Developing TPACK of university faculty through technology leadership roles
Bibliographic record
Abstract
This paper reports on a study that explored how faculty who take on technology leadership roles developed TPACK knowledge and built capacity for technology-enhanced teaching. The study was the second phase of a professional development initiative, called the Digital Pedagogies Collaboration, in a Faculty of Education. Four faculty, who had participated in technology workshops, volunteered to conduct workshops on technologies they had integrated into their own instruction. A qualitative case study design was used and data included pre- and post- interviews, videotaped technology workshops, and workshop artifacts. Findings show that taking on a leadership role as a workshop facilitator improved faculty members’ knowledge and skills around teaching with technology (TPACK). Moreover, the TPACK-based Professional Learning Design Model (TPLDM) was useful for designing content- centric workshops and the Faculty as Technology Leaders was a component that extended the TPACK Leadership Theory of Action Model (Thomas, Herring, Redmond, & Smaldino, 2013).
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".