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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
Hsu, Liwei – Computer Assisted Language Learning, 2023
The Language Massive Open Online Courses (LMOOCs) is a new platform of computer assisted language learning (CALL); since most LMOOCs witness high dropout rates, empirical evidence on English as a Foreign Language (EFL) learners' motivation to accept and utilize LMOOCs for English language learning is warranted. This study recruited 237 Taiwanese…
Descriptors: English (Second Language), Second Language Learning, Self Determination, MOOCs
Kim, Jungtae; Craig, Daniel A. – Computer Assisted Language Learning, 2012
Videoconferencing offers new opportunities for language testers to assess speaking ability in low-stakes diagnostic tests. To be considered a trusted testing tool in language testing, a test should be examined employing appropriate validation processes [Chapelle, C.A., Jamieson, J., & Hegelheimer, V. (2003). "Validation of a web-based ESL…
Descriptors: Speech Communication, Testing, Language Tests, Construct Validity
Chen, I-Jung; Chang, Chi-Cheng – Computer Assisted Language Learning, 2011
This study investigated whether the modality effect of content presentation modes on students' cognitive load and listening comprehension depends on the moderating effect of the learners' language proficiencies in a mobile learning context. One hundred and sixty-two students majoring in English in a technology university used personal digital…
Descriptors: Electronic Learning, Listening Comprehension, Cognitive Processes, Difficulty Level