The Impact of Preservice Teachers' Emotions on Computer Use: A Formative Analysis
Bibliographic record
Abstract
Previous research on the effect of technology-based preservice education programs has been assessed by examining changes in computer ability and attitudes. Systematic exploration looking at the effect of these programs on computer use has been noticeably absent. In addition, the role of emotions and use of computers has been largely ignored with one exception, computer anxiety. The purpose of the following study was to examine the impact of four basic emotions (anger, anxiety, happiness, sadness) on use of computers by preservice teachers in their coursework (university use) and in their practice teaching (field use). Happiness was reported often while learning new software—anxiety, anger, and sadness were experienced sometimes. All four emotion constructs were significantly correlated with all four university use constructs at the beginning of the laptop program. Increased positive emotions (happiness) were significantly correlated with increased use of computers at the university by the end of the program. Finally, increases in positive emotions and decreases in negative emotions were significantly related to teacher and student-based use of computers in the field.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".