Expanding Teacher's technological, pedagogical, and content knowledge with funds of knowledge: An exploratory STEM professional development model using video creation workshops
Bibliographic record
Abstract
Abstract Recent initiatives in the Philippines have underscored the significance of 21st‐century approaches to preparing K‐12 STEM public teachers to embrace technology‐enhanced pedagogies. This case study, part of a larger investigation, employed portraiture methodology to examine one science teacher's growth in technological, pedagogical, and content knowledge (TPACK) while integrating students' funds of knowledge (FoK) in a 4‐week science video creation workshop. The workshop trained the teacher as a learning doctor to diagnose teaching and learning impediments during pre‐ to post‐video production. Data included the teacher's pre‐ to post‐production video creation experience, reflections, and individual interviews. Findings indicated: (a) a gradual growth from a self‐assessed detached TPACK to an expanded TPACK, (b) concrete FoK integration, which served as a bridge to widen the teacher's TPACK, and (c) effective science video creation workshop, viewed through the lens of a science teacher as a learning doctor, offered explicit scaffolding to address teaching and learning impediments during video creation. The findings suggest that science video creation workshops represent an exploratory and innovative model for deliberate professional development (PD) in STEM education, particularly for teachers in rural areas. This model highlights the relevance of research integrating FoK and TPACK and offers a new approach to enhancing teachers' TPACK. The findings have potential implications for advancing PD for rural science teachers in the Philippines and STEM educators in rural areas globally, emphasizing the value of rural schools as centers for relevant pedagogical innovation in STEM 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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".