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Record W3125975613 · doi:10.3389/feduc.2020.618404

Planned Change: Drivers of High Implementation for a Pedagogical Self-Regulated Learning Intervention

2021· article· en· W3125975613 on OpenAlexaff
Laurie Faith, Angela Pyle

Bibliographic record

VenueFrontiers in Education · 2021
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsStatus quoSelf-regulated learningPsychological interventionAdaptabilityIntervention (counseling)MetacognitionPsychologyMathematics educationLearning ManagementKnowledge managementCognitionMedical educationPedagogyComputer sciencePolitical scienceManagementMedicine

Abstract

fetched live from OpenAlex

Resourcefulness and adaptability are essential to success in the modern economy; the motivation, metacognition, and cognitive skills required for self-regulated learning (SRL) have never been more important. Unfortunately, teacher-led SRL interventions rarely survive implementation, and teachers' general practices rarely reflect their intention to promote SRL. After discussing the shortcomings of virtual or modularized SRL education, this study explores the drivers of a human-led, communal, pedagogical approach. Data was collected over 3 months and three timepoints from 81 kindergarten to Grade 8 teachers who were genuinely dissatisfied by their status quo practices, ready for change, and largely eager to implement the novel teaching approach presented to them. Building on established theories of planned change implementation, this research shows a minimal effect of teachers' approval of the intervention on implementation. Rather, specific drivers to the implementation of complex, communal pedagogical interventions included the support of high-status supervisors and peers, while identified constraints to implementation included fears regarding management of student behavior.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.074
GPT teacher head0.458
Teacher spread0.384 · 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 designObservational
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

Citations2
Published2021
Admission routes1
Has abstractyes

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