Bibliographic record
Abstract
Background: To support a paradigm shift for 21st century learning, teacher design work is emphasized by conceptualizing teachers as designers. Despite the fact that teaching is increasingly referred to as a design science, both teacher educators and curriculum developers know little about how to enhance teacher design work in technology-enhanced learning environments. Further, teachers’ design knowledge, design experience and supports available to them are not articulated in a systematic manner.Purpose & Method: To address these issues, this study reports a systematic review of the literature on the design work of teachers within technology-enhanced learning environments, in STEM domains. In this review, there are four main themes: the context where teachers’ design work takes place, the form that the design work took, the aspect/phase of design process that the paper focuses on, and details of any supports that assist teachers in the work of design.Findings: Teacher design work takes place in a variety of contexts, and teacher design work also takes many forms. Research that reports on design work tends to focus on the implementation and evaluation components of the design process. Teachers have access to a variety of supports, including design materials and design frameworks.Conclusions: This synthesis identifies future areas of research in supporting STEM teachers’ design knowledge.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".