The Effects of Teachers’ Technological Pedagogical Content Knowledge (TPACK) on Students’ Scientific Competency
Bibliographic record
Abstract
The integration of Technological Pedagogical Content Knowledge (TPACK) into instructional design is pivotal for teachers. This intricate knowledge framework encompasses the interplay between technology, pedagogy, and subject matter, profoundly impacting the multifaceted aspects of student learning, encompassing knowledge acquisition, skill development, and the cultivation of desirable attributes. This research at hand adopts an exploratory approach, examining two sample groups: 1) Science teachers from secondary schools under the Northeastern Region of Thailand during the academic year 2565–2566, totaling 124 individuals, and 2) Secondary school students receiving instruction in science-related subjects from the aforementioned teachers, with a minimum of one classroom involved. Data collection tools include a survey on TPACK, a scientific competency assessment, and semi-structured interviews. Statistical analysis employs mean, standard deviation, and content analysis. Testing of hypothesis uses One-Way Analysis of Variance (One-Way ANOVA). The study reveals that students taught by science teachers with varying levels of TPACK exhibit statistically significant differences in scientific competency at a 0.05 significance level. When comparing scientific competency with TPACK levels of teachers, statistically significant differences at the 0.05 level are found in two pairs: 1) the Adapting level and the Advancing level and 2) the Exploring level and the Advancing level.
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.
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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".