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Using Instructional Software to Improve Oral Performance of Taiwanese Speakers of English

Taiwanese society perceives English as an important skill; most people expend significant efforts and resources to learn English. However, Taiwanese education focuses exclusively on traditional teaching in language courses, overlooking interactions and cultural concepts. Thus, students' ability to communicate orally in English remains relatively low. Existing literature indicates that Computer-Assisted Language Learning (CALL) can enrich language learning as computers provide many functions to enhance lectures in a meaningful and effective manner. In particular, CALL can incorporate speech recognition software to help develop oral language and practice pronunciation. Integrating CALL technology into traditional language courses may improve Taiwanese students' oral performance. The current study utilized learner-centered design and second language acquisition theories as guidelines to identify relevant software; the SECTIONS model and evaluation criteria were used to select the most suitable program for integration into a traditional course.
The experimental design was administered to two sophomore English classes whose students were not majoring in English at a private university in Taiwan over an eight-week period. Pre- and posttests were used to identify language achievement during the study while a questionnaire was administered to determine attitudes toward using the software. Changes in the Spoken English Test (SET) resulting from the treatment were found to have no statistically significant difference, but the experimental group demonstrated greater improvement on the sentence mastery section and vocabulary. The perceived usefulness and the subject norm had a significant and strong influence on the adoption and use of computer technologies in participants' English oral practice.

Identiferoai:union.ndltd.org:nova.edu/oai:nsuworks.nova.edu:gscis_etd-1116
Date01 January 2009
CreatorsChen, Yentzu
PublisherNSUWorks
Source SetsNova Southeastern University
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceCEC Theses and Dissertations

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