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Adnane Ez-zizi; Dagmar Divjak; Petar Milin – Language Learning, 2024
Since its first adoption as a computational model for language learning, evidence has accumulated that Rescorla-Wagner error-correction learning (Rescorla & Wagner, 1972) captures several aspects of language processing. Whereas previous studies have provided general support for the Rescorla-Wagner rule by using it to explain the behavior of…
Descriptors: Error Correction, Second Language Learning, Second Language Instruction, Gender Differences
Waad Alsaweed; Saad Aljebreen – International Journal of Computer-Assisted Language Learning and Teaching, 2024
Artificial intelligence revolution becomes a trend in most aspects of life. ChatGPT, an AI chatbot, has impacted various domains, including education and language learning. Enhancing writing abilities of ESL learners requires frequent writing practice and feedback, which ChatGPT can easily provide. However, ChatGPT's accuracy in identifying and…
Descriptors: Error Correction, Writing Instruction, Grammar, Morphemes
Shabnam Behzad – ProQuest LLC, 2024
Second language learners constitute a significant and expanding portion of the global population and there is a growing demand for tools that facilitate language learning and instruction across various levels and in different countries. The development of large language models (LLMs) has brought about a significant impact on the domains of natural…
Descriptors: Artificial Intelligence, Computer Software, Computational Linguistics, Second Language Learning
Huang, Ping-Yu; Tsao, Nai-Lung – Computer Assisted Language Learning, 2021
In this article, we describe an online English collocation explorer developed to help English L2 learners produce correct and appropriate collocations. Our tool, which is able to visually represent relevant correct/incorrect collocations on a single webpage, was designed based on the notions of collocation clusters and intercollocability proposed…
Descriptors: Second Language Learning, Second Language Instruction, English (Second Language), Error Correction
Mei-Rong Alice Chen – Educational Technology & Society, 2024
The increase in popularity of Generative Artificial Intelligence Chatbots, or GACs, has created a potentially fruitful opportunity to enhance teaching English as a Foreign Language (EFL). This study investigated the possibility of using GACs to give EFL students metalinguistic guidance (MG) in linguistics courses. Language competency gaps, a lack…
Descriptors: Metacognition, Transformative Learning, English (Second Language), Artificial Intelligence
Ranalli, Jim; Yamashita, Taichi – Language Learning & Technology, 2022
To the extent automated written corrective feedback (AWCF) tools such as Grammarly are based on sophisticated error-correction technologies, such as machine-learning techniques, they have the potential to find and correct more common L2 error types than simpler spelling and grammar checkers such as the one included in Microsoft Word (technically…
Descriptors: Error Correction, Feedback (Response), Computer Software, Second Language Learning
Miranty, Delsa; Widiati, Utami – Pegem Journal of Education and Instruction, 2021
Automated Writing Evaluation (AWE) has been considered a potential pedagogical technique that exploits technology to assist the students' writing. However, little attention has been devoted to examining students' perceptions of Grammarly use in higher education context. This paper aims to obtain information regarding the writing process and the…
Descriptors: Foreign Countries, Technology Uses in Education, Writing (Composition), Student Attitudes
Tono, Yukio; Satake, Yoshiho; Miura, Aika – ReCALL, 2014
This study reports on the results of classroom research investigating the effects of corpus use in the process of revising compositions in English as a foreign language. Our primary aim was to investigate the relationship between the information extracted from corpus data and how that information actually helped in revising different types of…
Descriptors: Computational Linguistics, Feedback (Response), Revision (Written Composition), English (Second Language)
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
Harbusch, Karin; Cameran, Christel-Joy; Härtel, Johannes – Research-publishing.net, 2014
We present a new feedback strategy implemented in a natural language generation-based e-learning system for German as a second language (L2). Although the system recognizes a large proportion of the grammar errors in learner-produced written sentences, its automatically generated feedback only addresses errors against rules that are relevant at…
Descriptors: German, Second Language Learning, Second Language Instruction, Feedback (Response)
Amaral, Luiz; Meurers, Detmar; Ziai, Ramon – Computer Assisted Language Learning, 2011
Intelligent language tutoring systems (ILTS) typically analyze learner input to diagnose learner language properties and provide individualized feedback. Despite a long history of ILTS research, such systems are virtually absent from real-life foreign language teaching (FLT). Taking a step toward more closely linking ILTS research to real-life…
Descriptors: Feedback (Response), Second Language Learning, Intelligent Tutoring Systems, Information Management
Amaral, Luiz A.; Meurers, W. Detmar – CALICO Journal, 2009
Error diagnosis in ICALL typically analyzes learner input in an attempt to abstract and identify indicators of the learner's (mis)conceptions of linguistic properties. For written input, this process usually starts with the identification of tokens that will serve as the atomic building blocks of the analysis. In this paper, we discuss the…
Descriptors: Grammar, Computer Assisted Instruction, Identification, Error Analysis (Language)
Nagata, Noriko – CALICO Journal, 2009
This paper presents a new version of Robo-Sensei's NLP (Natural Language Processing) system which updates the version currently available as the software package "ROBO-SENSEI: Personal Japanese Tutor" (Nagata, 2004). Robo-Sensei's NLP system includes a lexicon, a morphological generator, a word segmentor, a morphological parser, a syntactic…
Descriptors: Textbooks, Computer Assisted Instruction, Computer Software, Natural Language Processing
Granger, Sylviane; Kraif, Olivier; Ponton, Claude; Antoniadis, Georges; Zampa, Virginie – ReCALL, 2007
Learner corpora, electronic collections of spoken or written data from foreign language learners, offer unparalleled access to many hitherto uncovered aspects of learner language, particularly in their error-tagged format. This article aims to demonstrate the role that the learner corpus can play in CALL, particularly when used in conjunction with…
Descriptors: Metalinguistics, Natural Language Processing, English (Second Language), Second Language Learning
Chang, Yu-Chia; Chang, Jason S.; Chen, Hao-Jan; Liou, Hsien-Chin – Computer Assisted Language Learning, 2008
Previous work in the literature reveals that EFL learners were deficient in collocations that are a hallmark of near native fluency in learner's writing. Among different types of collocations, the verb-noun (V-N) one was found to be particularly difficult to master, and learners' first language was also found to heavily influence their collocation…
Descriptors: Sentence Structure, Verbs, Nouns, Foreign Countries
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