Building a Better Wall: Assessing Children’s Design Technology Learning in Nature-Based Early Childhood Education
Bibliographic record
Abstract
Abstract The teaching and learning of design technology that occurs in nature-based early childhood education and care (ECEC) contexts such as nature kindergartens remains under-theorised. There is a growing body of scholarship that describes how teaching and learning occurs in these contexts as well as highlighting the benefits for young children learning in the natural environment. Recently, in the perspective of the Australian ECEC sector, how students experience design technology in nature-based contexts (bush kinders, an adaption of the European forest school approach to ECEC) was reported on. Despite design technology being accounted for in bush kinders as part of play-based learning of STEM, assessment of how this learning is supporting student’s comprehension of design technology remains an area for further attention. Often, educators rely solely on observations and anecdotal note taking for assessment which points to a need to support teachers with more rigorous assessment models. This paper adapts an assessment model for science learning and reconsiders it in terms of design technology teaching and learning. The paper’s aim is to support educators to develop children’s deeper understandings of design technology and make learning meaningful in nature-based education settings. Using vignettes, the children’s learning of design technology available in natural surroundings is analysed. This paper proposes that bush kinders are a valuable context for teaching and learning as they allow educators to develop skills to assess children’s design technology knowledge. The analysis of the data and its consideration against one play-based learning assessment model is also valuable in generating a broader narrative that deepens insights into the teaching and learning experience of design technology education in early childhood nature-based contexts.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".