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Showing 1 to 15 of 124 results Save | Export
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Zixi Li; Curtis J. Bonk – Computer Assisted Language Learning, 2025
The research study explored online language learners' self-directed language learning (SDLL) experiences, benefits, motivations, and challenges when employing educational tools like Duolingo in an out of classroom context. To gain insights into SDLL, in-depth and semi-structured interviews with 10 Duolingo users were conducted. Study results…
Descriptors: Computer Software, Independent Study, Second Language Learning, Second Language Instruction
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Jodi M. Tommerdahl; Chrystal Sapphire Dragonflame; Amanda A. Olsen – Computer Assisted Language Learning, 2024
A systematic review examining the efficacy of commercially available foreign language-learning apps (FLL) was completed. A database search of ERIC, PsychINFO, and LearnTechLib produced 1,786 journal articles. After applying specific inclusion and exclusion criteria based on Burston's seminal study (2015) requiring a minimum number of 10…
Descriptors: Computer Assisted Instruction, Teaching Methods, Second Language Learning, Second Language Instruction
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Asma Almusharraf; Daniel Bailey – Computer Assisted Language Learning, 2025
Machine translation (MT) practice and activity development in education are possible when students with diverse backgrounds contribute to helping define how MT can best be used for language learning. This study employed a questionnaire based on an adapted version of the technology acceptance model (TAM) to gain perspective on the perceptions,…
Descriptors: Web Sites, Student Attitudes, Language Proficiency, Second Language Learning
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Siowai Lo – Computer Assisted Language Learning, 2025
Neural Machine Translation (NMT) has gained increasing popularity among EFL learners as a CALL tool to improve vocabulary, and many learners have reported its helpfulness for vocabulary learning. However, while there has been some evidence suggesting NMT's facilitative role in improving learners' writing on the lexical level, no study has examined…
Descriptors: Translation, Computational Linguistics, Vocabulary Development, English (Second Language)
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Nouf J. Aljohani – Computer Assisted Language Learning, 2025
This paper proposes an updated framework for the evaluation of the computer-assisted language learning (CALL) framework, further developed from Chapelle (2001) and González-Lloret and Ortega (2014). Based on a review of prominent previous CALL evaluation frameworks, an exploration of relevant literature in formal evaluation and my own first-hand…
Descriptors: Task Analysis, Second Language Learning, Second Language Instruction, Computer Assisted Instruction
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Jamie Costley; Han Zhang; Matthew Courtney; Galina Shulgina; Matthew Baldwin; Mik Fanguy – Computer Assisted Language Learning, 2025
While the use of collaborative peer editing is widespread in some online learning contexts, little is known about how constituent editing behaviours impact student writing quality when using shared online documents as the mediating tool. Therefore, the present study (n = 176) examines the effects of English language learners' peer editing…
Descriptors: Editing, Peer Evaluation, English (Second Language), Second Language Learning
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Peichin Chang; Pin-Ju Chen; Li-Ling Lai – Computer Assisted Language Learning, 2024
Machine Translation (MT) tools have advanced to a level of reliability such that it is now opportune to consider their place in language teaching and learning. Given their potential, the current study sought to engage EFL university sophomores in recursive editing afforded by Google Translate (GT) for one semester, and investigated (1) whether the…
Descriptors: Editing, Computer Software, Artificial Intelligence, Translation
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Keng-Chih Hsu; Neil E. Barrett; Gi-Zen Liu – Computer Assisted Language Learning, 2025
With the rise of augmented reality (AR) and context-aware ubiquitous learning (CAUL), pedagogical designers in computer assisted language learning are increasingly developing authentic English for Specific Purposes (ESP) learning environments. However, there has been little research regarding the development of evidence-based principles for…
Descriptors: Tourism, English for Special Purposes, Second Language Instruction, Second Language Learning
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Xin An; Ching Sing Chai; Yushun Li; Ying Zhou; Bingyu Yang – Computer Assisted Language Learning, 2025
To address the emerging trend of language learning with Artificial Intelligence (AI), this study explored junior and senior high school students' behavioral intentions to use AI in second language (L2) learning, and the roles of related technological, social, and motivational factors. An eight-factor survey was constructed using a 5-point Likert…
Descriptors: Educational Trends, Trend Analysis, Second Language Learning, Second Language Instruction
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Bin Zou; Qinglang Lyu; Yining Han; Zijing Li; Weilei Zhang – Computer Assisted Language Learning, 2025
Adapted from the Technology Acceptance Model (TAM), the Integrated Model of Technology Acceptance (IMTA) has been used to examine the perceptions and acceptance of computer-assisted language learning (CALL), such as online learning, mobile learning, and learning management systems. However, whether IMTA can be applied to empirical research on…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Artificial Intelligence
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Azizullah Mirzaei; Hanieh Shafiee Rad; Ebrahim Rahimi – Computer Assisted Language Learning, 2024
The Attention, Relevance, Confidence, and Satisfaction (ARCS) model provides a basis for integrating motivational dynamics and technological affordances into the design and implementation of instructions to maintain learner motivation and interest. Little attention has been paid to this potential in teaching the complex and often demotivating…
Descriptors: Flipped Classroom, English (Second Language), Second Language Learning, Second Language Instruction
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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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Rustam Shadiev; Jiatian Yu – Computer Assisted Language Learning, 2024
We reviewed articles on computer-assisted language learning, focusing on intercultural education studies published in the last five years. We investigated the following aspects: (1) the theoretical foundation that the studies were based on, e.g., theory, hypothesis, model, or framework, (2) the technologies used by the participants, (3) the…
Descriptors: Multicultural Education, Computer Assisted Instruction, Teaching Methods, Second Language Learning
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Xinghua Wang; Hui Pang; Matthew P. Wallace; Qiyun Wang; Wenli Chen – Computer Assisted Language Learning, 2024
This study investigated the application of an artificial intelligence (AI) coach for second language (L2) learning in a primary school involving 327 participants. In line with Community of Inquiry, learners were expected to perceive social, cognitive, and teaching presences when interacting with the AI coach, which was considered a humanized…
Descriptors: Artificial Intelligence, Second Language Instruction, Second Language Learning, Student Attitudes
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Christiansen, Iben Maj; Els, Rosanne – Computer Assisted Language Learning, 2021
Few people who did not grow up speaking Zulu have learned the language later. There are limited resources for second language Zulu learning, whether textbooks, readers, or computerised resources. We set out to develop software for this purpose, to support learners' independent learning. Drawing on research on language learning, we used a number of…
Descriptors: Computer Assisted Instruction, Teaching Methods, Second Language Learning, Second Language Instruction
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