Dominant Goal Orientations Predict Differences in Academic Achievement during Adolescence through Metacognitive Self-Regulation
Bibliographic record
Abstract
This study investigated whether academic achievement was predicted by the goal which generally drives a student’s learning behaviour. Secondly, the role of metacognitive self-regulation was examined. The dominant goal orientation was assessed using a new method. 735 adolescents aged 10-19 years read vignettes of students that reflect four goal orientations. Participants indicated which student they resembled most, which revealed their dominant goal orientation. Age, sex and level of parental education were controlled for. Results showed that students with motivation goals of the mastery and performance-approach types obtained higher grades than students characterized by the performance-avoidance and work-avoidance goal type. A mediation analysis showed that goal orientations predicted achievement through the level of metacognitive self-regulation. Intrinsically motivated students showed the best metacognitive self-regulation skills of all students, whereas work-avoidant students had the lowest level of self-regulation skills. The scores of students with performance goals fell in-between. The research showed that the higher grades obtained by performance-approach students, compared to performance-avoidant and work-avoidant students, can partially be explained by their higher levels of metacognitive self-regulation. Thus, goal orientation predicted achievement differences through metacognitive self-regulation skills. This suggests that intrinsic motivation and self-regulation skills should ideally be supported in the classroom. Furthermore, it suggests that teachers could use vignettes to distinguish different types of students in order to identify students who are vulnerable to lower academic achievement.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".