Teacher-Student Relationships in Diverse New Zealand Year 10 Mathematics Classrooms: Teacher Care
Bibliographic record
Abstract
Teacher-student relationships are considered influential for academic achievement and motivation, particularly for students of minority and low socio-economic groups. Teacher care is an essential component of effective teacher-student relationships. This study examined factors that contribute to developing and maintaining caring teacher-student relationships in low socio-economic multicultural classrooms (Maori, Pasifika, New Zealand European). Three areas of teacher care were explored: care for students as individuals, their mathematical progress, and for students as culturally located individuals. The sample comprised three urban schools, one class and one teacher in each school for each of two years (six Year 10 mathematics teachers and their classes in total). Three data collection periods were used: the initial four weeks of the school year, and two weeks late in each of school terms 2 and 3. Each data collection period included classroom observations, teacher and student interviews, and teacher and student questionnaires. Within a holistic context of classroom well being, characteristics of caring teacherstudent relationships were found to fit within four dispositional aspects (liking, respecting, and being tolerant of each other, and being able to reflect one's personal identity), and four themes (knowing each other as people, knowing each other as learners, knowing each other's cultures, and enhancing feelings of cultural identity). Specific classroom practices found to be supportive of respectful caring teacher-student relationships included using humour, one-to-one teacher-student interactions, making opportunities for sharing personal identities, and expecting mathematical progress. Mixed results were obtained regarding how deeply students value their heritage cultures, whether or not they believe these are well reflected in their schools and classrooms, and the extent to which they would like them to be reflected in these places. There is evidence that for many Maori, Pasifika, and low socio-economic students, mathematics teachers can enhance students' motivation and mathematical achievement by using explicitly caring practices. Teachers must acknowledge and attend to caring teaching approaches to maximise their students' progress in, and enjoyment 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".