Internet-based attributional retraining and self-esteem: investigating effects on academic achievement and attrition in post-secondary students
Bibliographic record
Abstract
Attributional retraining (AR) is a remedial intervention that targets students' maladaptive causal attributions for poor performance by encouraging controllable attributions that correspond to academic success. AR interventions are derived from Weiner's attribution theory (Weiner, 1985, 1995, 2006) that outlines how performance and achievement striving are influenced by the attributions individuals make about evaluative outcomes. Recent findings indicate that post-secondary students with high self-esteem experience unanticipated declines in academic performance following in-person AR. With a sample of 274 American university students, the current study sought to determine if this iatrogenic effect would be replicated using two versions of a recently developed internet-based AR program. Students' sessional grade point averages and rates of voluntary course withdrawal were measured longitudinally across three semesters following the intervention. Hypotheses were tested using 2 (low/high self-esteem) x 3 (Aptitude Test AR, Writing AR, No AR) x 3 (time: Winter 2007, Fall 2007, Winter 2008) repeated-measures analyses of covariance. Results revealed that the aptitude test AR format initially hurt high self-esteem students' performance, but that this iatrogenic effect reversed itself in the following semester. No effect was found for low or high self-esteem students in the writing-based AR condition. Future directions for internet-based AR research are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".