Examining Features of Tasks and Their Potential to Promote Self-Regulated Learning
Bibliographic record
Abstract
The term “self-regulated” is used to describe learners who have highly effective learning and work habits. They are successful in and beyond school. This investigation examines whether and how teachers, who are masters at supporting young students’ development of self-regulated learning (SRL), can mentor student teachers to design tasks and develop practices that promote elementary school students’ SRL. Nineteen student teachers were paired with 19 mentor teachers in a cohort that emphasized SRL theory and practice. In general, student teachers remained with the same mentors throughout their teacher education program and were supported by faculty associates 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, lesson plans and unit plans. Data indicate some student teachers designed tasks and implemented practices that promote SRL and that mentors’ practices accounted for 20% of the variance observed in the student teachers’ practices. Finally, 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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".