Technology Acceptance on Smart Board among Teachers in Terengganu Using UTAUT Model
Bibliographic record
Abstract
The purpose of this study is to seek the acceptance level of Smart Board among teachers in schools based on the construct presented by the UTAUT Model (Venkatesh et al., 2003). 68 questionnaires were distributed to respondents who are teachers in five schools in the Besut District. These schools are among the many schools that are provided with Snmart Boards by the Terengganu government. The questionnaire consists of 4 items on demography, 19 items related to the usage of Smart Boards which uses the Likert Scale. The respondents were teachers who are familiar with using the Smart Boards. The data was analysed using SPSS to get the descriptive statistics and SmartPLS to find the coefficient correlation. The findings showed that there is positive significant influence between the Performance Expectancy factor (?=0.569, p<0.01) and the Facilitating Conditions factor (?=0.295, p<0.01) towards Behavioural Intention with the value of R2=0.72. Both the Performance Expectancy and the Facilitating Conditions factors showed that 72% of the teachers have Behavioural Intention to use the Smart Board during their teaching and learning process. Further study on the acceptance of Smart Board either among the teachers or students are vital because there are not many study has been and this technology is still new in Malaysian schools.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".