Investigation of Teacher Candidates’ Technology Competencies and Perceptions in Terms of Various Variables
Bibliographic record
Abstract
The aim of this study is to examine the teacher candidates’ technology competencies and perceptions in terms of various variables (gender, type of education, department, whether they have their own computers or not, the situation of connecting to the Internet). The study is a survey model, and the research group consists of five hundred eighteen teacher candidates studying in nine different departments in the spring term of 2018–2019 academic year at Atatürk University Kazım Karabekir Education Faculty. “Technology Perception Scale” and “Computer Competency Scale”, which is developed by Tınmaz (2004), were used as data collection tools. The Cronbach alpha value of the Technology Perception Scale was calculated as ninety-four, and the Computer Competency Scale was eighty-eight. Independent Samples “T” test and Kruskal Wallis “H” test were used for data analysis. It has been concluded that there is no significant difference in terms of technology competencies of the teacher candidates in terms of education type, department, having own computer or not, and internet connection variabilities but there is a significant difference in terms of technology competencies in terms of gender (in favor of male) and there is a significant difference in terms of perceptions of gender (in favor of men), type of education (in favor of evening education), department, whether having a computer or not (in favor of having a computer) and the variables of connecting to the Internet.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".