A Technological Pedagogical Content Knowledge (TPACK) Scale for Geography Teachers in Senior High School
Bibliographic record
Abstract
With information technology being employed extensively in school education，the TPACK (Technological Pedagogical Content Knowledge) theoretical framework is adopted by a growing number of researchers to study, assess and advance teachers’ ability to integrate IT into course teaching. However, there is no measurement instrument designed specifically to assess Geography teachers’ TPACK competences in Mainland China so far. In this study, based on the currently available TPACK measurement instruments, we attempt to develop, following the 7-factor TPACK model, a measurement scale for senior high school Geography teachers in Mainland China. Invitation emails were sent to target teachers and a total of 869 valid responses were received from 9 Mainland provinces. Confirmatory factor analysis was administered on the collected data to attest convergent validity and discriminant validity of the scale, as well as the 7-factor TPACK model. As demonstrated with our research findings, the TPACK knowledge structure of senior high school Geography teachers in Mainland China accords with the 7-factor model, with factor loadings of the 37 measured variables all distributed between 0.57 and 0.94, and composite validity values of each factor ranging between 0.87 and 0.93, which indicates the scale has good convergent validity; after the seven factors being paired with each other, the chi-square value differences between constrained and unconstrained models all reach the significant level of 0.05, which indicates the scale has good discriminant validity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".