The Attitudes of Science and Mathematics Teachers toward the Integration of Information and Communication Technology (ICT) in their Educational Practice: The Application of the Unified Theory of Acceptance and Use of Technology (UTAUT)
Bibliographic record
Abstract
This research aims to identify science and mathematics teachers’ attitudes towards integrating Information and Communication Technology (ICT) in their educational practice through applying the Unified Theory of Acceptance and Use of Technology (UTAUT). A questionnaire instrument was developed based on the constructs of the UTAUT (performance expectancy, effort expectancy, social influence and facilitating conditions) and attitudes scale. The study sample consisted of a group of mathematics and science teachers in governorate of Ma’an. The participants were randomly selected. Descriptive and regression analysis were used to analyze the data. The results showed the attitudes of science and mathematics teachers towards integrating information and communication technology in the educational process were high and positive. In addition, the results showed that science and mathematics teachers had positive and high perceptions of integrating information and communication technology in the educational process in all dimensions (performance expectancy, effort expectancy, social influence, and facilitating conditions). Furthermore, the unified theory of acceptance and use of technology was valid in explaining the attitudes of Science and Mathematics teachers toward the integration of ICT in the in their educational practice.
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".