From Policy to Practice: Educators’ Sensemaking and Implementation of Assessment and Evaluation During the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic had a profound impact on how teachers engaged with educational policy. During this period, teachers were met with the challenging task of bridging their existing understandings of educational policy with COVID-19 directives issued by the provincial government, to form new implementation strategies. This study examines how fourteen secondary school teachers from various schools and school boards across Ontario, Canada, navigated student assessment and evaluation during the COVID-19 pandemic. The study is multi-faceted: it begins by exploring teachers’ initial understanding of assessment and evaluation, with a particular focus on their knowledge, interpretation, and implementation of the policy document Growing Success: Assessment, Evaluation, and Reporting in Schools (2010) in their pedagogical practices. The study then examines how their approaches to assessment and evaluation evolved in response to COVID-19 directives, as well as the challenges they encountered in their teaching during the pandemic. The findings suggest that teachers’ sensemaking of assessment and evaluation is shaped by their knowledge of the Growing Success policy and their cumulative experiences with implementation over time. After the release of COVID-19 directives, teachers engaged in both individual and collective sensemaking to interpret, adapt, and integrate these new government directives into their teaching practices. However, the challenges posed by assessing and evaluating students during the pandemic prompted educators to revisit and adjust their understanding and approach to navigate the complexities of this new teaching context. While educational policies and directives are intended to drive change at the school level, they are subject to reinterpretation and adaptation as they interact with educators and the context of each school. Consequently, the findings of this study emphasize the importance of involving teachers in the development of educational policies, directives, and reform efforts. Such involvement would provide valuable insights and highlight the complexities involved in policy enactment, ultimately bridging the gap between policy as text and policy in practice.
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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 | 0.143 | 0.225 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.034 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".