E-Learning as a Platform to Enhance the Speaking Skill of Rural Women Visually Challenged Students– An Experimental Study
Bibliographic record
Abstract
In rural government colleges, there are a lot of issues faced by the teachers and the students in the teaching and learning process like infrastructure, materials, usage of technology, implementation of innovative methods, etc. Visually challenged learners need exposure to learning through technology and need to discern the value of the effective learning process through innovative methodologies, especially rural women visually challenged learners. They possess higher concentration levels when compared to the other students. Providing them E-learning platform to enhance their learning process and to acquire language skills is considered an effective and constructive mode of learning. The study aims to design an E-learning module for rural women visually challenged learners and to provide a platform for the learners to acquire language skills rather than learning. The sample of the study is rural women visually challenged learners from various rural Government colleges in Erode and Karur District, Tamilnadu, India. The methodology of the study is analyzing the needs of the learners, designing an e-learning module based on their needs, conducting pre-tests, implementing of e-learning module, and conducting post-tests. Hence the study focuses on enhancing the speaking skills of rural women visually challenged learners through an E-learning platform.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".