Achieving Learning Regulation Support: How a Socially Shared Approach Fits, Functions, and Thrives
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".