The role of within‐class consensus on mastery goal structures in predicting socio‐emotional outcomes
Bibliographic record
Abstract
BACKGROUND: Within-class consensus on mastery goal structures describes the extent to which students agree in their perceptions of mastery goal structures. Research on (work) teams suggests that higher levels of consensus within a group indicate a well-functioning social environment and are thus positively related to beneficial socio-emotional outcomes. However, the potential of within-class consensus to predict socio-emotional outcomes has not yet been explored in research on mastery goal structures. AIMS: This study aimed to test whether within-class consensus on the three mastery goal structures dimensions of task, autonomy, and recognition/evaluation has predictive power for socio-emotional outcomes in terms of classroom climate, negative classmate reactions to errors, and cooperative learning. SAMPLE: A total of 1,455 Austrian secondary school students (65.70% female) in 157 classrooms participated in this study. METHODS: Students responded to items measuring their perceptions of mastery goal structures, classroom climate, error climate, and cooperative learning. Items assessing mastery goal structures, error climate, and cooperative learning referred to the subject of mathematics and items assessing classroom climate referred to positive classmate relations without focusing on a subject. RESULTS: Results from multilevel structural equation models revealed that within-class consensus on all mastery goal structures dimensions predicted a less negative error climate. Additionally, consensus regarding task and autonomy predicted more frequent use of cooperative learning strategies, and consensus regarding task predicted a more positive classroom climate. CONCLUSIONS: Our findings show that higher levels of within-class consensus on mastery goal structures enhance beneficial socio-emotional outcomes. Moreover, the results emphasize the value of expanding the scope of educational research to the study of within-class consensus.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".