Why do I teach? Teachers' instrumental and prosocial motivation predict teaching quality across East and West
Bibliographic record
Abstract
BACKGROUND: Individuals pursue teaching careers for numerous reasons, such as for instrumental or prosocial purposes. AIMS: This study examined the personal (instrumental motivation) and social (prosocial motivation) utility of teaching as predictors of teaching quality in terms of clarity of instruction, classroom management, and cognitive activation. SAMPLE: We used data from the Teaching and Learning International Survey (TALIS) 2018, which included 50,595 teachers from 1252 schools in 10 countries and regions. METHODS: We performed a series of regression analyses to test a model of instrumental and prosocial motivation to predict three indicators of teaching quality (clarity of instruction, classroom management, and cognitive activation) while controlling for demographic characteristics (age, sex, educational level, and teaching experience). We examined this model in countries and regions from Eastern (Japan, Korea, Singapore, Shanghai and Taipei) and Western (Australia, Canada, New Zealand, United Kingdom and the United States of America) cultures. RESULTS: Results demonstrated that instrumental motivation predicted clarity of instruction in the East and classroom management in both the East and West; prosocial motivation, however, was a more consistent predictor of all indicators of teaching quality, except classroom management in the West, across cultures. CONCLUSION: Teachers' prosocial motivation to benefit others and contribute to society must be considered to understand teaching quality across various cultural contexts. Implications for theory, practice and policy are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".