Bibliographic record
Abstract
As technology becomes increasingly integrated with education, research regarding relationships between students' computer-related attitudes, affect, and motivation following technological difficulties is paramount in improving learning experiences. Previous research evaluating motivation and emotions in education has employed Weiner's Attribution Theory (1985), which proposes that perceived causal attributions made following failure events influence attribution-based emotions and subsequent actions. The present research assesses relationships between computer-based attributions and emotions following hypothetical scenarios and experimentally manipulated computer errors for university students from an eastern, research-intensive Canadian university (N = 349). The findings of this multi-study investigation presented significant relationships between computer-related attributions and emotions relative to both hypothetical scenario and experimental conditions. While hypothesized negative effects of stable attributions were observed across studies, results for personally controllable and external attributions were inconsistent. In consideration of the present findings, as well as a lack of research exploring university students' responses to technological challenges as informed by Attribution Theory, further research in which these effects are longitudinally replicated is warranted. Implications and future directions for computer-related motivational processes are also 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".