Developing a competency taxonomy for teacher design knowledge in technology-enhanced learning environments: a literature review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Recent research in technology-enhanced learning environments has indicated the need to redefine the role of teachers as designers. This supports successful learners better able to adapt to twenty-first century education, in particular STEM education. However, such a repositioning of teaching as a design science challenges teachers to reconceptualize educational practice as an act of design, not in the artistic meaning of the word. Our recent research finding also indicated that teacher design knowledge (TDK) processes are often invisible to both the teacher educators and the teachers. To respond to these challenges, this paper will define TDK for STEM teachers by making TDK visible in the form of a TDK competency taxonomy. A systematic literature review was conducted to identify the characteristics of teaching practices in technology-enhanced learning environments. This TDK competency taxonomy consists of four main categories drawing on existing literature on teacher design work and teacher instructional design: data practice, design practice, knowledge creation practice, and professional teaching practice. The implications of these findings were discussed.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.006 |
| 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 it