Examining public rural science high school teachers’ use of technology: portraiture in educational action research
Bibliographic record
Abstract
Recent initiatives in the Philippines, the site of this study, have stressed the importance of teachers engaging in education research, emphasizing its importance in professional progress. In this study, we employed the, sometimes characterized, ‘messiness’ of action research to reframe the relatively uncontrolled circumstances brought about by the wider implementation of information and communication technology (ICT) policies and initiatives in the context of science education. We draw from the first author’s overlapping identity as an insider-outsider-in-between in relation to the five science teacher-participants and their pre-to-post video production engagements and reflections during science video workshops throughout a 45-day fieldwork. We also draw on portraiture methodology to examine select science teachers’ challenges as they integrate ICT in their classes. Portraiture, with its emphasis on how people construct, co-construct, and characterize their lived experiences, offers researchers a set of approaches that offer a sense of participants’ agency, and to gain a better sense of their life experiences. With further analysis of participants’ interviews during the pre-to-post video production stages framed through the lenses of technological, pedagogical, content knowledge (TPACK) and funds of knowledge (FoK), this study offers three portraits of science teachers’ challenges. These portraits highlight teachers’ understandings of, and responses to Philippine government policy in ICT implemented in public and rural high schools in the country, teachers’ use of technology for their professional development, and their science teaching practices rooted in local knowledge.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.026 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".