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Showing 1 to 15 of 23 results Save | Export
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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
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Huiyu Zhang; Linda Fang – Educational Media International, 2023
In Temasek Polytechnic, Singapore, several AI chatbots acting as digital teaching assistants were trialed between July 2021 and August 2022. As these were developed for different purposes, it is important to learn if these AI chatbots help achieve the desired learning outcomes. This paper focuses on lessons learnt from three chatbots designed for…
Descriptors: Foreign Countries, Artificial Intelligence, Asynchronous Communication, Teaching Assistants
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Koji Osawa – RELC Journal: A Journal of Language Teaching and Research, 2024
With the recent rapid technological advance, second language (L2) educators have increasingly incorporated technologies into writing pedagogy. Two of the major technologies to promote L2 writing are e-portfolios and automated written corrective feedback (AWCF). Notably, feedback-rich portfolios facilitate L2 learners' self-regulation and writing…
Descriptors: Artificial Intelligence, Computer Software, Writing Instruction, Writing Evaluation
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Bolgün, M. Ali; McCaw, Tatiana – Computer Assisted Language Learning, 2019
With the ever-increasing number of available language technology products, there is also a need to evaluate them objectively. Unsubstantiated beliefs about what language technology can and cannot do inside or outside the language classroom often influence decisions about the choice of language technology to be used. The declarative/procedural…
Descriptors: Neurosciences, Second Language Learning, Second Language Instruction, Metalinguistics
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Mize, Minnie; Park, Yujeong; Schramm-Possinger, Megan; Coleman, Mari Beth – Intervention in School and Clinic, 2020
Tablet devices, as assistive and instructional technologies, can be highly effectual pedagogical tools with multifaceted benefits, such as the ability to integrate multimedia and the ability to track student progress over time. The unique value of particular assistive and instructional technologies explains, in part, why they are more widely used…
Descriptors: Reading Difficulties, Scoring Rubrics, Multimedia Instruction, Literacy
Ferlazzo, Larry; Sypnieski, Katie Hull – American Educator, 2018
With more than 35 years of combined experience teaching English language learners (ELLs), Larry Ferlazzo and Katie Hull Sypnieski understand how students learn. Together, they have written a series of books on the topic, and in this article they share their insights. Chief among them is that ELLs require particular instructional strategies to help…
Descriptors: English Language Learners, Teaching Methods, Individualized Instruction, Student Motivation
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Lawley, Jim – Language Learning & Technology, 2015
This paper describes the development of web-based software at a university in Spain to help students of EFL self-correct their free-form writing. The software makes use of an eighty-million-word corpus of English known to be correct as a normative corpus for error correction purposes. It was discovered that bigrams (two-word combinations of words)…
Descriptors: Computer Software, Second Language Learning, English (Second Language), Error Correction
Garnier, Marie – European Association for Computer-Assisted Language Learning (EUROCALL), 2012
This article presents the preliminary steps to the implementation of detection and correction strategies for the erroneous use of N+N structures in the written productions of French-speaking advanced users of English. This research is carried out as part of the grammar checking project "CorrecTools", in which errors are detected and corrected…
Descriptors: Error Correction, Language Research, English (Second Language), French
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O'Brien, Myles – The EUROCALL Review, 2012
The Mango Suite is a set of three freely downloadable cross-platform authoring programs for flexible network-based CALL exercises. They are Adobe Air applications, so they can be used on Windows, Macintosh, or Linux computers, provided the freely-available Adobe Air has been installed on the computer. The exercises which the programs generate are…
Descriptors: Web Based Instruction, Computer Software, Error Correction, Feedback (Response)
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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
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Gamon, Michael; Leacock, Claudia; Brockett, Chris; Dolan, William B.; Gao, Jianfeng; Belenko, Dmitriy; Klementiev, Alexandre – CALICO Journal, 2009
In this paper we present a system for automatic correction of errors made by learners of English. The system has two novel aspects. First, machine-learned classifiers trained on large amounts of native data and a very large language model are combined to optimize the precision of suggested corrections. Second, the user can access real-life web…
Descriptors: English (Second Language), Error Correction, Second Language Learning, Computer Assisted Instruction
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Cai, Li; Hayes, Andrew F. – Journal of Educational and Behavioral Statistics, 2008
When the errors in an ordinary least squares (OLS) regression model are heteroscedastic, hypothesis tests involving the regression coefficients can have Type I error rates that are far from the nominal significance level. Asymptotically, this problem can be rectified with the use of a heteroscedasticity-consistent covariance matrix (HCCM)…
Descriptors: Least Squares Statistics, Error Patterns, Error Correction, Computation
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De Felice, Rachele; Pulman, Stephen – CALICO Journal, 2009
In this article, we present an approach to the automatic correction of preposition errors in L2 English. Our system, based on a maximum entropy classifier, achieves average precision of 42% and recall of 35% on this task. The discussion of results obtained on correct and incorrect data aims to establish what characteristics of L2 writing prove…
Descriptors: Language Patterns, Form Classes (Languages), Error Correction, Second Language Learning
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Burston, Jack – CALICO Journal, 1996
Four grammar checkers, all of French Canadian origin, were evaluated: "Le Correcteur 101,""GramR,""Hugo Plus," and "French Proofing Tools." Results indicate that "Le Correcteur 101" is the best French grammar checker on the market and worth its premium cost. (two references) (CK)
Descriptors: Computer Assisted Instruction, Computer Software, Error Analysis (Language), Error Correction
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Ryan-Thompson, Lin A. – Teaching English in the Two-Year College, 2005
Grading essays and research papers can be the most trying part of any writing instructor's job. Fitting corrections, suggestions, and notes between double-spaced lines of text and into margins often creates a legibility problem, which only worsens when grading stacks of papers to meet deadlines. This essay describes electronic grading, a method…
Descriptors: Writing Teachers, Grading, Writing Instruction, Computer Assisted Instruction
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