The Degree of Achieving ISTE Standards among Pre-Service Teachers at “the Public Authority for Applied Education and Training” (PAAET) in Kuwait from Their Point of Views
Bibliographic record
Abstract
The aim of the study was twofold; first, to examine the degree of achieving International Society for Technology in Education (ISTE) standards among pre-service teachers at the college of basic education at the Public Authority for Applied Education and Training (PAAET) from their point of views and second, to examine differences in the pre-service teachers’ point of views of the degree of achieving ISTE standards based on their gender, age, major, academic year, their technological competence, their attitudes towards the use of technology, and their extent of technology use. A cross-sectional design was used; questionnaire instrument was designed and developed to collect data from the participants. The participants were student teachers enrolled in different classes in the summer semester in the academic year of 2018/2019 at the college of basic education at “the Public Authority for Applied Education and Training” (PAAET) in Kuwait. Two hundreds and eighty three pre-service teachers completed the questionnaire. The results showed that the participants expressed positive perceptions of achieving ISTE standards. The results showed insignificant variations in pre-service teachers’ perceptions of the achievement of ISTE standards based on their gender, age, major, and academic year. Furthermore, the results showed that pre-service teachers’ perceptions of the achievement of ISTE standards had significant relationships with pre-service teachers’ attitudes towards the use of technology, pre-service teachers’ perceptions of their technological competences, and pre-service teachers’ extent of using technology. Based on the findings, set of recommendations for practice and for future research were provided.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".