Mentoring Student Teachers to Support Self‐Regulated Learning
Bibliographic record
Abstract
We use the term “self‐regulated learning” (SRL) to describe independent, highly effective approaches to learning that are associated with success in and beyond school. Research has indicated that fostering SRL in elementary school children requires a level of instructional sophistication and student awareness that may be beyond the capabilities of beginning teachers. This article presents findings from the first 2 years in a 4‐year investigation of whether and how highly effective, high‐SRL teachers in a large, diverse, suburban Canadian school district can mentor student teachers to design tasks and develop practices that promote elementary school students’ SRL. Across Years 1 and 2, 37 student teachers were paired with 37 mentor teachers in grades K–5 in a cohort that emphasized SRL theory and practice. In general, student teachers remained with the same mentors throughout their yearlong teacher education program and were supported by faculty associates (teachers seconded by the university to supervise student teachers’ practice) and researchers who also had expertise in promoting SRL. Researchers observed mentor and student teachers teaching, videotaped professional seminars, and collected samples of student teachers’ reflections on teaching, lesson plans, and unit plans. The observational data, which are the focus of this article, indicated that many student teachers were capable of designing tasks and implementing practices associated with the promotion of SRL. In general, student teachers’ tasks and practices resembled those of their mentors, and the complexity of the tasks that mentors and student teachers designed was strongly predictive of opportunities for students to develop and engage in SRL.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".