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Record W7017120687

Achieving Learning Regulation Support: How a Socially Shared Approach Fits, Functions, and Thrives

2022· dissertation· W7017120687 on OpenAlexaboutno aff

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldMedicine
TopicGestational Trophoblastic Disease Studies
Canadian institutionsnot available
Fundersnot available
KeywordsObjectivity (philosophy)Qualitative researchHigher educationChristian ministryCoding (social sciences)Professional learning communitySocial learningTeaching method
DOInot available

Abstract

fetched live from OpenAlex

Across three studies, the purpose of this dissertation was to explore how socially shared learning regulation (SSLR) “fits” as a suitable solution to the lack of learning regulation support in Ontario, “functions” to support teachers’ professional learning and students’ independence, and best “thrives” among teacher implementers who face a range of constraints. Three different research methodologies were used with the overall goal of providing guidance to educators and researchers regarding the use of socially shared learning regulation as a classroom teaching approach. In Study 1, data gathered from 50 key Ontario Ministry of Education (MOE) documents were analysed to measure the alignment between SSLR and the practices and mandates that are prescribed as guidance for educators in Ontario; considerable “fit” between the MOE publications and the central tenants of SSLR was discovered. Especially within these areas of fit, a lack of pedagogical specificity for educators was both apparent and remarked upon by the documents themselves. These seem as though they could be addressed, at least partly, through the use of SSLR. In Study 2, a qualitative interview method was used to explore the way an SSLR pedagogy may “function” to support teachers’ pedagogical learning, personal learning regulation development, and student independence. A second researcher was heavily involved in the recruiting, interviewing, transcribing, and coding for this study to supply greater objectivity and place the first researcher at an arms-length from a topic in which she had a vested interest. In this study, a wide range of pedagogical learning resulting from teachers’ use of SSLR was revealed, as well as an epistemological shift in the way teachers viewed their students as learners. In Study 3, a mixed methods approach was used to gather descriptive information about the types of enablers and constraints faced by teachers as they attempted to engage in SSLR – a brand new pedagogical approach for most. Survey and short answer data was collected over 3 months and 3 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, the findings of this study indicated 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 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 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)0.032
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 score0.258
Threshold uncertainty score0.513

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0180.050
Scholarly communication0.0190.012
Open science0.0040.019
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.311
Teacher spread0.289 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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