Challenges of Implementing Free and Open Source Software (FOSS): Evidence from the Indian Educational Setting
Bibliographic record
Abstract
The use of Free and Open Source Software (FOSS), a subset of Information and Communication Technology (ICT), can reduce the cost of purchasing software. Despite the benefit in the initial purchase price of software, deploying software requires total cost that goes beyond the initial purchase price. Total cost is a silent issue of FOSS and can only be evaluated in the particular environment in which it is adopted, in this case Kerala, India, fora state-level FOSS project called IT@School. This project is one of the largest deployments of free open source software FOSS-based ICT education in the world and impacts 6 million students and 200,000 teachers every year. This study analyzes the perception of 43 senior FOSS implementation project officials. It details how FOSS was introduced and reports on major challenges and how those challenges were overcome in a secondary educational setting in India. Email interviews, document analysis, and online case studies were used to collect the data. The lack of adequate resources to train the teachers was the single biggest challenge in the adoption of FOSS. The emerging strategies for efficient FOSS implementation could be used in other states in India and in other developing countries.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".