Reciprocal Learning Partnerships between Elementary Mathematics Teachers: A Partnership between Canada and China
Bibliographic record
Abstract
This study investigates the Reciprocal Learning Partnerships (RLP) between two pairs of elementary school mathematics teachers where each pair is comprised of one teacher from Changchun, China and one teacher from Toronto, Canada. These teachers are part of a seven-year reciprocal learning project focused on understanding the cultural and educational perspectives of Canadian and Chinese mathematics educators. Furthermore, this study explores how long term cross-cultural reciprocal learning partnerships can be used as a form of professional development for mathematics teachers. Data for this study was collected through interviews, classroom observations, videos, documents, journals and e-communication transcripts. These exploratory case studies focus on the teacher’s professional development needs and their personal narratives as they participate in reciprocal learning partnerships. Findings suggest Canadian and Chinese teachers have complementary strengths that make ideal reciprocal learning partners (RLP). Some findings include: (1) subject specific teacher knowledge, pedagogical knowledge, and knowledge of the learner are main areas of focus for Canada-China RLPs; (2) Canadian teachers were able to learn content knowledge from their reciprocal learning partners; (3) differentiated learning, especially for students with exceptionalities, is a common area of interest for both Canadian and Chinese teacher’s due to recent curriculum reforms in both nations; (4) cultural misconceptions are still present in Canadian and Chinese schools, and they can be challenged and corrected through reciprocal learning; (5) long-term RLPs can increase teacher efficacy; (6) RLPs are developed through three main stages where teachers learn about each other, learn from each other, and then learn with each other; (7) student-involvement in in-service teacher development can increase teacher motivation and accountability; (8) administrative and peer support are advantages to motivating RLPs; (9) when translations are involved in RLP communications, it is important to use tools that allow participants to express their personality and emotions; (10) portability, ease of use, and accessibility are important factors to consider when selecting technology for in-service teacher development. This research suggests that mathematics teachers from Canada and China are willing and eager to learn from each other to increase their teacher knowledge. Recommendations for future research are also outlined.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".