Building Capacity for Deep Learning
Bibliographic record
Abstract
Post COVID-19, gives school leaders the opportunity to build back a better school system focusing on the needs of students, preparing students to thrive in the 21st century by shifting from teacher-centred to learner-centred pedagogy. Changing teachers’ attitudes, beliefs, and skills to make this shift requires new learning through creating an effective professional learning environment. This organizational improvement plan explores how to build teacher capacity for 21st century learning at the Family of Independent Schools (a pseudonym) in Ontario through the creation of collaborative inquiry teams where teachers develop an individual and collective understanding of deep learning. Deep learning creates student-centred partnerships that integrate academics, well-being, and equity outcomes into regular classroom practices. Social cognitive theory is the theoretical framework that supports teacher learning through leveraging triadic reciprocal causation and its impact on teacher self-efficacy. Collaborative inquiry teams provide a structure for a professional learning environment where opportunities for enactive mastery, vicarious experiences, verbal persuasion and affective states support teachers’ self-efficacy as they change their skills, behaviours and attitudes. Transformational and instructional leadership practices focussing on building relationships, capacity and instructional structures are instrumental in supporting student learning by supporting teacher learning. A three-year implementation plan includes the change plan, a monitoring and evaluation framework and a persuasive and active communication plan to support the change. The organizational improvement plan concludes by considering ways to ensure the plan's sustainability over time.\nKeywords: 21st century learning, collaborative inquiry, deep learning, instructional leadership, self-efficacy, social cognitive theory, transformational leadership, triadic reciprocal causation
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".