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Download full textBonner, Euan; Lege, Ryan; Frazier, Erin – Teaching English with Technology, 2023
Large Language Models (LLMs) are a powerful type of Artificial Intelligence (AI) that simulates how humans organize language and are able to interpret, predict, and generate text. This allows for contextual understanding of natural human language which enables the LLM to understand conversational human input and respond in a natural manner. Recent…
Descriptors: Teaching Methods, Artificial Intelligence, Second Language Learning, Second Language Instruction
Direct linkGoh, Tiong-Thye; Sun, Hui; Yang, Bing – Computer Assisted Language Learning, 2020
This study investigates the extent to which microfeatures -- such as basic text features, readability, cohesion, and lexical diversity based on specific word lists -- affect Chinese EFL writing quality. Data analysis was conducted using natural language processing, correlation analysis and stepwise multiple regression analysis on a corpus of 268…
Descriptors: Essays, Writing Tests, English (Second Language), Second Language Learning
Bailey, Daniel; Lee, Andrea Rakushin – TESOL International Journal, 2020
Different genres of writing entail various levels of syntactic and lexical complexity, and how this complexity influences the results of Automatic Writing Evaluation (AWE) programs like Grammarly in second language (L2) writing is unknown. This study explored the use of Grammarly in the L2 writing context by comparing error frequency, error types…
Descriptors: Grammar, Computer Assisted Instruction, Error Correction, Feedback (Response)
Celik, Serkan; Elkatmis, Metin – Educational Sciences: Theory and Practice, 2013
One of the critical contributions of the emerging technologies in computer sciences is the capability of corpus compilation and processing. Corpus resources and approaches are regarded as a potentially valuable areas both in developing instructional methods and designing pedagogical materials. This study aimed to explore the effect of exposing…
Descriptors: Computational Linguistics, Computer Assisted Instruction, Semi Structured Interviews, Foreign Countries
The Ghost in the Machine: Generating Error Messages in Computer Assisted Language Learning Programs.
Peer reviewedAllen, John Robin – CALICO Journal, 1996
Discusses how computer-assisted language learning programs can generate error messages to help students in different ways. The article points out that an easier solution is to program a computer to recognize several different kinds of generic errors not related to any particular question but applicable to many situations, in order to generate…
Descriptors: College Students, Computer Assisted Instruction, Error Analysis (Language), Error Correction
Naeem, Marwa Ahmed Refat – Online Submission, 2007
The current study aims at investigating the effect of a suggested CALL program on developing EFL learners' mechanics of writing in English. An unbiased simple random sample of eighty fourth-year students (2006/2007) of the English Department at the Faculty of Education in Kafr El-Sheikh has been chosen to carry out the experiment. Forty students…
Descriptors: Experimental Groups, Control Groups, Punctuation, Computer Assisted Instruction
