NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 4 results Save | Export
Peer reviewed Peer reviewed
Direct linkDirect link
Ghulam Abbas; Naureen Nazar; Zhirun Huang; Zhanhao Jiang – Asia-Pacific Education Researcher, 2025
This study compares the grammatical errors (GEs) made by non-English major undergraduate students from China and Pakistan, aiming to identify common and distinct types of error and to explore potential reasons behind them. By focusing on these two linguistically and culturally distinct groups, this research seeks to enhance understanding of…
Descriptors: Foreign Countries, Computer Assisted Instruction, Error Patterns, Error Analysis (Language)
Peer reviewed Peer reviewed
Direct linkDirect link
Lu, Jijian; Tao, Yan; Xu, Jinghao; Stephens, Max – Interactive Learning Environments, 2023
This study is the extension of our previous visualizing study on the commognition processes in computer-supported one-to-one tutoring. With the help of the scale of commognitive responsibility score, we found that the main triggers of the commognition process shift are the positive transfer of knowledge and cognitive conflict. On the basis of…
Descriptors: Cognitive Processes, Computer Assisted Instruction, Tutoring, Artificial Intelligence
Peer reviewed Peer reviewed
Direct linkDirect link
Xiao, Wenqi; Park, Moonyoung – International Journal of Computer-Assisted Language Learning and Teaching, 2021
With the advancement of automatic speech recognition (ASR) technology, ASR-based pronunciation assessment can diagnose learners' pronunciation problems. Meanwhile, ASR-based pronunciation training allows more opportunities for pronunciation practice. This study aims to investigate the effectiveness of ASR technology in diagnosing English…
Descriptors: Automation, Computer Software, Handheld Devices, Diagnostic Tests
Peer reviewed Peer reviewed
Direct linkDirect link
Qin, Ying – International Journal of Computer-Assisted Language Learning and Teaching, 2019
This study extracts the comments from a large scale of Chinese EFL learners' translation corpus to study the taxonomy of translation errors. Two unsupervised machine learning approaches are used to obtain the computational evidences of translation error taxonomy. After manually revision, ten types of English to Chinese (E2C) and eight types…
Descriptors: Taxonomy, Translation, Computer Assisted Instruction, Second Language Learning