The Effect of Using Discourse Topic and Gender on Listening Comprehension of Iranian Advanced EFL Learners
Bibliographic record
Abstract
Having been aware of the difficulties second language learners experience with Listening tasks and its important effect on language learning, the researchers of the present study decided to see whether the presence or absence of discourse topic prior to the spoken texts and gender have any role in EFL learners’ listening comprehension. To gain these ends, two questions were posed: 1) does discourse topic affect the listening comprehension of Iranian advanced EFL learners? And 2) does gender affects the listening comprehension of Iranian advanced EFL learners. To answer these questions, the quasiexperimental method and a Two-way ANOVA was used to analyze the data and examine the nullhypotheses. Sixty-one male and 61 female EFL learners, among 191 students, were selected from a Foreign Language Institute in Iran, Tehran. The aforementioned participants were then split into two intact groups: control and target. The target group took the listening tests containing discourse topic, while the students in control group took the same listening tests without discourse topic. After the results and scores of the two groups were carefully dissected regarding their gender, it was found that those learners who had access to the discourse topic outperformed those who didn’t, and there was no significant difference in the listening performance of male and female participants. So based on the gathered data the results of this study made this researcher to claim that using the appropriate discourse topic for text in listening tasks make them easier and more satisfying to comprehend. The learners can predict the incoming discourse and guess the message of the whole text beforehand. Furthermore, they get interested in the content of the text via hearing the topic and look forward to knowing the message of the text.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".