A Critical Examination of the Technological Pedagogical Content Knowledge Framework
Bibliographic record
Abstract
This study explores the Technological Pedagogical Content Knowledge (TPACK) for three experienced mathematics secondary teachers from a Toronto public school. By using a multiple case study, teachers' attitudes, skills, and approaches toward the use of Information and Communications Technology (ICT) in classrooms are described. By being aware of the three main facets of TPACK (technological, pedagogical, and mathematical aspects), the relative importance of each component and their intersections were scrutinized. Although from the same school, the teachers had very different conducts of showing their integration of ICT in mathematical pedagogy and therefore, their TPACK was different. Teachers demonstrated various strategies and different paces of adopting ICT: One teacher was a later adapter of ICT with strong emphasis in pedagogy, a second teacher was an early adapter of ICT with focus on finding an adequate technical support for mathematical content, and the third teacher was a very early adopter of ICT with extraordinary capabilities to reflect on the mathematics curriculum and continuingly adapt to his classrooms' needs. It was noticed that the teachers integrated technology to (a) help them describe the concepts to students; (b) motivate students to learn mathematics; (c) give students opportunities to experiment with mathematical concepts and skills; (d) assess, evaluate, and provide feedback to student's work, and (e) help them communicate mathematical solutions. Overall, the framework shows consistency in tracing their assorted routines of integrating technology in various classroom contexts. In the end, some considerations and insights on the potential of the TPACK framework are provided.
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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 | 0.022 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.006 |
| Science and technology studies | 0.012 | 0.045 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| 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".