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Hellmich, Emily A. – Computer Assisted Language Learning, 2021
Recent calls from applied linguistics and from CALL have emphasized the importance of situating the understanding and use of digital tools for language learning within layered contexts. An important component of these layered contexts is societal discourses of technology, which are multiple and far from neutral. In response to these calls, this…
Descriptors: Computer Assisted Instruction, Second Language Learning, Second Language Instruction, Student Attitudes
Zare, Javad; Karimpour, Sedigheh; Aqajani Delavar, Khadijeh – Computer Assisted Language Learning, 2023
The purpose of the present study was to investigate if following data-driven learning (DDL) to raise the learners' awareness of discourse organizers through concordancing improves their comprehension of English academic lectures. To address this issue, the current study adopted a quasi-experimental (comparison group, pretest-posttest) design. 96…
Descriptors: Classroom Communication, Discourse Analysis, Computational Linguistics, English for Academic Purposes
Linking Adverbials in First-Year Korean University EFL Learners' Writing: A Corpus-Informed Analysis
Ha, Myung-Jeong – Computer Assisted Language Learning, 2016
This study examines the frequency and usage patterns of linking adverbials in Korean students' essay writing in comparison with native English writing. The learner corpus used in the present study is composed of 105 essays that were produced by first-year university students in Korea. The control corpus was taken from the American LOCNESS…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Asians
Chukharev-Hudilainen, Evgeny; Saricaoglu, Aysel – Computer Assisted Language Learning, 2016
Expressing causal relations plays a central role in academic writing. While it is important that writing instructors assess and provide feedback on learners' causal discourse, it could be a very time-consuming task. In this respect, automated writing evaluation (AWE) tools may be helpful. However, to date, there have been no AWE tools capable of…
Descriptors: Discourse Analysis, Feedback (Response), Undergraduate Students, Accuracy
Liaw, Meei-Ling; Bunn-Le Master, Susan – Computer Assisted Language Learning, 2010
This study examines if and how collaboration and intercultural learning took place during telecollaboration by exploring the linguistic features of the discourse used by the participants, as well as the patterns and types of interactions between intercultural interlocutors. EFL students in Taiwan were paired up with pre-service teacher education…
Descriptors: Electronic Learning, Preservice Teacher Education, Linguistics, Online Courses
Kong, Kenneth – Computer Assisted Language Learning, 2009
Self-study is playing an increasingly important role in the learning and instruction of many subjects, including second and foreign languages. With the rapid development of the internet, language websites for self-study are flourishing. While the language of print-based teaching materials has received some attention, the linguistic and…
Descriptors: Textbooks, Structural Analysis (Linguistics), Online Courses, Information Sources

Schwind, Camilla B. – Computer Assisted Language Learning, 1995
Presents a framework for dealing with errors in natural language sentences within the context of automated second-language teaching. Using a feature grammar, it is possible to describe various types of errors in a uniform framework, clearly define an error, and analyze the error source. (24 references) (Author/CK)
Descriptors: Computer Assisted Instruction, Context Effect, Discourse Analysis, Error Analysis (Language)