A Corpus-Based Analysis of Discourse Markers in ESL Writing Proficiency: Implications for Vocabulary Expansion, Writing Anxiety, and Cultural Context
Bibliographic record
Abstract
The use of discourse markers (DMs) is a critical component of writing proficiency in English as a Second Language (ESL), influencing coherence and cohesion in academic texts. This study investigates the variation in discourse marker usage between first-year and fourth-year Egyptian university students to understand how proficiency levels affect written cohesion. A corpus of 400 student essays was analyzed using a mixed-methods approach, incorporating both quantitative frequency analysis and qualitative functional categorization. Results indicate that first-year students overuse basic additive markers (and, but, also), leading to redundant structures and limited textual variety. In contrast, fourth-year students employ a wider range of contrastive and inferential markers (however, therefore, thus), demonstrating greater discourse competence and improved logical flow. The findings highlight the developmental trajectory of ESL learners and suggest that increased exposure to academic writing conventions supports more effective discourse structuring. These results underscore the need for targeted pedagogical interventions that emphasize the nuanced use of DMs in writing instruction. Future research should explore instructional strategies that facilitate the effective integration of DMs across different proficiency levels and examine the role of explicit discourse marker training in fostering advanced writing skills.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".