An introduction to functions and variables in technology education, in a STEM-centred context at the elementary level
Bibliographic record
Abstract
STEM education is gaining popularity in primary and high school curricula worldwide, emphasizing effective instructional methods. This article discusses a case study using the Technology Design Process (TDP) to create teaching materials to introduce variables and functions in a mathematical context at the elementary level. The TDP's iterative stages were used in the development, and data was collected from different sources: pre- and post-questionnaires, as well as a working document dealing with pupils' understanding based on designing, making, testing, and simulating. Nineteen students from a fourth-grade classroom (9–10 years old) participated in the study. The results indicate that STEM activities enhance classroom engagement and math learning while fostering problem-solving skills in a transdisciplinary context. This research encourages elementary teachers to incorporate more STEM activities and emphasizes the importance of the design process for critical thinking and practical skills. It also suggests that technology teachers include these design process steps in their teaching to develop engineering design skills and spark student interest in STEM subjects.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".