Boosters can enhance motivation treatment efficacy in achievement settings by reducing negative emotions
Bibliographic record
Abstract
Novel, unpredictable learning conditions encountered during school-to-college transitions undermine students’ personal (agentic) control, emotional resilience, and performance (Perry, 2003, 2005; Schneider & Preckel, 2017). Though motivation treatments can offset these perils in achievement settings (Perry et al., 2014), boosting motivation-treatment efficacy remains largely unexamined. In an online, two-semester introductory course, a cognitive-reframing Booster (vs. no-Booster) was administered in Semester 1, after an empirically valid motivation intervention to assess Booster benefits for students’ Semester 2 academic development (N= 911). Booster effects were assessed using ANCOVA, Moderated Multiple Regression (MMR), and path-analytic procedures. The Semester 1 Booster attenuated Semester 2 negative achievement emotions, stress, and course withdrawals, and increased course grades. MMR analyses revealed further Semester 2 Booster effects arising from students’ low perceived academic control and low performance starting Semester 1. Path analyses indicated that attenuated negative emotions mediated Booster-grades direct effects. These results advance the motivation-treatment literature by showing that cognitive-reframing boosters increase treatment efficacy in line with Weiner’s (1985, 2014, 2006, 2018) theory governing attribution-based emotion-motivation linkages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".