The tripartite model of intrinsic motivation in education: A 30‐year retrospective and meta‐analysis
Bibliographic record
Abstract
INTRODUCTION: Intrinsic motivation is a well-established concept in psychology, yet, different types of intrinsic motivation have not been thoroughly investigated. We examined covariates associated with three types of intrinsic motivation from self-determination theory (SDT) within the education context: IM to know, IM to accomplish, and IM to experience stimulation. METHODS: A meta-analysis was conducted on samples examining the tripartite model of intrinsic motivation between 1989 and 2019. In total, 78 samples met the inclusion criteria, representing 41,633 participants across multiple nationalities. The average age of participants across samples was 19 years, and 58.2% of participants were female. Path analysis and relative weight analysis were applied to meta-analytically derived correlations. DISCUSSION: Results indicated that IM to know and IM to accomplish were moderately strong predictors of adaptive student outcomes. However, results also indicated a large degree of redundancy including indistinguishable antecedent pathways. IM to experience stimulation was positively, yet, less strongly associated with adaptive outcomes. However, it did appear to be empirically distinct from the remaining intrinsic motivation types in respect to its outcomes and antecedents. CONCLUSION: Intrinsic motivation appears to be a relatively homogeneous construct within educational psychology. Specification of different types of intrinsic motivation is likely to provide only marginal benefit.
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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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.023 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".