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Record W2932767386

Challenges and opportunities of a ‘blended’ professional development program with K-12 science teachers

2018· article· en· W2932767386 on OpenAlexaffabout
Xavier Fazio, Kamini Jaipal-Jamani

Bibliographic record

Venue2019 Conference of the Canadian Society for the Study of Education · 2018
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsBrock University
Fundersnot available
KeywordsBlended learningAccountabilityProfessional developmentScience educationFaculty developmentNext Generation Science StandardsMedical educationFace (sociological concept)PsychologyScale (ratio)PedagogyMathematics educationEducational technologySociologyPolitical scienceMedicine
DOInot available

Abstract

fetched live from OpenAlex

A priority for science education is to enhance pedagogical innovation in classrooms with practicing science teachers through professional development (PD). One way forward is to use a 21st century approach by designing a blended (online and face-to-face) program. While this approach is not new, this PD model has not been investigated as part of a large-scale program in Canada. Since blended programs can vary considerably, evidence is still sparse on its impact on teachers’ practice. Over the project duration, over 200 K-12 science teachers participated in the project. The aim was to support science teachers in implementing innovative science teaching practices (e.g., inquiry) in their classrooms. The program had three elements: face-to-face workshops, collaboration in an online learning platform, and knowledge mobilization through sharing of developed resources. Based on findings we provide recommendations and contribute to the research call to identify evidence-based practices from studies on blended-learning PDs. This includes attending to technological supports, accountability considerations, and integrating online and face-to-face sessions. While changes in participants practices were not sizeable, our study contributes to research that is specific to science teaching communities in Canada.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.188
GPT teacher head0.412
Teacher spread0.224 · 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 teacher head, 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

Citations0
Published2018
Admission routes2
Has abstractyes

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