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Direct linkAsma 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
Zhai, Na; Ma, Xiaomei – Computer Assisted Language Learning, 2022
Automated writing evaluation (AWE) has been used increasingly to provide feedback on student writing. Previous research typically focused on its inter-rater reliability with human graders and validation frameworks. The limited body of research has only discussed students' attitudes or perceptions in general. A systematic investigation of the…
Descriptors: Automation, Writing Evaluation, Feedback (Response), College Students
Li, Rui; Meng, Zhaokun; Tian, Mi; Zhang, Zhiyi; Ni, Chuanbin; Xiao, Wei – Computer Assisted Language Learning, 2019
Automated Writing Evaluation (AWE) has been widely applied in computer-assisted language learning (CALL) in China. However, little is known about factors that influence learners' intention to use AWE. To this end, by adding two external factors (i.e. computer self-efficacy and computer anxiety) to the technology acceptance model (TAM), we surveyed…
Descriptors: Foreign Countries, English (Second Language), Second Language Learning, Automation
García Botero, Gustavo; Questier, Frederik; Zhu, Chang – Computer Assisted Language Learning, 2019
Can mobile-assisted language learning (MALL) foster self-directed learning outside the classroom? This article examines informal, out-of-class engagement with a MALL tool: Duolingo. After being invited to use Duolingo, 118 higher education language students agreed to have their activity in the application tracked. In addition to the data collected…
Descriptors: Telecommunications, Handheld Devices, College Students, Educational Technology
Yu, Ping; Pan, Yingxin; Li, Chen; Zhang, Zengxiu; Shi, Qin; Chu, Wenpei; Liu, Mingzhuo; Zhu, Zhiting – Computer Assisted Language Learning, 2016
Oral production is an important part in English learning. Lack of a language environment with efficient instruction and feedback is a big issue for non-native speakers' English spoken skill improvement. A computer-assisted language learning system can provide many potential benefits to language learners. It allows adequate instructions and instant…
Descriptors: English (Second Language), Foreign Countries, Second Language Instruction, Computer Assisted Instruction
Strobl, Carola; Jacobs, Geert – Computer Assisted Language Learning, 2011
In this article, we set out to assess QuADEM (Quality Assessment of Digital Educational Material), one of the latest methods for evaluating online language learning courseware. What is special about QuADEM is that the evaluation is based on observing the actual usage of the online courseware and that, from a checklist of 12 different components,…
Descriptors: Foreign Countries, Electronic Learning, Video Technology, Feedback (Response)
