Secondary pre-service teachers’ perceptions of technological pedagogical content knowledge (TPACK): What do they really think?
Bibliographic record
Abstract
Meaningful integration of digital technology into learning and teaching is ill-structured, complex, and messy. Inherent in the complexity is the interaction between the different domains of teacher knowledge. The multifaceted problem is further compounded by the diversity of learners and technology in today's dynamic classroom contexts. Pre-service teachers often feel ill-prepared to plan for effective technology integration in their classrooms. Technological pedagogical content knowledge (TPACK) has provided educators with a theoretical framework to unpack the complexity of technology integration. It sits at the heart of three interrelated components: content knowledge, pedagogical knowledge, and technological knowledge. These knowledge areas interact, support, and constrain each other. This study investigated secondary pre-service teachers’ perceptions of TPACK. Data were collected through an online survey and interviews. Following a brief introduction to TPACK, this article explores secondary pre-service teachers’ perceptions of TPACK and its components, along with their professional learning needs for TPACK development. Implications for teacher education programs are also 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".