The Role of Oral Communication In Accessing and Assessing Mathematical Understanding: Case Studies of Primary School Teachers' Perceptions of Teaching Mathematics and Teaching Literacy
Bibliographic record
Abstract
The study investigated primary teachers’ perspectives on teaching mathematics and teaching literacy. The focus was on oral communication strategies to see if perspectives could be harmonized by building on teachers’ greater comfort with teaching literacy. Case studies in a small suburban GTA school provided qualitative data through classroom observation, on-going conversations about observed teaching episodes, semi- formal interviews with the teachers and principal at the beginning and end of the study, and one participant’s blog and research report. Participants’ teaching experience ranged from 10 to 25+ years from kindergarten to grade 7. During the study (2013-2014), members of the school staff were organized into teaching partners by grade level to teach math through inquiry with an emphasis on communicating mathematical ideas. Evidence collected from grades 1 and 3 lead to the following findings: (1) Essential resources for teaching math effectively that teachers need and want, are available; However, teachers are unaware of their existence; (2) Teachers who try to implement reform strategies without understanding how they work do not achieve the desired result; 3) A teacher with well developed processes for making sense of mathematics, can identify gaps in student understanding by relating student behaviour to her own processes; (4) When a teacher contrasts his/her sense-making teaching strategies in literacy and mathematics, he/she can better identify areas of dissatisfaction in his/her math instruction and possible strategies to try; (5) A teacher who is able to think about teaching goals in more general terms may come to see parallel objectives between certain teaching strategies in math and non-math subjects; (6) Teaching mathematics through inquiry requires students to have a solid grounding in early literacy as well as early mathematics; and (7) Reporting requirements that ask for student achievement in mathematics on a strand by strand basis encourage teachers to teach the subject strand by strand. The study has implications for effective professional development, teachers learning math content and developing teaching materials, improving teacher confidence and the development of mindful reform practice. Suggestions for stakeholders to facilitate teachers’ reform practice are included at the end of the study.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".