The Effect of Using Theme-Based Blended Learning on Egyptian EFL University Students’ Oral Performance
Bibliographic record
Abstract
This study examines the effect of theme-based blended learning on Egyptian EFL university students' oral performance. In this context, barriers such as the influence of mother tongue, students’ huge number, restricted opportunities for authentic resources, and limited classroom time often hinder fluent and confident expression. To address these challenges, a mixed-method, quasi-experimental pre-/post-test design was employed with 30 intermediate-level second-year students from the Faculty of Business Technology at the Canadian International College. Across 17 blended sessions, students engaged with authentic themes using digital tools such as Jitsi Meet, Edpuzzle, Nearpod, and WhatsApp. Quantitative analysis using paired-sample t-tests revealed statistically significant differences in overall oral performance and across strategic, discourse, interactional, and paralinguistic competencies (η² > 0.90). Qualitative data from observations, recordings, and student reflections suggested improvements overall and each oral performance competency, particularly in intonation, topic maintenance, active listening, and use of fillers. The findings indicate that integrating authentic themes within a blended learning framework promotes sustained oral practice and provide meaningful exposure to authentic language contexts. Despite limitations such as the small sample size and absence of a control group, the study offers insights into the potential of theme-based blended learning to foster engagement and support oral proficiency development.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".