Analysis Of The Ability Of Technolgical, Pedagogical, Content, Knowledge Of Senior High School Teachers In Developing Learning Tools
Bibliographic record
Abstract
This study aims to investigate Teachers' Level of Preparation towards the Integration of Technology, Pedagogy and Content (TPAC) in an educational context. The main focus of this research is to understand the extent to which teachers have the knowledge and skills in integrating technology in the learning process with attention to pedagogy and content elements. The research method used involved a survey and quantitative and qualitative data analysis to assess teachers' TPAC. Surveys were conducted to collect demographic data and teachers' level of understanding of TPAC aspects, the samples used were 3 biology teachers in SMA Negeri 1 Kupang The results showed that the teacher's TPACK ability was quite good with a technological knowledge (TK) score of 43.6%, pedagogical knowledge (PK) of 52%, content knowledge (CK) of 75.6%, technological content knowledge (TCK) of 53.3%, pedagogical content knowledge (PCK) of 51.3%, technological pedagogical knowledge (TPK) of 61.6% and TPACK of 49.6%.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".