The Effect of Learning vs. Outcome Goals on Self‐Efficacy, Satisfaction and Performance in an MBA Program
Bibliographic record
Abstract
Dans cette expérience de terrain, on applique la théorie de la fixation des objectifs au sentiment d’efficacité personnelle d’étudiants, à leur satisfaction envers le programme du MBA aussi bien qu’à leur performance (GPA). Immédiatement après leur avoir fixé des objectifs spécifiques élevés, nous avons mesuré le sentiment d’efficacité personnelle des étudiants en MBA. Celui des étudiants de la condition “objectifs de résultats pour la fin de l’année” (long terme) était inférieur à celui de sujets qui étaient dans la condition “faites de votre mieux” ou dans celle “but d’apprentissage”. Les sujets présentant des difficultés spécifiques en ce qui concerne les buts d’apprentissage sont plus satisfaits du programme MBA que ceux des autres conditions expérimentales. Le GPA était significativement plus élevé dans la condition “but d’apprentissage” que dans celle “but de performance”à long terme. Les sujets qui ont des objectifs à court terme et un objectif de résultat à long terme ont un GPA plus élevé que ceux qui ont seulement un objectif à long terme ou que ceux qui sont dans l’urgence de faire de leur mieux. Les implications de ces résultats pour la théorie et la pratique sont discutées. The present field experiment examined the application of goal setting theory on student self‐efficacy, satisfaction with the MBA program, as well as performance (i.e. GPA). Immediately after setting specific high goals, the self‐efficacy of MBA students who set year end (distal) outcome goals was lower than participants in either the “do your best” or the learning goal conditions. Participants who set specific difficult learning goals had higher satisfaction with the MBA program than those in other experimental conditions. GPA was significantly higher in the learning goal condition relative to the distal performance goal condition. Participants who set proximal goals, in addition to a distal outcome goal, had a higher GPA than those who only set a distal goal or those who were urged to do their best. Implications of these findings for theory and practice 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".