Students’ perception on blending mathematics teaching with kindness
Bibliographic record
Abstract
This paper showcases the effectiveness of integrating deliberate acts of kindness (DAKs) into mathematics education at the university level. Kindness was incorporated into engaged labs, mid-class online quizzes, small-group collaboration, and final grade calculations. Instructors also learned students’ names, valued their input, and shared personal anecdotes. We analyse both qualitative and quantitative survey responses from 248 first-year students of an introductory calculus class at a large Canadian university. This study explores the interplay among student perceptions of school kindness, active and effective learning, classroom supportiveness, optimism, prosocial and social goals, life satisfaction, and academic self-efficacy. The results show that instructors’ caring behaviours were correlated with positive perceptions of instructor kindness. Furthermore, students of empathetic caring instructors had significantly higher school kindness scale scores. The insights from this study motivate educators to incorporate DAKs into their teaching to enhance student well-being. Additionally, these findings can inform future pedagogical studies and foster advancements in pedagogy research.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".