Preservice teachers’ preparedness to integrate computer technology into the curriculum
Bibliographic record
Abstract
For Canada to compete effectively in the digital world, beginning teachers need to play an important role in integrating computer technology into the curriculum. Equipment and connectivity do not guarantee successful or productive use of computers in the classroom, but the combination of the teaching style and technology use has the potential to change education. In this research, the computer self-efficacy beliefs of 210 preservice teachers after their first practice teaching placements were examined. First, the quantitative component of the study involved the use of Computer User Self-Efficacy (CUSE) scale where students’ previous undergraduate degree, licensure area, experience and familiarity with software packages were found to have statistically significant effects on computer self-efficacy. Second, the qualitative data indicated that society and school were the most positive factors that influenced preservice teachers’ attitudes towards computers, while the family had the highest percentage of negative influence. Findings reveal that although preservice teachers had completed only two months of the program, those with higher CUSE scores were more ready to integrate computers into their lessons than those with lower scores. Résumé: Pour que le Canada puisse entrer en compétition dans le monde numérique, les nouveaux enseignants devront jouer un rôle important d’intégration des technologies informatiques dans le curriculum. Les équipements et la connectivité ne garantissent pas une utilisation gagnante ou productive de l’ordinateur en salle de classe, mais la combinaison de styles d’enseignement et d’usages de la technologie a le potentiel de changer l’éducation. Dans cette étude, les croyances d’auto-efficacité à l’ordinateur de 210 futurs enseignants après leur première affectation ont été examinées. Premièrement, la partie quantitative de l’étude impliquait l’utilisation de l’échelle du Computer User Self-efficacy (CUSE) qui a montré un effet statistiquement significatif des études de premier cycle des étudiants, du domaine dans lequel ils sont certifiés pour pratiquer, de l’expérience et de la familiarité avec des logiciels sur l’auto-efficacité avec les ordinateurs. Deuxièmement, les données qualitatives indiquent que la société et l’école sont les facteurs les plus positifs qui influencent l’attitude des futurs enseignants par rapport aux ordinateurs, alors que la famille a l’influence négative la plus forte. Les résultats ont montré que malgré le fait que les futurs enseignants n’avaient complété que deux mois de leur programme, ceux qui présentaient un score CUSE élevé étaient plus enclins à intégrer les ordinateurs dans leurs leçons que ceux qui avaient obtenu un score plus faible.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".