Achievement-Based Rewards and Intrinsic Motivation: A Test of Cognitive Mediators.
Bibliographic record
Abstract
This study assessed how rewards impacted intrinsic motivation when students were rewarded for achievement while learning an activity, for performing at a specific level on a test, or for both. Undergraduate university students engaged in a problem-solving activity. The design was a 2 2 factorial with 2 levels of reward in a learning phase (reward for achievement, no reward) and 2 levels of reward in a test phase (reward for achievement, no reward). Intrinsic motivation was measured as time spent on the experimental task and ratings of task interest during a free-choice period. A major finding was that achievement-based rewards during learning or testing increased participants’ intrinsic motivation. A path analysis indicated that 2 processes (perceived competence and interest–internal attribution) mediated the positive effects of achievement-based rewards in learning and testing on intrinsic motivation. Findings are discussed in terms of the cognitive evaluation, attribution, and social–cognitive theories.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".