How capable are they of Becoming a Digital Teacher? Correlation Analysis of Individual Characteristics, Digital Self-efficacy, and Digital Citizenship among Pre-service Teachers in Northeast Thailand
Bibliographic record
Abstract
Understanding pros and cons of digital technology is an essential competency for pre-service teachers so that they are able to conduct online instruction appropriately. They need to prepare themselves comprehensively to be able to use the new educational technology that is now widely accessible particularly since the Covid-19 pandemic incidence. This paper investigates the relationship between individual characteristics, digital self-efficacy, and digital citizenship among pre-service teachers in higher education. Cluster sampling was used to select 384 pre-service teachers from three higher education institutes located in the northeast of Thailand. A research tool used in the research was an online questionnaire. The data analysis utilized descriptive statistics, the Chi-square test, and the Pearson correlation. The research discovered that pre-service teachers had a relatively high level of digital self-efficacy, while having relatively low level of digital citizenship. Correlation analysis observed a positive relationship among individual characteristics, digital self-efficacy and digital citizenship. The implications of this research highlighted the significance of implementing civic education in higher education level in order to foster adequate knowledge, skill, and awareness of citizenship in pre-service 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| 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".