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Record W3200446438 · doi:10.21432/cjlt28052

Advancing Knowledge Creation in Education Through Tripartite Partnerships

2021· article· en· W3200446438 on OpenAlexaffvenue
Sharon Friesen, Barbara Brown

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2021
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGeneral partnershipKnowledge buildingKnowledge managementGovernment (linguistics)Professional developmentInstructional designAuditWork (physics)Educational technologyProcess (computing)Collaborative learningPedagogySociologyComputer scienceEngineeringPolitical scienceBusiness

Abstract

fetched live from OpenAlex

The purpose of this paper is to highlight the work of one tripartite partnership with stakeholders to improve and strengthen novice teachers’ pedagogical designs using design based professional learning guided by the principles of knowledge building/knowledge creation. The tripartite partnership involved 450 novice teachers from an urban school division, a practitioner-research university team, and the provincial government. Drawing upon one case, this paper analyzes the ways in which the design-based professional learning mirrored the knowledge building/knowledge creation processes highlighting the ways in which teachers worked in collaborative, collective, and connected ways to progressively improve pedagogical designs for collective knowledge building. Computer supported, networked digital technologies provided a community to develop an audit trail to keep track of progressive improvements and refinements to their pedagogical designs and to support, enable, and enhance knowledge building discourse. Design-based professional learning informed by the 12 principles of knowledge building/knowledge creation provided novice teachers with a process to work collectively as a community, progressively improving and refining their pedagogical designs, identifying the role of their pedagogical designs in their students’ work, and engaging with other teachers in their respective schools.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.386
Teacher spread0.348 · 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 designNot applicable
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

Citations6
Published2021
Admission routes2
Has abstractyes

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