An attribution-based motivation treatment for low control students who are bored in online learning environments.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Perceived control (PC) and boredom are academic risk factors that undermine motivation and performance in competitive achievement settings (Pekrun, Goetz, Daniels, Stupnisky, & Perry, 2010; Perry, Hladkyj, Pekrun, & Pelletier, 2001). Attribution-based motivation treatments (attributional retraining: AR) can assist students who exhibit single-risk factors, but AR efficacy remains unexamined for students with multiple-occurring risk factors in online learning environments. In a prepost randomized treatment study, AR was administered to students who differed in PC (low, high) and boredom (low, high) in an online, 2-semester course. For students with co-occurring risk factors (low PC–high boredom), AR (vs. no-AR) recipients performed better on a posttreatment course test, had higher control-related beliefs, and were twice as likely to remain in the course. AR (vs. no-AR) treatment effects were absent for students not having co-occurring risk factors. These results advance research on attribution-based motivation treatments for students who exhibit co-occurring academic risk factors in online learning environments. (PsycINFO Database Record (c) 2018 APA, all rights reserved)
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it