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Christopher Saarna – International Journal of Technology in Education, 2024
This study seeks to clarify whether teachers are able to distinguish between essays written by English L2 students or generated by ChatGPT. 47 instructors who hold experience teaching English to native speakers of Japanese in universities or other higher education institutions were tested on whether they could identify between human written essays…
Descriptors: Identification, Artificial Intelligence, Computer Software, Grammar
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Chae-Eun Kim – Journal of Pan-Pacific Association of Applied Linguistics, 2022
This study explores how Korean-to-English machine translation (MT) systems (e.g., Google Translator, NAVER Papago) deal with Korean passive structures. Cross-linguistically, Korean and English passives show different ways to construct passive-voice sentences from active structure. English passives including with [to be + past participle] may have…
Descriptors: Korean, English (Second Language), Second Language Learning, Second Language Instruction
UK Department for Education, 2024
This report sets out the findings of the technical development work completed as part of the Use Cases for Generative AI in Education project, commissioned by the Department for Education (DfE) in September 2023. It has been published alongside the User Research Report, which sets out the findings from the ongoing user engagement activity…
Descriptors: Artificial Intelligence, Technology Uses in Education, Computer Software, Computational Linguistics
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Lei, Jiun-Iung – English Language Teaching, 2020
While Automated Writing Evaluation (AWE) can perform an error diagnosis (Chen & Cheng, 2008), previous studies used to exclude it from the process of error analysis. This study aimed to examine the reactions of Grammarly Premium towards a group of night school students' English writings at a Taiwanese technical university. The participants of…
Descriptors: Writing Evaluation, Computer Software, Error Analysis (Language), Second Language Learning
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Phoophuangpairoj, Rong; Pipattarasakul, Piyarat – International Journal of Educational Methodology, 2022
During the pandemic of Coronavirus disease 2019 (COVID-19), English as a foreign language (EFL) students have to study and submit their assignments and quizzes through online systems using electronic files instead of hardcopies. This has created an opportunity for teachers to use computer tools to conduct preliminary assessment of the students'…
Descriptors: Essays, Writing Evaluation, Second Language Learning, Second Language Instruction
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Tsai, Shu-Chiao – Computer Assisted Language Learning, 2022
This study investigates the effectiveness of using Google Translate as a translingual CALL tool in English as a Foreign Language (EFL) writing, keyed to the perceptions of both more highly proficient Chinese English major university students and less-proficient non-English majors. After watching a 5-minute passage from a movie, each cohort of…
Descriptors: Computer Assisted Instruction, Translation, Second Language Learning, Second Language Instruction
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Paul John; Nina Wolf – CALICO Journal, 2020
Our study examines written corrective feedback generated by two online grammar checkers (GCs), Grammarly and Virtual Writing Tutor, and by the grammar checking function of Microsoft Word. We tested the technology on a wide range of grammatical error types from two sources: a set of authentic ESL compositions and a series of simple sentences we…
Descriptors: English (Second Language), Feedback (Response), Automation, Grammar
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Mughaz, Dror; Cohen, Michael; Mejahez, Sagit; Ades, Tal; Bouhnik, Dan – Interdisciplinary Journal of e-Skills and Lifelong Learning, 2020
Aim/Purpose: Using Artificial Intelligence with Deep Learning (DL) techniques, which mimic the action of the brain, to improve a student's grammar learning process. Finding the subject of a sentence using DL, and learning, by way of this computer field, to analyze human learning processes and mistakes. In addition, showing Artificial Intelligence…
Descriptors: Artificial Intelligence, Teaching Methods, Brain Hemisphere Functions, Grammar
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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)
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Tsai, Shu-Chiao – Computer Assisted Language Learning, 2019
This study investigates the impact on extemporaneous English-language first drafts by using Google Translate (GT) in three different tasks assigned to Chinese sophomore, junior, and senior students of English as a Foreign Language (EFL) majoring in English. Students wrote first in Chinese (Step 1), then drafted corresponding texts in English (Step…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Computer Software
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Jishvithaa, Joanna M.; Tabitha, M.; Kalajahi, Seyed Ali Rezvani – Advances in Language and Literary Studies, 2013
This research paper aims to explore the usage of the English Auxiliary "Be" Present Tense Verb, using corpus based method among Malaysian form 4 and form 5 students. This study is conducted by identifying and classifying the types of errors in the Auxiliary "Be" Present Tense verb in students' compositions from the MCSAW corpus…
Descriptors: Morphemes, English (Second Language), Error Patterns, Second Language Learning
Yoon, Su-Youn – ProQuest LLC, 2009
This dissertation provides an automated scoring method of speech fluency for second language learners of English (L2 learners) based that uses speech recognition technology. Non-standard pronunciation, frequent disfluencies, faulty grammar, and inappropriate lexical choices are crucial characteristics of L2 learners' speech. Due to the ease of…
Descriptors: Phonemes, Second Language Learning, Scoring, Correlation
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Hassan, Sharifah Zakiah Wan; Hakim, Simon Faizal; Rahim, Mahdalela; Noyem, John Francis; Ibrahim, Sueb; Ahmad, Johnny; Jusoff, Kamaruzaman – English Language Teaching, 2009
This study explores Universiti Teknologi MARA (UiTM) Sarawak graduating students' oral proficiency, focusing on grammatical accuracy. Oral proficiency in English has always been the benchmark of language proficiency, and in the context of UiTM's language teaching curriculum, efforts to enhance students' oral proficiency are implemented through…
Descriptors: Foreign Countries, English (Second Language), Second Language Instruction, Second Language Learning
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Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis
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Price, Charlotte; Bunt, Andrea; McCalla, Gordon – Computer Assisted Language Learning, 1999
Introduces a computer-assisted language-learning system called L2tutor that is designed to provide an immersion experience to travelers before they leave on a trip to a country where a different language is spoken. The learner takes part in a fully mixed-initiative dialog with the system to gain fluency and hone vocabulary and grammatical skills.…
Descriptors: Computer Assisted Instruction, Computer Software, Dialogs (Language), Error Patterns
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