Characteristics of mathematics classroom teaching in elementary schools in Chongqing, China: Canadian mathematics teachers’ perspectives.
Bibliographic record
Abstract
With increasing interests in understanding Chinese students’ outstanding mathematics achievements in international comparative studies, considerable research has focused on characteristics of mathematics classroom teaching in China. In this paper, we contribute to understand this issue from an outsider perspective through investigating the visiting Canadian team which consisted of two elementary mathematics teachers, two school principals, and one mathematics education researcher. Research data were collected through direct and indirect interactions between a pair of Windsor-Chongqing research ‘Sister Schools’, including Skype meetings; formal and informal conversations with teachers; and most importantly, an all-day onsite session during which visiting Canadian teachers interacted extensively with their Sister School counterparts in Chongqing, China. Hinged on instructional and cultural perspectives, data analyses were based on grounded theory that was utilised to deeply explore the characteristics of effective mathematics teaching from the perspectives of the Canadian team. Findings reveal that those characteristics include (a) The integration of the history of mathematics into classroom mathematics teaching; (b) The development and implementation of well-structured lessons; (c) The adoption of an active student-teacher, and teacher-student interaction style; (d) A mathematical knowledge-package summary made by the students at the end of the lesson.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".