The Effect of Teaching Structural Discourse Markers in an EFL Classroom Setting
Bibliographic record
Abstract
<p>This study aimed to explore the effects of explicit teaching on the acquisition of spoken discourse markers (DMs) on EFL learners’ presentation production. It also aimed to measure the impact of two different treatments on the acquisition of a set of DMs.</p><p>This study is an experimental study and focuses on the overall production of spoken structural DMs in pre and post instruction where two particular teaching methods are employed. For this purpose, 41 English as a Foreign Language (EFL) female learners from the foundation program participated and they were on the Upper intermediate or B2 level on the Common European Framework of Reference Ability Scale (CEFR) at Taibah University in Saudi Arabia. Learners were divided into two groups; one group was taught using Task-Based-Language Teaching method (TBLT) while for the other group was taught using the Presentation-Practice-Production model (PPP) was used. Based on the functions of structural discourse markers, five selected topics were taught by the researcher for two hours per lesson, which makes up ten hours per group.</p><p>The study mainly aspires to answer three questions; Firstly, it explores which discourse markers do Saudi EFL learners use in giving presentations in English speaking classes (pre–test) and the reason for doing so, is to examine the progress of learners use of DMs through the whole teaching period. Secondly, it investigates which DMs do Saudi learners use after instruction in the immediate post-test and in the delayed post-test that is four weeks after the instruction. Finally, by carrying out a comparative analysis between (TBLT and PPP) the study aims to find out which teaching method is more effective and why.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".