STEAM Camp: Teaching Middle School Students Mathematics, Science and Coding through Digital Designs
Bibliographic record
Abstract
In this study, we explore how to teach mathematics, science and coding through digital tools, design projects, and global competencies. We explore the question: How do upper elementary school children develop an understanding of mathematics and science coupled with coding through digital design? The theoretical framework adopted for this study is Kafai and Burke’s (2014) definition of Computational Participation: a shift from code to actual applications; a shift from tools to communities; a shift from starting from scratch to remixing; and a shift from screens to tangibles. We conducted a qualitative case study interlinked with Design-Based Research. Both STEAM camps were an outreach program for students in grades 4-8 in Ontario, Canada. The two camps were designed and facilitated by a research team from the Faculty of Education. The research team developed the curriculum through an iterative process (design-test-revise-repeat).  There were 43 students registered in the STEAM camps, and 34 of them participated in the study. We collected observation, interviews, audio/video recordings, and survey data as well as pictures of the students’ work. Our main findings were that students were provided with opportunities to: 1) develop a deeper understanding of curricular concepts; 2) engage more with the digital tools when they were remixing, improving, and reimaging the design; and 3) apply their knowledge to global competencies. The findings of this research have implications for improvements in researching, designing, and implementing design projects as part of a pedagogical approach to teaching mathematics and science, coupled with coding, in an interdisciplinary context.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".