Self-Perception About Digital Skills of Pre-Service Teachers in a Thailand University Context
Bibliographic record
Abstract
The digital revolution has significantly impacted education, with digital technology becoming an integral part of teaching and learning, resulting in the emergence of a digital society. Educational institutions at all levels currently demand new qualifications and knowledge from modern-day teachers, including digital skills for effectively transmitting knowledge to learners to ensure that learning outcomes align with societal needs. Developing pre-service teachers poses a challenge in being educators who effectively transfer knowledge to learners. This study examined the digital skills competence required for future teachers, linking these skills with Thailand’s National Qualifications Framework for Higher Education, Professional Teacher Standards, and National Educational Standards. A total of 36 competencies across six areas have been identified. This paper analyzed 360 responses from a convenience sample of undergraduate education students in Thailand based on this validated instrument. The research findings indicated that users prioritize information technology and communication skills, necessitating a diverse range of skills, including search, tool usage, communication, and collaboration through technology. This data can be used to design learning management systems to develop digital skills aligned with the evolving needs and essential skills of future teachers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".