Determinants of teacher digital competence: Empirical evidence of vocational schools in Indonesia
Bibliographic record
Abstract
The goal of this study is to find and analyze the factors that affect teachers' digital competence, consisting of teacher personal characteristics (gender, age, experience, vocational teachers, and attitudes towards technology) and school context (school status, school accreditation, school leadership support, and curriculum support). The research method uses a quantitative approach with a causal design. The study included a total of 444 teachers from vocational high schools (SMK) located in western Indonesia. The characteristics of teachers that influence digital competence are attitudes towards technology and vocational teachers, and this means that teachers who teach vocational subjects have better digital competence than teachers who teach general basic subjects and local content. Curriculum support has a significant impact on vocational school instructors' digital competency. Improved curricular support enhances teachers' digital competence through effective lesson design, execution of teaching and learning activities, and utilization of digital-based practical resources. Meanwhile, age, gender, and school status are not determinants of teacher digital competence. The findings of this investigation recommend that the government, especially the Ministry of Education and the director general of vocational education, include teacher digital competence in evaluating teacher performance and become a consideration for school principals and related offices to improve technology facilities and improve the digital literacy of vocational teachers. This study distinguishes itself from previous research by investigating the determinants that impact teachers’ digital proficiency in vocational education. It specifically considers the influence of school status and vocational school accreditation, taking into account the unique combination of school-based and work-based education with diverse teaching approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.010 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".