Open Educational Resources: Challenges and Opportunities in Indian Primary Education
Bibliographic record
Abstract
Education is a fundamental human right, yet one fifth of the world’s population lives with poor literacy. India is home to the largest number of illiterate people, with infrastructural, cultural, and socio-economic factors hindering equitable access to quality education. Due to the rapidly growing technology and Internet usage in the country, open educational resources (OER) are increasingly being used as a vital tool to help transcend barriers to child literacy, also aiding in educational attainment. While an array of scholarly works provides evidence of the potential in OER to influence higher education outcomes in developing nations, academic analysis of their impact on primary level education attainment has been minimal. This paper retrieve lessons from three children’s content providing organizations to understand the opportunities and challenges of OER in primary-level education in developing nations with similar cultural, infrastructural, and socio-economic issues. While the findings of this study suggest that the use of OER allows for greater distribution and scale across different cultural and linguistic settings, particularly in rural and remote regions, they also warn against the adaptation and pedagogical barriers of OER into societies where traditional modes of education are established and trusted.
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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 | 0.004 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".