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Record W4390117043 · doi:10.33524/cjar.v23i3.657

Curiosity-Driven, Inquiry-Based Science Projects Bridge Face-to-Face and Online Learning Formats During COVID-19: A Teacher's Community of Inquiry

2023· article· en· W4390117043 on OpenAlexafffundvenue
Carol Rees, H. David Allen, Morgan Whitehouse, Naowarat Cheeptham, Michelle Harrison, Elizabeth DeVries, Grady Sjokvist, Christine Miller

Bibliographic record

VenueThe Canadian Journal of Action Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsThompson Rivers University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCuriosityInquiry-based learningPsychologyThematic analysisMathematics educationAction researchPedagogyCreativitySociologyQualitative research

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.034
metaresearch head score (Gemma)0.062
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.062
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0170.010
Scholarly communication0.0090.007
Open science0.0040.025
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.492
GPT teacher head0.530
Teacher spread0.038 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes3
Has abstractyes

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