When Academic Technology Fails: Effects of Students’ Attributions for Computing Difficulties on Emotions and Achievement
Bibliographic record
Abstract
As education experiences are increasingly mediated by technology, the present research explored how causal attributions for academic computing difficulties impacted emotions and achievement in two studies conducted with post-secondary students in North America and Germany. Study 1 (N = 1063) found ability attributions for computer problems to be emotionally maladaptive (more guilt, helplessness, anger, shame, regret, anxiety, and boredom), with strategy attributions being more emotionally adaptive (more hope, pride, and enjoyment). Study 2 (N = 788) further showed ability attributions for computer problems to predict poorer academic achievement (grade percentage) over and above effects of attributions for poor academic performance. Across studies, the effects of effort attributions for computer problems were mixed in corresponding to more negative computing-related emotions despite academic achievement benefits. Implications for future research on students’ academic computing attributions are discussed with respect to domain-specificity, intervention, and technical support considerations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".