Alleviation of social injustices in STEM education: Harnessing pedagogical affordances of virtual and augmented reality applications through open learning
Bibliographic record
Abstract
This paper explores the extent to which pedagogical affordances of virtual and augmented reality (VAR) applications can be harnessed as a means to alleviate social injustices in science, technology, engineering and mathematics (STEM) education through open learning. The enhancement of epistemic and epistemological access in STEM education requires coherent implementation of appropriate strategic interventions which are essentially geared towards the promotion of pedagogic innovation in its broadest sense. The empirical investigation adopted a qualitative research design located within the interpretivist paradigm. Qualitative data was collected through semi-structured interviews. The study is underpinned by the theory of social justice framework as a theoretical lens. Key findings emanating from the study demonstrated that sustainable integration of VAR applications in STEM education can essentially be harnessed as a catalytic tool to address the articulation gap between school and higher education through parity of participation within the broader South African context. The realisation of this key strategic imperative hinges to a large degree on the critical interrogation of enablers and constraints about sustainable utilisation of VAR applications in STEM teaching and learning.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".