. This inquiry explores how ERT has impacted digital technology usage in current K-12 teacher practice. Data has been collected from K-12 teachers enrolled in graduate programs at one large university through an online questionnaire, semi structured interviews, a review of documents provided by interview participants, and analytic memos. Analysis and interpretation of findings is in progress, and will be organized by way of examining the key research questions through Cultural Historical Activity Theory. This research will also reveal digital technologies introduced during ERT, and factors influencing a teacher’s decision to integrate new technologies into current practice. The author will conclude by offering recommendations that may be useful in the work of technological change in K-12 education.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".