Fostering Academic Success through English Language Learning and Teaching at Rural Universities
Bibliographic record
Abstract
In rural higher education settings, the lack of access to quality English language resources presents a significant barrier to developing the essential language skills that underpin academic success. This study examines the essential English language learning and teaching competencies required by students and faculty in rural higher education institutions, with a specific focus on the southern Tamil Nadu context. Adopting a quantitative, cross-sectional survey design, the study examines the needs of students and faculty in relation to their skills, resource types, instructional methods, facilities, and technological supports for effective English language acquisition. Two tailored questionnaires—Questionnaire A for students and Questionnaire B for faculty were disseminated through Google Forms to collect data on English language needs and practices. Frequency analysis of the responses revealed a pronounced demand for English for Academic Purposes (EAP), with participants highlighting the importance of enhanced language proficiency to support academic performance. Furthermore, the study identifies gamification as a promising pedagogical approach to increase student engagement and foster more effective language learning. The findings suggest that targeted interventions, including innovative and technology-driven teaching strategies, are essential to bridge the language proficiency gap and promote academic success in these rural educational environments.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".