Investigating Pre-Service Early Childhood Education Teachers’ Technological Pedagogical Content Knowledge (TPACK) Competencies Regarding Digital Literacy Skills and Their Technology Attitudes and Usage
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The integration of technology into education is a substantial issue for supporting and updating teachers’ professional development in today’s world and bringing up digitally literate generations and well-educated human capital. Studies have shown that technology integration in education is a complex and multidimensional issue. TPACK transcends the triad of core knowledge types and comprises the basis for the effective integration of technology into teaching. Therefore, the present study sought to understand the contribution of the technology attitudes and usage, digital literacy skills, and online reading comprehension strategies in pre-service early childhood teachers’ TPACK competencies. The participants in the study were 481 voluntary pre-service early childhood teachers (female=398, male=83). The data were collected as a cross-sectional survey. The study findings revealed that pre-service teachers’ TPACK competencies are associated with their technology attitude and usage, digital literacy skills, and online reading comprehension strategies, as well as that the variables explained 38% of the variance. However, pre-service teachers’ grade level and GPA are not related to their self-reported TPACK competencies. These findings can be seen as signals of the necessity for theoretical knowledge and practice to be developed in pre-service teachers’ technology integration in education.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it