An Analysis of Social Studies Teachers’ Perception Levels Regarding Web Pedagogical Content Knowledge
Bibliographic record
Abstract
Web pedagogical content knowledge generally takes pedagogical knowledge, content knowledge, and Web knowledge as basis. It is a structure emerging through the interaction of these three components. Content knowledge refers to knowledge of subjects to be taught. Pedagogical knowledge involves knowledge of process, implementation, learning methods, and teaching methods. Web knowledge is about general Web competencies such as the use of tools related to the Web, Web-based communication, and Web-based interaction. The purpose of this study is to analyze social studies teachers’ perception levels regarding Web pedagogical content knowledge. The population of the study covers social studies teachers in Turkey while the sample of the study covers 601 social studies teachers who were randomly selected from 75 cities of Turkey in 2015. Data collection tool employed in this study is Web Pedagogical Content Knowledge Scale composed of 30 items and five factors, developed by Lee, Tsai, and Chang (2008), and adapted to the Turkish language by Horzum (2011). Data analysis of the study was conducted via IBM SPSS Statistic 23 package. The findings were analyzed based on arithmetic mean, standard deviation, Mann-Whitney U test, and Kruskal-Wallis test. The significance of the data was evaluated at a significance level of 0.05. The results indicate that social studies teachers’ perceptions regarding Web pedagogical content knowledge are high. The results also show that they consider themselves competent in this matter, and their perceptions regarding Web pedagogical content knowledge significantly vary by gender, the Department of graduation, and experience of using computers whereas they do not significantly differ by educational background and status of having a computer.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".