The Impact of Preservice Teachers' Emotions on Computer Use: A Formative Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it