Features of Email Exchanges between Saudi EFL Students and Their Instructors
Bibliographic record
Abstract
This study investigates the features of email exchanges between Saudi EFL (English as a Foreign Language) students and their instructors, focusing on communicative strategies and topics. The research analyzes 250 authentic emails sent by female undergraduate students in two Saudi universities to their Saudi non-native English-speaking (NNES) instructors. The findings reveal that facilitative topics, such as class attendance and assignment submissions, were the most frequently discussed (63.95%), followed by substantive topics like assignment clarification and evaluation (35.27%). Relational topics, limited to course-related gratitude, were the least common (0.78%). In terms of communicative strategies, requesting was the dominant strategy (93.5%), primarily for information and grades, while reporting (5.6%) and negotiating (0.9%) were less prevalent. The study underscores the pedagogical implications of these email interactions, highlighting how students' reliance on facilitative and substantive topics reflects their immediate academic needs and engagement with the learning process. The predominance of requests for information and clarification suggests gaps in classroom instruction, emphasizing the need for clearer communication of course expectations. Additionally, the minimal use of relational topics indicates a transactional approach to email communication, which may limit opportunities for building rapport and collaborative learning. The study highlights the importance of adhering to English email conventions and suggests the need for explicit instruction in email etiquette and pragmatic competence. It shows that making requests is a crucial aspect of communication that requires greater focus in EFL contexts.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".