Curiosity-Driven, Inquiry-Based Science Projects Bridge Face-to-Face and Online Learning Formats During COVID-19: A Teacher's Community of Inquiry
Bibliographic record
Abstract
This paper is a report on a year-long action research project with a Community of Inquiry where a group of teachers from across primary, secondary, and tertiary contexts were developing and implementing student-centered, curiosity-driven, inquiry-based science projects to bridge face-to-face and online learning contexts and support their students’ engagement in learning during the COVID-19 pandemic. We followed the teachers through two research cycles to investigate their driving question: “How can we best support our students’ learning in blended learning environments through Curiosity-Driven, Inquiry-Based Science Education?” We report on their ideas, successes, and challenges as they created and implemented eighteen projects. In the first cycle of inquiry in fall 2020, the teachers met online to discuss plans, they implemented their plans with their classes, and they met online to reflect on their projects and share resources. In the second cycle of inquiry in spring 2021, the teachers met online again for further planning, implementation, and reflection. We recorded all online meetings, collected resources that teachers shared, and conducted thematic analysis. Findings indicated the primary focus for the teachers were: which education technology methods to use; the importance of supporting their students’ voices to discuss their work at all stages of their projects; coming up with appropriate means of assessment of their students’ projects; supporting their students in their developing research and problem-solving skills; and supporting their students to reflect on their learning. This study is significant because it demonstrates the creativity and innovation of a group of teachers in their efforts to support their students’ engagement and learning through Curiosity-Driven, Inquiry-Based Science Education during the Covid-19 pandemic. The teachers’ projects have been shared on an Open Education Resource.
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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 | 0.034 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".