Supporting Self-regulated Learning across Disciplines in International Post-secondary Education
Bibliographic record
Abstract
This paper reports on the results of a project involving the incorporation of Self-Regulated Learning Supportive Practices (SRL-SPs) in required first-year courses for new international students at Vantage College, a specialized pathway program at The University of British Columbia. Three case studies detail how SRL-SPs were adapted and implemented by instructors within three different disciplinary contexts and represent findings on how SRL-SPs can support instructor teaching and student learning when those students and their instructors are from diverse educational backgrounds or cultures of learning (Johansen & Tkachenko, 2019). Findings include an enhanced understanding of the expectations that instructors and students bring about knowledge and knowledge production processes in the disciplinary learning contexts of a Canadian university and how SRL-SPs foster international students’ metacognition about their own learning. Further, a cross-disciplinary Community of Practice for instructors was developed through the project and furthers critical reflection on disciplinary and cultural expectations within post-secondary teaching and learning. Our overarching conclusion is that the success of SRL-SPs depends upon the increased presence and self-reflection of instructors in structuring the learning environment and activities. These and other insights provide an understanding of the potential benefits of SRL-SPs for international students and their instructors and promise to inform how instructors plan their courses with a diverse student body.
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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 | 0.009 | 0.015 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".