Online teaching during the COVID-19 pandemic: exploring science/STEM teachers’ curriculum and assessment practices in Canada
Bibliographic record
Abstract
The COVID-19 pandemic necessitated school closures globally, resulting in an abrupt move to online/distance teaching or emergency remote teaching (ERT). Teachers and students pivoted from face-to-face engagement to online environments, thus impacting curriculum, pedagogy, and student outcomes across a variety of disciplines. In this paper, the authors focus on science/STEM teachers' experiences with online teaching and learning in a Canadian context during the pandemic. Qualitative and quantitative data were collected through an online questionnaire administered to 75 Grade 1-12 science/STEM teachers in a Canadian province in May-July 2020. Through the TPACK framework and self-efficacy theory, the authors explore i) curriculum planning and implementation in online settings, ii) assessment practices and their effectiveness, and iii) student outcomes, as observed by the teachers. Results indicate that teachers used a variety of platforms, and choice of platform was mainly due to user-friendliness and interactivity, or administrative decision making. Despite teachers organizing online lessons during ERT, gaps were identified in teachers' TPACK framework and self-efficacy, thus impacting their curriculum development, pedagogical approaches, and assessment practices. In general, teaching strategies included pre-recorded videos and self-directed learning in which teachers assigned specific tasks for students to perform independently. Teachers prioritized subject content and covering curriculum objectives over creative and student-centered pedagogical approaches. Assessment techniques employed were viewed by teachers as unauthentic and generally ineffective. Moreover, teachers reported difficulties addressing student needs and abilities, resulting in challenges providing equitable and inclusive online teaching. Finally, online teaching was viewed negatively by most teachers, in terms of student engagement and outcomes.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".