Student Motivation and Associated Outcomes: A Meta-Analysis From Self-Determination Theory
Bibliographic record
Abstract
Student outcomes are influenced by different types of motivation that stem from external incentives, ego involvement, personal value, and intrinsic interest. The types of motivation described in self-determination theory each co-occur to different degrees and should lead to different consequences. The associations with outcomes are due in part to unique characteristics and in part to the degree of autonomy each entails. In the current meta-analysis, we examine these different types of motivation in 344 samples (223,209 participants) as they relate to 26 performance, well-being, goal orientation, and persistence-related student outcomes. Findings highlight that intrinsic motivation is related to student success and well-being, whereas personal value (identified regulation) is particularly highly related to persistence. Ego-involved motives (introjected regulation) were positively related to persistence and performance goals but also positively related with indicators of ill-being. Motivation driven by a desire to obtain rewards or avoid punishment (external regulation) was not associated with performance or persistence but was associated with decreased well-being. Finally, amotivation was related to poor outcomes. Relative weights analysis further estimates the degree to which motivation types uniquely predict outcomes, highlighting that identified regulation and intrinsic motivation are likely key factors for school adjustment.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.016 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".