Exploring Teachers’ Technological Pedagogical Content Knowledge as an Indicator for the Planning of In-service Teacher Training in Chemistry Education
Bibliographic record
Abstract
This study examined how teachers teaching chemistry at different levels of education perceive their levels of technological pedagogical content knowledge (TPACK) and examined the relationship of TPACK with age, gender, teaching at different levels of education, time spent teaching chemistry, and frequency of information and communication technology (ICT) use. The study involved 261 teachers, 246 women and 15 men, from all over Slovenia, who have been teaching chemistry for an average of 18 years at different levels of education, with an average age of 45 years. The results showed that teachers teaching chemistry content perceive a high level of TPACK. There is a statistically significant correlation between age, time spent teaching chemistry, and frequency of ICT use with the perceived level of technological pedagogical content knowledge. Younger teachers, those with less professional experience and teachers who use ICT more frequently rated their TPACK higher. Based on the results of the survey, guidelines for planning the in-service teacher training that would support the development of TPACK of teachers teaching chemistry content were developed.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".