Investigating teachers’ use of student communication as a teaching strategy to promote student learning and student achievement in a grade 7 and 8 Ontario mathematics classroom
Bibliographic record
Abstract
The 2015 EQAO scores report that 50% of grade 6 students in Ontario performed below provincial expectations in Mathematics. Only 50% achieved level 3 or above in this mandated provincial annual testing in mathematics. Besides the different stakeholders’ initiatives and measures to address this alarming situation, the Ontario Education Minister has dedicated $60 million to help support mathematics education in schools and introduced 60 minutes of daily math learning in grades 1 to 8. However, current research in Mathematics education claim that “little is known from existing high quality research about what effective teachers do to generate greater gains in student learning. Further research is needed to identify and more carefully define the skills and practices underlying these differences in teachers’ effectiveness, and how to develop them in teacher education programs” (NMAP, 2008, p. xxi). This research proposal investigates how Ontario mathematics teachers are effectively using student communication as a teaching strategy to promote student learning with adolescents. It approaches teaching and learning from a constructivist perspective and makes extensive use of social learning theories. A qualitative case study approach will be used due to the complexity of the research and the presence of unknown variables.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".