A Psychometric Re-Examination of the Science Teaching Efficacy and Beliefs Instrument (STEBI) in a Canadian Context
Bibliographic record
Abstract
A teacher’s self-efficacy has been found to be one of the most important factors contributing to a successful teaching–learning outcome for both the teacher and the students. Numerous studies have shown that there is a relationship between students’ self-efficacy, students’ academic achievement, teacher burnout and a teacher’s sense of self-efficacy. In this study, the psychometric properties of the Science Teaching Efficacy and Beliefs Instrument (STEBI) by Riggs and Enochs (1990) were re-examined in a Canadian context utilizing data of 1630 teachers from the Pan-Canadian Assessment Program (PCAP) in 2013. Exploratory factor analysis (EFA) and its associated methods were used to investigate the factorial structure of the STEBI, and Cronbach’s alpha was calculated as a measure of reliability. The results showed adequacy of a two-factor solution and similar overall patterns of factor loadings across orthogonal and oblique rotations. In terms of reliability analysis, both factors had reliability coefficients lower than the original scale. The implications of these findings and the future directions for research 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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".