Foldscope Embedded Pedagogy in Stem Education: A Case Study of SDG4 Promotion in India
Bibliographic record
Abstract
Most government schools and colleges run on low budgets in India, yet they cater to a large population of students. In government settings, biology labs lack basic equipment such as simple microscopes or compound microscopes or sometimes possess just one instrument. In the absence of compound or simple microscopes, the students lose interest in science. To solve this issue, the Department of Biotechnology, Ministry of Science and Technology, Government of India, introduced the origami microscope, the foldscope, into the Indian educational system. In this article, we describe the design of a sustainable, inclusive, and equitable curricula for teaching biological concepts using the foldscope. We describe the implementation of foldscope-adapted curricula in post-secondary settings to teach natural selection, developmental biology, parasitology, and economic Zoology via individual, small-group, and large-group field trips, and project-based learning that involves experiential learning. We found that these culturally sensitive pedagogies involving translingual instructions had the potential to retain students and make science education accessible for the linguistically and culturally diverse population of India. We could successfully implement our project as per the five priority action areas outlined by UNESCO. Therefore, we propose the adoption of the foldscope-adapted curricula under SDG4 to make STEM education accessible in budget-frugal settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".