From Chalkboards to Cultural Relevance: A Mixed-Methods Study on ELL Support in Post-Conflict Higher Education
Bibliographic record
Abstract
This study investigates the relationship between access to institutional resources and student satisfaction among English Language Learners (ELLs) at the English Language and Literature Department of X University, a post-conflict, resource-constrained higher education setting. Employing a mixed-methods approach, the research integrates quantitative data from surveys and academic writing tests with qualitative insights from classroom observations, focus groups, and semi-structured interviews. Spearman correlation analyses revealed a weak but statistically significant positive relationship between student satisfaction and access to online learning platforms (ρ = 0.184, p < .05) and language labs (ρ = 0.127, p < .05), while access to library resources (ρ = 0.082, p = .095) and textbooks (ρ = 0.041, p = .412) showed minimal or no significant association. Qualitative findings reinforced these results, with students reporting outdated materials, limited lab availability, frequent power outages, and heavy reliance on lecture-based instruction. The study further found that scaffolded instruction improved student writing scores by 22% (ρ = 0.286, p < .001), and culturally responsive teaching increased engagement by 15%. Despite their effectiveness, these strategies remain underutilized due to a lack of faculty training and institutional support. The findings underscore the need for comprehensive reforms, including investment in infrastructure, professional development in inclusive pedagogy such as Universal Design for Learning (UDL) and Culturally Responsive Teaching (CRT), and curriculum revisions to incorporate local cultural content. These interventions are crucial for enhancing English language education and promoting academic equity in post-conflict and low-resource university settings.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".