Patterns of Technological Pedagogical and Content Knowledge in Preservice-Teachers’ Literacy Lesson Planning
Bibliographic record
Abstract
This study explored the patterns of Technological Pedagogical and Content Knowledge (TPACK) in 45 preservice teachers’ literacy lesson plans that integrated digital texts or tools. A priori coding and content analysis were used to identify preservice teachers’ demonstrations of combinations of TPACK constructs. Findings indicated that preservice teachers demonstrated TPACK (41%) and combined Technological Content Knowledge and Pedagogical Content Knowledge most frequently (42%), Pedagogical Content Knowledge less frequently (13%), and other patterns rarely, combined Technological Content Knowledge and Technological Pedagogical Knowledge (1%), Technological Content Knowledge (1%), Technological Pedagogical Knowledge (0%) and combined Pedagogical Content Knowledge and Technological Pedagogical Knowledge (0%). This study cohered with previous research that found just under half of teachers demonstrated TPACK. However, it differed from previous studies that did not show patterns of Pedagogical Content Knowledge but Technological Pedagogical Knowledge, as our data showed Pedagogical Content Knowledge but not Technological Pedagogical Knowledge. Finally, it extended previous research by identifying patterns of literacy preservice teachers’ demonstrations of TPACK in their elementary literacy lesson plans. It also demonstrated new ways of combining TPACK constructs (i.e., Technological Content Knowledge and Pedagogical Content Knowledge, Technological Content Knowledge and Technological Pedagogical Knowledge, and Pedagogical Content Knowledge and Technological Pedagogical Knowledge), which when used to code the data resulted in a more comprehensive definition of TPACK. Only 2% of the lesson plans did not demonstrate any of the combinations.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".