STEAM Education: Culturally Responsive Mathematics, Science, and Computing
Bibliographic record
Abstract
Approximately one-third of the population in Canadian schools consists of ethnically, linguistically, and culturally diverse students. In some cases, the majority of the student population is culturally diverse or representative of minority groups. In response to this shift in demographics, Ladson-Billings suggested that educators reflect on their teaching philosophies and practices to make them culturally responsive to their students’ needs. Although there is extensive research on culturally responsive pedagogies (CRPs), there is a lack of research on how they impact student learning and achievement in STEM (Science, Technology, Engineering, and Mathematics) and non-STEM subjects. To address the gaps in the literature on CRPs, this study will explore the following question and sub-questions through a STEAM (Science, Technology, Engineering, Arts, and Mathematics) integrated lens: What impact does CRP have on the teaching and learning of mathematics through a STEAM-integrated lens? (a) How do students develop an understanding of mathematics through coding, crafting, and prototyping using digital tools and software? (b) In what ways does culturally responsive teaching affect student motivation and engagement when learning mathematics in the classroom? (c) How can students explore their culture, identity, and interests through storytelling, coding, and mathematical thinking? (d) What are teachers’ perspectives on culturally responsive practices in the context of culturally responsive mathematics teaching (CRMT) tools? (e) What are the students’ perspectives on mathematics, and other skills learned through the making of design projects, stories, and cultural artifacts? To address these questions, we conducted a qualitative case study interlinked with Design-Based Research (DBR). The research team collected questionnaires, observation and interview data, and pictures of student work/projects. The main findings of this study were: (i) students engaged more with the technology when applying their knowledge, remixing the code, sharing resources, prototyping, and reimaging their designs; and (ii) students had rich learning opportunities when exploring their cultural identity, sharing their personal stories, and building collaborative communities. These results have implications for designing and implementing CRPs as an approach to teaching and learning mathematics and other STEAM subjects. They also have implications for optimizing the learning experience and deepening the students’ overall understanding.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".