Meeting the Needs of Educators in Professional Development for Self-Regulated Learning: Bridging the Gap from Theory to Practice
Bibliographic record
Abstract
There is a growing awareness within education of the necessity to explicitly teach self-regulated learning skills (Spruce & Bol, 2014). Documents published by the Ontario Ministry of Education, With Our Best Future in Mind (2009) and Every Child, Every Opportunity (2010), demonstrates that there is a critical need to shift away from traditional teaching pedagogies toward pedagogies focused on the development of autonomous learning through fostering self-regulated learning (SRL) skills. \nToday’s businesses and workplaces require skills such as critical thinking, problem solving, collaboration, effective communication, motivation, persistence, learning to learn and self-management (Pelligrino & Hilton, 2012). Given the importance researchers and policy makers have placed on SRL it would appear obvious that teachers would be expected to foster SRL in their daily practices with students in K to 12. Although elementary teachers believe SRL to be important, the development and fostering of SRL in elementary classrooms remains limited (De Smul, Heirwig, Devos & Van Keer, 2019). On the surface offering professional development (PD) to support educators in the fostering of SRL seems a practical solution. However, many teachers are unsatisfied with the PD available to them and as a result do not always take advantage of the PD on offer. \nThe purpose of this project was to create a PD programme that is flexible and therefore able to address the individual needs of teachers and still provide opportunities for teachers to be involved in collaborative learning. The idea of co-learning, opportunities for professional dialogue, networking and time and space to share, discuss and challenge ideas have been found to be important factors for effective PD (Avalos, 2011). Educators involved in professional conversations as part of my project also identified these factors as important.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.079 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.023 | 0.026 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.022 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".