Challenges of Public High School Teachers in the Philippines in Integrating Technology in Science Education
Bibliographic record
Abstract
Due to a lack of a national dedicated agency and vision on Information and Communications Technology (ICT) in the Philippines from 1996 to mid-2016, the country has faced several barriers in raising the quality of education. Framed against the transitions of new ICT agency, this study will seek to investigate the challenges science teachers in select Philippine public high schools face in integrating ICT in their teaching. This research will use a case study approach and view the challenges of selected science teachers through technological, pedagogical, content knowledge (TPACK) and funds of knowledge (FoK) frameworks. A combination of qualitative and quantitative methodologies will be employed in the study. The quantitative data will come from survey and self-reported questionnaires. The qualitative data will come from classroom observations and interviews. The study aims to identify trends and patterns in the challenges faced by science teachers in using ICT to enhance the quality of their teaching. By viewing through TPACK and FoK frameworks, this study will inform how science teachers negotiate relationships in the teaching and learning environment among pedagogical practices, technology, funds of knowledge, and curricular content against the influence of institutional issues such as policy, administration, and management.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".