Teachers' goal orientations: Effects on classroom goal structures and emotions
Bibliographic record
Abstract
BACKGROUND: Prior research has shown teachers' goal orientations to influence classroom goal structures (Retelsdorf et al., 2010, Learning and Instruction, 20, 30) and to also impact their emotions (Schutz et al., 2007, Emotion in education, Academic Press, Amsterdam, the Netherlands). However, empirical research evaluating possible causal ordering and mediation effects involving these variables in teachers is presently lacking. AIM: The present 6-month longitudinal study investigated the relations between varied motivational, behavioural, and emotional variables in practising teachers. More specifically, this study examined the reciprocal, longitudinal relations between teachers' achievement goals, classroom goal structures, and teaching-related emotions, as well as cumulative mediational models in which observed causal relations were evaluated. SAMPLES: Participants were 495 practising teachers from Canada (86% female, M = 42 years). METHODS: Teachers completed a web-based questionnaire at two time points assessing their instructional goals, perceived classroom goal structures, achievement emotions, and demographic items. RESULTS: Results from cross-lagged analyses and structural equation modelling showed teachers' achievement goals to predict their perceived classroom goal structures that, in turn, predicted their teaching-related emotions. CONCLUSIONS: The present results inform both Butler's (2012, Journal of Educational Psychology, 104, 726) theory on teachers' achievement goals and Frenzel's (2014, International handbook of emotions in education, Routledge, New York, NY) model of teachers' emotions in showing teachers' instructional goals to both directly predict their teaching-related emotions, as well as indirectly through the mediating effects of classroom goal structures.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".