Examining public rural science high school teachers’ use of technology: portraiture in educational action research
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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