Theory of Planned Behavior Predicts Graduation Intentions of Canadian and Israeli Postsecondary Students with and without Learning Disabilities/Attention Deficit Hyperactivity Disorder
Bibliographic record
Abstract
We tested the ability of Ajzen’s Theory of Planned Behavior (TPB) model to predict intention to graduate among Canadian and Israeli students with and without a learning disability / attention deficit hyperactivity disorder (LD/ADHD). Results based on 1486 postsecondary students show that the model’s predictors (i.e., attitude, subjective norms, perceived behavioral control related to graduation) predicted between 32% and 59% of the variability in intention to graduate. Gender made a minor contribution to the prediction equation and age was unrelated. This suggests that our TPB measure, included in Table 1, is valid for diverse samples and countries. It can be used as a quick, inexpensive way to predict intention to graduate and, as shown by previous investigations, actual graduation rates. Thus, strengthening the three predictors may result in higher graduation rates.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".