Exploring AR and VR Tools in Mathematics Education through Culturally Responsive Pedagogies
Bibliographic record
Abstract
Augmented reality (AR) and virtual reality (VR) have been noted to enhance student learning by supporting spatial reasoning and visualization, long-term memory, engagement, and increased motivation. The researchers situated the exploration of these tools for learning within the culturally responsive pedagogy (CRP) in mathematics education. The researchers conducted a qualitative case study interlinked with design-based research (DBR). Data were collected using questionnaires, interviews, observations, and documentation. Sixty-five students in grades three to eight and 10 adults participated in the context of a STEAM camp. Students used tools such as block-based coding and digital and crafting design materials to learn, understand, and apply mathematical concepts (i.e., for mathematical thinking and modelling). The researchers designed and taught the learning activities which used a game-design-like coding software and Cospaces Edu app. The AR and VR activities were within the learning contexts of storytelling and cultural artifacts. The researchers report the study's results, analyzing mathematics concepts learned through the activities. Specifically, students' motivation was boosted when students used game-design-like software within the storytelling context of the app.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".