Investigating Pre-Service Early Childhood Education Teachers’ Technological Pedagogical Content Knowledge (TPACK) Competencies Regarding Digital Literacy Skills and Their Technology Attitudes and Usage
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".