Effective school leadership to support innovative teaching: mathematics education using the thinking classrooms framework
Bibliographic record
Abstract
Manitoba students are struggling in mathematics. In the 2020/21 school year, 12.8% of Grade 9 students failed to earn their mathematics credit on their first attempt; this nearly triples to a 34.2% failure rate for Indigenous students (Manitoba Education and Early Childhood Learning, n.d.). Additionally, on the 2019 Pan-Canadian Assessment Program (PCAP), Grade 8 students in Manitoba had mean scores below the Canadian mean in all mathematics subdomains (O’Grady et al., 2021). Liljedahl (2016, 2021) found that students struggle to learn mathematics in traditional classrooms because teachers are planning lessons that do not require them to actively think. In response, he developed the Thinking Classrooms framework for teaching mathematics; it’s use has exploded in Kindergarten to Grade 12 classrooms in Manitoba, across Canada and around the world. While much research has been done to develop the framework and recommend effective practices for teachers, none has examined the role of school leaders who are expected to support it. This is problematic because policymakers are expending large amounts of money, time, and physical resources for teachers to create their own Thinking Classrooms while school leaders are left to muddle through. This study asks, what are the optimal practices for school leaders who want to support the mathematics instruction of teachers who are establishing Thinking Classrooms in K-12 schools? An interview-based qualitative research approach was used to learn from the experiences of teachers and school leaders in a large metropolitan school division located in Manitoba. The results are useful for school leaders who want to support their own teachers’ implementation of the Thinking Classrooms framework and improve the education of mathematics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".