Towards A Model to Improve English Language Standards in Schools: Impact of Socio-Economic Factors of Stakeholders
Bibliographic record
Abstract
There is a dire need to enhance the English language standards in schools of developing countries using English as a second or Foreign language, due to its importance in employability and high-earning ability in the job market. Enhancing English language standards in schools is vital to improving the English language competence of school leavers and undergraduates to achieve an English language quality level as a nation, to produce diversified graduates of global quality, to address the unemployability problem in developing countries. Sri Lanka, with a rich history of a colonial era, is no exception. Studies on the influence of Socio-Economic factors of stakeholders on improving the English language standards in schools had received poor attention from past researchers, especially in the Sri Lankan education context. Hence, the overall purpose of this study is to develop a theoretical model, to explore the impact of socio-economic factors of stakeholders on English Language Standards in Sri Lankan schools. The study reviewed reliable secondary data published in scholarly extant literature, government Policy Documents, Research Reports of reputed institutions, etc., relevant to the above primary relationship and key concepts of the study. Six main stakeholders in the socio-economic context of the school English Language education were identified: (i) Education Policy Makers, (ii) School Management, (iii) School Principals, (iv) English Language Teachers, (v) Students, and (vi) Parents. An integrated, seven-construct conceptual model, labeled ‘ELS Model’ (English Language Standard Model), was developed, to examine the impact of socio-economic factors of the six stakeholders on improving English Language Standards in schools. This ELS Model presents original insights and future directions to scholars/researchers and significant implications for policymakers.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".