Exploring Relationships Among Resilience, Engagement, Personality, and Performance in Teacher Education
Bibliographic record
Abstract
Preservice teachers are the future of education and at the pinnacle of contemporary pedagogical practices, which could potentially influence generations of children. Investigating the relationships among preservice teachers’ resilience, engagement, personality traits, and their practicum and coursework performance is critical as it provides rich data for how preservice teachers professionally develop and succeed at the onset of their careers. Resilience, the quality of bouncing back from adversity; engagement, linked to workplace success and burnout, as well as personality, a stable trait of human behavior, are informative and measurable constructs. Self-reported grades on campus and in practicum; as well as self-evaluation on teaching confidence and preparedness are performance metrics in relation to these three psychological constructs. This study administered a five-part 39-item questionnaire to two-cohorts of 139 preservice teachers, one cohort beginning, and the other finishing their teacher education program. Descriptive statistics, factor analysis, correlation and regression analysis were completed. The results of this study indicated that engagement and two personality traits of conscientiousness and extraversion were significantly correlated and predicted four measures of performance. Although resilience was extracted as a single factor according to the BRS (Smith et al., 2008), this variable maintained no correlation to any of the four performance measures and negatively predicted self-evaluation of preparedness for teaching. Engagement was also extracted in a single factor, different from previous models using the ETS (Klassen, Yerdelen, & Durksen, 2013). Personality did not show any coherent factor structure in this study, and items were forced into respective personality factors according to previous work (Rammestedt & John, 2007). This study has important implications for teacher education and for teacher career onset and longevity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".