NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 4 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Jingjing Zhu; Xi Zhang; Jian Li – Computer Assisted Language Learning, 2024
Traditional L2 pronunciation teaching puts too much emphasis on explicit phonological knowledge ('knowing that') rather than on procedural knowledge ('knowing how'). The advancement of mobile-assisted language learning (MALL) offers new opportunities for L2 learners to proceduralize their declarative articulatory knowledge into production skills…
Descriptors: Artificial Intelligence, Technology Uses in Education, Pronunciation Instruction, Second Language Instruction
Peer reviewed Peer reviewed
Direct linkDirect link
Zheng, Chunping; Wang, Lili; Chai, Ching Sing – Computer Assisted Language Learning, 2023
Although formative assessment has been recognized as an effective way for improving learning, scant attention has been paid to the specific design on the sequence of applying formative assessment practice in computer-assisted language learning (CALL). Even less emphasis has been devoted to the cognitive and affective outcomes of different orders…
Descriptors: Self Evaluation (Individuals), Peer Evaluation, Video Technology, Formative Evaluation
Peer reviewed Peer reviewed
Direct linkDirect link
Zhengdong Gan; Christopher Fulton; Siying Li – Computer Assisted Language Learning, 2024
This article presents the development and validation of the Pre-Service EFL Teachers' Motivational Beliefs about Instructional Use of Technology scale (PTMB-EFL) using a randomly split sample. Confirmatory factor analysis supported a 23-item six-factor structure of the PTMB-EFL generated from exploratory factor analysis. Significant correlations…
Descriptors: Preservice Teachers, English (Second Language), Language Teachers, Teacher Motivation
Peer reviewed Peer reviewed
Direct linkDirect link
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