The Impact of Pushed Output on Oral Proficiency of Iranian Efl Learners
Bibliographic record
Abstract
AbstractDue to lack of quantitative investigations that either support or refute Pushed Output Hypothesis (Swain, 1985), current study attempted to establish baseline quantitative data on impacts of pushed output on oral proficiency of Iranian EFL leamers.To achieve this purpose, 30 female EFL learners were selected from a whole population pool of 50 based on standard test of IELTS interview and were assigned into an experimental group and control group using a stratified random assignment procedure. The participants in experimental group received pushed output treatment while students in control group received non-pushed output treatment. The data were collected through IELTS interview for measuring oral proficiency in both pre-test and post-test. The statistical results reveal that experimental group outperformed control group in oral proficiency (pKey words: pushed output, oral proficiency, EFL learners1.1. IntroductionUp to present, all scholars in field of applied linguistic have been encouraged to study how second language learners can acquire oral proficiency. The demand for oral proficiency in English has been sharply increasing because of strong situation of English as a language for international communication.Many studies in second language acquisition have been carried out to investigate how input and output contribute to language learning development. The results of all studies can be interpreted both from language learning perspectives and teaching perspectives. Output, as its name appears, refers to language in which a learner produces and a listener perceives. In last two decades, researchers concentrated more on input rather than output in their studies as an element for acquiring second language. However, recently some researchers have focused more on role of output practice in acquiring language (e.g., Hanaoka, 2007; Izumi, 2003; Kormos, 2006; Swain, 1995, 2005).The understanding and definition of pushed output, for most part, is grounded in Swain' data collection from a Canadian French immersion program. Swain (1985,1995) mentioned that immersion program in Canada proved that comprehensible input alone was insufficient to ensure that learners achieved language acquisition. Her observation showed that immersion students achieved near-native-like second language (L2) comprehension but they did not achieve near-native-like L2 production abilities.Regarding what contributing factors can assist language learners to develop their oral proficiency, and scarcity of empirical studies that support or rebut Pushed Output Hypothesis (POH) (Swain, 1985); specifically, this study aims at examining impact of Pushed Output (PO) on oral proficiency of Iranian EFL learners.1.2. Pushed Output HypothesisAs mentioned above, Merill Swain (1985) disregarded input as playing a significant role for language acquisition. Her observation on immersion program revealed that production was necessary to acquisition. Hence, Swain (1985) proposed a concept and termed Pushed Output Hypothesis (POH). According to Swain (1985), PO is output that extends linguistic repertoire of learner as he or she attempts to create precisely and appropriately desired meaning (p. 252). Swain (1985) also notes that when learners are pushed to engage in production, they have chance to deliver messages which are precise, coherent and appropriate (p. 249). In other words, L2 learners are pushed to modify their initial production in order to facilitate native speakers' understanding by modifying their linguistic output in a more targetlike way (Makey, 2012). Izumi (2002) notes the importance of output in learning may be construed in terms of learners' active deployment of their cognitive resources (p. …
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".