Understanding Teacher Strengths: Self-Regulated Learning and Self-efficacy in a Canadian Sample of Pre- and in-service Teachers
Bibliographic record
Abstract
The current exploratory study examined the relationship between self-regulated learning (SRL), self-efficacy (SE), and emotions in a sample of pre- and in-service student teachers at a Canadian university. Forty-seven pre- and in-service teachers completed questionnaires that measured SRL, SE, and affect. Propensity score matching was used to compare differences in SRL and SE in a sample of 20 participants. The strength and relationship between SRL, SE, and positive and negative affect were also examined in the overall sample. Furthermore, information was obtained regarding the level at which SRL predicts SE while controlling for gender, degree of study, and positive and negative affect. Results indicated no significant differences in the levels of SRL and SE in the matched sample of pre- and in-service teachers. A moderate correlation was found between SE and positive affect. However, no significant correlations were found between SRL and SE when controlling for positive affect and a weak significant correlation was found between these set of variables when controlling for negative affect. A moderate correlation between SE and positive affect was found when controlling for negative affect. These findings suggest that affect plays an important part in the relationship between SRL and SE, especially when it comes to the sense of mastery teachers have. Results also showed that SRL significantly predicted 10% of the variance in SE in the participants of this sample. When assessing the linear relationship between SRL and SE sequentially, by controlling for gender, degree, and positive and negative affect, results suggested that adding affect to the SE prediction model was statistically significant. Similar to the correlations, affect is important in teachers’ estimations of what they are capable to do in the classroom. SRL may be an important counterbalance to negative affect and its undesirable effects on SE. Results of the present study may contribute to understand these psychological attributes in this teacher sample. Implications for teacher training programs and future directions in this area are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".