Why Do College Students Cheat? A Structural Equation Modeling Validation of the Theory of Planned Behavior
Bibliographic record
Abstract
Cheating on tests is a serious problem in education. The purpose of this study was to test the efficacy of a modified form of the theory of planned behavior (TPB) to predict cheating behavior among a sample of Saudi university students. This study also sought to test the influence of cheating in high school on cheating in college within the framework of the TPB. Analyses were conducted on a sample of 322 undergraduate students using structural equation modeling. The results were consistent with the TPB model’s predictions. The TPB model explained a modest variance in cheating in college. When cheating in high school added to the model, the proportion of explained variance increased and cheating in high school was the best predictor of cheating in college. Although not hypothesized by the TPB, subjective norm had a direct effect on attitude.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Open science | 0.001 | 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 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".