Predicting undergraduate student outcomes: Competing or complementary roles of self-esteem, self-compassion, self-efficacy, and mindsets?
Bibliographic record
Abstract
Whereas several individual differences have been shown to predict academic and psychological outcomes among university students, it is not always clear which are most impactful, in part because many of the constructs overlap. Thus, the purpose of the present study was to examine the unique contributions of self-esteem, self-compassion, self-efficacy, and mindsets when predicting outcomes among university students. Undergraduate students (N = 214) completed an online survey including measures of the predictors as well as the outcomes of self-control, mental health, and both course and term grades. Correlations confirmed the overlap among the predictors highlighting the importance of examining the unique contributions of each. Results of multiple regression analyses showed that self-esteem and self-compassion explained unique variance in depression and anxiety over and above self-efficacy and growth mindsets. In contrast, self-efficacy and growth mindsets each significantly predicted self-control when controlling for self-esteem and self-compassion. Only self-efficacy predicted course grades. Given our results, we suggest that self-compassion and one’s beliefs about their abilities are complementary strengths for students attending university and should be considered when designing interventions to improve outcomes.
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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 | 0.009 |
| 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 | 0.001 | 0.001 |
| 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".