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Tzu-Yu Tai – Computer Assisted Language Learning, 2024
Intelligent personal assistants (IPAs) are a valuable tool in language learning because they provide opportunities for authentic interaction. However, their effectiveness, compared with that of human interlocutors, in facilitating second and foreign language interaction has not been explored. Therefore, this study investigated the effect of IPAs…
Descriptors: Artificial Intelligence, Natural Language Processing, English (Second Language), Second Language Learning
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Tzu-Yu Tai; Howard Hao-Jan Chen – Computer Assisted Language Learning, 2024
English speaking is considered the most difficult and anxiety-provoking language skill for EFL learners due to lack of access to authentic language use, fear of making mistakes, and peers' negative comments. With automatic speech recognition and natural language processing, intelligent personal assistants (IPAs) have potential in foreign language…
Descriptors: English (Second Language), Speech Communication, English Language Learners, Anxiety
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Huang, Ping-Yu; Tsao, Nai-Lung – Computer Assisted Language Learning, 2021
In this article, we describe an online English collocation explorer developed to help English L2 learners produce correct and appropriate collocations. Our tool, which is able to visually represent relevant correct/incorrect collocations on a single webpage, was designed based on the notions of collocation clusters and intercollocability proposed…
Descriptors: Second Language Learning, Second Language Instruction, English (Second Language), Error Correction
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Timpe-Laughlin, Veronika; Sydorenko, Tetyana; Daurio, Phoebe – Computer Assisted Language Learning, 2022
Often, second/foreign (L2) language learners receive little opportunity to interact orally in the target language. Interactive, conversation-based spoken dialog systems (SDSs) that use automated speech recognition and natural language processing have the potential to address this need by engaging learners in meaningful, goal-oriented speaking…
Descriptors: Second Language Learning, Second Language Instruction, Oral Language, Dialogs (Language)
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Moussalli, Souheila; Cardoso, Walcir – Computer Assisted Language Learning, 2020
Second/foreign language (L2) classrooms do not always provide opportunities for input and output practice [Lightbown, P. M. (2000). Classroom SLA research and second language teaching. Applied Linguistics, 21(4), 431-462]. The use of smart speakers such as Amazon Echo and its associated voice-controlled intelligent personal assistant (IPA) Alexa…
Descriptors: Artificial Intelligence, Pronunciation, Native Language, Listening Comprehension
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Goh, Tiong-Thye; Sun, Hui; Yang, Bing – Computer Assisted Language Learning, 2020
This study investigates the extent to which microfeatures -- such as basic text features, readability, cohesion, and lexical diversity based on specific word lists -- affect Chinese EFL writing quality. Data analysis was conducted using natural language processing, correlation analysis and stepwise multiple regression analysis on a corpus of 268…
Descriptors: Essays, Writing Tests, English (Second Language), Second Language Learning
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Pérez-Paredes, Pascual; Ordoñana Guillamón, Carlos; Aguado Jiménez, Pilar – Computer Assisted Language Learning, 2018
Combined with the ubiquity and constant connectivity of mobile devices, and with innovative approaches such as Data-Driven Learning (DDL), Natural Language Processing Technologies (NLPTs) as Open Educational Resources (OERs) could become a powerful tool for language learning as they promote individual and personalized learning. Using a…
Descriptors: Language Teachers, Educational Resources, Telecommunications, Handheld Devices
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Katushemererwe, Fridah; Nerbonne, John – Computer Assisted Language Learning, 2015
This study presents the results from a computer-assisted language learning (CALL) system of Runyakitara (RU_CALL). The major objective was to provide an electronic language learning environment that can enable learners with mother tongue deficiencies to enhance their knowledge of grammar and acquire writing skills in Runyakitara. The system…
Descriptors: Computer Assisted Instruction, Native Language Instruction, Grammar, Language Maintenance
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Chukharev-Hudilainen, Evgeny; Saricaoglu, Aysel – Computer Assisted Language Learning, 2016
Expressing causal relations plays a central role in academic writing. While it is important that writing instructors assess and provide feedback on learners' causal discourse, it could be a very time-consuming task. In this respect, automated writing evaluation (AWE) tools may be helpful. However, to date, there have been no AWE tools capable of…
Descriptors: Discourse Analysis, Feedback (Response), Undergraduate Students, Accuracy
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Karlström, Petter; Lundin, Eva – Computer Assisted Language Learning, 2013
Digital tools are not always used in the manner their designers had in mind. Therefore, it is not enough to assume that learning through CALL tools occurs in intended ways, if at all. We have studied the use of an enhanced word processor for writing essays in Swedish as a second language. The word processor contained natural language processing…
Descriptors: Computer Assisted Instruction, Second Language Instruction, Novelty (Stimulus Dimension), Natural Language Processing
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Amaral, Luiz; Meurers, Detmar; Ziai, Ramon – Computer Assisted Language Learning, 2011
Intelligent language tutoring systems (ILTS) typically analyze learner input to diagnose learner language properties and provide individualized feedback. Despite a long history of ILTS research, such systems are virtually absent from real-life foreign language teaching (FLT). Taking a step toward more closely linking ILTS research to real-life…
Descriptors: Feedback (Response), Second Language Learning, Intelligent Tutoring Systems, Information Management
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Wu, Shaoqun; Franken, Margaret; Witten, Ian H. – Computer Assisted Language Learning, 2010
Extensive knowledge of collocations is a key factor that distinguishes learners from fluent native speakers. Such knowledge is difficult to acquire simply because there is so much of it. This paper describes a system that exploits the facilities offered by digital libraries to provide a rich collocation-learning environment. The design is based on…
Descriptors: Electronic Libraries, Language Processing, Internet, Native Speakers
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Wood, Peter – Computer Assisted Language Learning, 2008
This article discusses the use of the General Architecture for Text Engineering (GATE) as a tool for the development of ICALL and NLP applications. It outlines a paradigm shift in software development, which is mainly influenced by projects such as the Free Software Foundation. It looks at standards that have been proposed to facilitate the…
Descriptors: Computer Software, Natural Language Processing, Technological Advancement, Computer Assisted Instruction
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Harbusch, Karin; Itsova, Gergana; Koch, Ulrich; Kuhner, Christine – Computer Assisted Language Learning, 2008
We built an NLP system implementing a "virtual writing conference" for elementary-school children, with German as the target language. Currently, state-of-the-art computer support for writing tasks is restricted to multiple-choice questions or quizzes because automatic parsing of the often ambiguous and fragmentary texts produced by pupils…
Descriptors: Essays, Tests, Writing Instruction, Natural Language Processing
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Stewart, Iain A. D.; File, Portia – Computer Assisted Language Learning, 2007
Early and intermediate second language (L2) learners often encounter difficulties when engaging in introductory social conversations, typically having had little opportunity to practise such interactions. This article describes a project to design and prototype a computer dialogue system, Let's Chat, which would allow learners to rehearse social…
Descriptors: Second Language Learning, Speech Communication, Linguistic Input, Language Processing
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