Integrating FoK and TPACK in action research: The impact of video creation workshops on pre-service science teachers
Bibliographic record
Abstract
This mixed-methods study presents an Asian perspective on the impact of a science video creation workshop as an intervention on the technology adoption and utilization of pre-service science teachers. It aims to offer insights relevant to Initial Teacher Education programs in international contexts, including those in Europe and North America, where most of the literature referenced in this study was sourced. Integrating the Funds of Knowledge (FoK), Technological, Pedagogical, and Content Knowledge (TPACK), and action research frameworks, the intervention provided insights into pre-service teachers' professional development. Data collection included pre- and post-test surveys, individual interviews, and focus group discussions. Quantitative analysis using paired samples t-tests revealed statistically significant improvements in technology adoption and utilization across all levels of Morel’s Matrix (2016). Triangulated with qualitative analysis, findings highlighted three key themes: enhancing science teaching through contextualized content, improving pedagogical practices via technology, and fostering inclusivity and cultural responsiveness. These results underscore the workshop’s potential in gradually developing positive beliefs toward technology integration among pre-service teachers. The study's findings emphasize the value of integrating FoK and TPACK within action research to bridge theory and practice. It provides additional evidence for technology adoption in science education. This research contributes to the limited literature on pre-service teacher education in Asia, particularly in the Philippines, and offers insights into the potential of action research to foster meaningful and sustainable changes in teaching practices within broader science teacher preparation programs across international contexts.
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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 | 0.026 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".