Technology, Pedagogy, and Content Knowledge: An Australian Case Study
Bibliographic record
Abstract
Teacher Education students, at the bachelor’s and post-graduate level, complete programs that expose them to educational theories and best teaching practices. However, the extant literature has repeatedly demonstrated that many preservice teachers (PST) are unprepared to apply such knowledge to real-world educational settings. The problem may be particularly acute when it comes to the use of technology in classrooms. Given increasing government investment in technology and the burgeoning digital industries, teachers can play a critical role in demonstrating the effective use of technology in the course of teaching and learning. This study used a survey based on the Technological Pedagogical Content Knowledge (TPACK) model to evaluate PSTs self-perceived competencies in integrating technology into their teaching practices. Over a span of two years, PSTs enrolled in a unit offering six weeks’ professional experience were invited to respond to the survey and rate their cognizance of relevant teaching practices. Respondents indicated some familiarity with TPACK, but significant gaps were also evident. Moreover, despite the lack of significant differences among age groups in PSTs perceived ability to apply the TPACK model, noticeable differences were observed in their experiences regarding gender and prior employment.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".