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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.013 | 0.001 |
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".