Appraising Computing Self-Efficacy Emotions across a 5-Week Multimedia Authoring Project
Bibliographic record
Abstract
Emotion is a key aspect of how non-specialists learn computing. The emotions included in Computing Self-Efficacy (CSE) research were identified prior to the emergence of recent models of emotion. There has been no attempt to inventory attitudes elicited while learning computing, using contemporary psycholinguistic models of subjectivity. This study of 58 medical students in Saudi Arabia used Appraisal analysis of weekly written personal responses to gain a comprehensive overview of emotions elicited during five weeks’ instruction on website-building. A Before-After Survey identified gains made in reported frequency of tasks performed outside class. A Weekly Attitude Survey identified the strength of 6 previously-identified CSE emotions, framed as positive-negative pairs. Participant journals showed that many emotions included in previous CSE emotions are not frequently-realised, and attitudes are changeable across the learning process. Overall, most positive-negative pairs do not behave correlatively, some persist where others progress, and incidence is a better guide than polarity to an attitude’s significance. Capacity and confidence suggest three stages in learning a computing task.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".