Synthesizing Digital Teacher Competencies for Teaching Profession Students in Higher Education
Bibliographic record
Abstract
The synthesis of digital teacher competencies for students in the higher education teaching profession aims to: 1) synthesize digital teacher competencies for teacher students in higher education 2) assess how suitable these digital teacher competencies are for students in the teaching profession within higher education. This study employed document analysis and exploratory research. The researcher conducted an in-depth analysis of research papers and articles published from 2014 to 2023 related to digital teacher competencies in higher education students. These papers had been published in research database and were selected based on their titles and keywords. In total, 22 research papers were initially considered. After a rigorous review, 12 studies in both Thai and English were shortlisted for further analysis. The researcher synthesized a theoretical framework and a design framework. Then, a questionnaire was administered to 5 experts in the field, and their responses were analyzed using statistical measures, including averages and standard deviations. The results of the research showed that 1) digital teacher competencies for higher education teacher students consist of 9 competencies, namely: Competency 1. Knowledge, skills, abilities about digital technology, Competency 2. Adaptation and change of technology. Competency 3. Solving problems and using digital technology and organizing virtual environments 4. Ethics, morality and technological safety 5. Communication and team work 6. Teaching strategies and ICT application 7. Innovative education creators and evaluators 8.expected characteristics and learned lesson, and 9. Connecting knowledge and fostering collaboration; 2) the results of the assessment of the suitability of digital teachers for higher education students by experts are at the most appropriate level, with a mean (x ) score of 4.51, a standard deviation (S.D.) of 0.21.
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 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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".