Examining High School Students’ Personality Traits of Extraversion and Emotional Stability in Relation to their Academic Expectation and Value Appraisals
Bibliographic record
Abstract
Both personality and motivational characteristics can be considered to estimate individuals’ potential for adjustment and development. That said, these characteristics are rarely studied in conjunction. In this study, we examine how personality and motivational characteristics relate one to the other and influence how students approach learning. More precisely, we analyzed the additive and interactive effects of two intimately related traits (extraversion and emotional stability) from the Big Five model of personality on five motivational dimensions reflecting academic expectations (sense of competence) and values (interest, utility, and mastery and performance goal orientations). Data was collected from a sample of 303 students in tenth and eleventh grade (108 boys and 195 girls; Mage = 16.275 years, SD = 1.149) in a French-Canadian public high school in the greater Quebec City area. Primary analyses included both bivariate correlation and hierarchical regression analyses. Correlations indicated that emotionally stable students were more interested in learning. Hierarchical regression showed that emotionally stable students were more likely to exhibit a performance goal orientation, especially when they were extroverted. These analyses also revealed that emotionally stable students were more interested in learning, especially when they had a strong sense of competence. Results are discussed in light of the limited but relevant existing literature.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".