Investigating the Effects of Written Output and Input Enhancement on EFL Learners’ Grammatical Development

Azadeh Baghban Ferdous

Abstract


This study investigated the relative effectiveness of two types of instructional treatments, input enhancement, and output treatment via text reconstruction activity for teaching five target forms, involving 60 Iranian learners of English. The target forms were selected to address the noticing issue and noticing/triggering function of output in language learning. By administering the KET test, the researcher made sure of the learners’ homogeneity. Then, the participants were assigned to two conditions, input and input enhancement (non-output group), and input and output (the output group). After a brief grammar explanation, the learners in the non-output group engaged in comprehension of reading texts containing the target forms and underlined the target forms ( as an input enhancement technique). The learners in the output group were given the same grammatical explanation and the same reading texts. However, they were given the opportunities to reconstruct the texts as accurately as possible. The participants took two kinds of tests, namely a recognition test and two production tests. The performance of each group was compared by analyzing the learners' scores. The results of data analysis indicated that the two treatment groups were not significantly different in terms of their acquisition of target forms in the recognition test. However, the findings showed the superior performance of the output group than the non-output group in the production tests. The results suggest that although input enhancement techniques have benefits for learners' linguistic development, the output treatment due to its reflective nature and higher cognitive demand can lead learners to higher linguistic development.


Keywords


language awareness, informed-learning, form-focused instruction, input and output hypothesis, noticing, task-based language learning, language reconstruction, psycholinguistics, deep learning, contemplative learning

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