The shifting educational landscape: science teachers’ practice during the COVID-19 pandemic through an activity theory lens
Bibliographic record
Abstract
In March 2020, the COVID-19 pandemic closed all educational institutions. Teachers were called upon to respond quickly to the needs of K-12 students. They had to learn how to navigate online learning systems while simultaneously delivering engaging inquiry-based activities in high-stakes school science courses. To understand how teachers navigated these dual tensions, we have drawn on Cultural-Historical Activity Theory (CHAT) to describe how teachers learned and mediated their professional practices to meet the educational needs of their students. We examine the rapidly changing school activity system and how these changes impacted teachers' epistemological beliefs about student engagement and evaluation. We report that teachers developed new styles and attitudes about teaching that reflected the new educational landscape imposed by the pandemic. We explore the pedagogical shifts that characterize this specific time and how the newly acquired pedagogies could find permanence in teachers' activities post-pandemic. This study reports on the experiences of ten teachers from two high schools as they adapt to change during the global pandemic. We followed the teachers' professional journey as they worked in a professional learning community to develop online practices. Professional learning meetings, semi-structured interviews, and participant journals captured teachers' successes and failures as they struggled to adapt inquiry-based science lessons to meet the challenges of teaching online. Their practices shifted as they engaged students in synchronous collaborative projects and laboratory activities, and they developed alternative formative and summative assessment practices. This study contributes to a growing body of research of teacher practice through a CHAT theoretical framework to understand teachers' professional learning during a time of change and upheaval.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.032 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".