Investigation of the Self-efficacy of the Teachers in Technological Pedagogical Content Knowledge and Their Use of Information and Communication Technologies
Bibliographic record
Abstract
The aim of this study was to determine the teachers' views on technological pedagogical content knowledge (TPACK)self-efficacy and the frequency of using ICT in education and to examine whether there is a differentiation in theseviews according to certain variables. In addition, the relationship between teachers' use of ICT and TPACK-ISTEself-efficacy in various processes is examined. The study which is carried out with a descriptive survey, causalcomparison and correlational survey models includes 387 secondary and high school teachers as study sample. In thestudy, teachers consider themselves very competent especially in terms of content knowledge. TPACK-ISTEself-efficacy of vocational and technical teachers was significantly higher than that of the science and mathematicsteachers. While teachers benefit from ICT tools less frequently in measurement and evaluation as well as inpresenting the course subject; in terms of presenting the course subject, high school teachers use ICTs significantlymore often than the secondary school teachers; those who did receive in-service training on the use of technology ineducation use ICTs significantly more often than those who did not receive it. It is seen from the study results thatthe variables such as preparing the lecture notes, presenting the course subject and doing study explain theTPACK-ISTE self-efficacy significantly.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".