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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
González-Cruz, Belén; Cerezo, Lourdes; Nicolás-Conesa, Florentina – Studies in Second Language Learning and Teaching, 2022
This study compared the effects of computer-mediated (CM) versus pen-and-paper (P&P) writing on written accuracy and feedback processing in tasks written and rewritten collaboratively following a pedagogical treatment in two intact authentic classrooms. The study involved 32 secondary education low-proficiency English-as-a-foreign-language…
Descriptors: Second Language Learning, Second Language Instruction, Accuracy, Feedback (Response)
Woods-Groves, Suzanne; Hughes, Charles A.; Rodgers, Derek B.; Balint-Langel, Kinga; Alqahtani, Saeed S.; Neil, Katelyn M.; Hinzman, Michelle – Education and Training in Autism and Developmental Disabilities, 2020
The authors in this study used a pre-posttest experimental design with random assignment to treatment or control group to assess the use of an electronic editing cognitive strategy. The participants were 16 college students with intellectual and developmental disabilities enrolled in a 2-year postsecondary program at a Midwestern institute of…
Descriptors: Students with Disabilities, Intellectual Disability, Developmental Disabilities, Instructional Effectiveness
Lin, Po-Han; Liu, Tzu-Chien; Paas, Fred – Reading and Writing: An Interdisciplinary Journal, 2017
Computer-based spell checkers help to correct misspells instantly. Almost all the word processing devices are now equipped with a spell-check function that either automatically corrects errors or provides a list of intended words. However, it is not clear on how the reliance on this convenient technological solution affects spelling learning.…
Descriptors: Educational Technology, Technology Uses in Education, Spelling, Word Processing
AbuSeileek, A. F. – Journal of Computer Assisted Learning, 2013
This study investigated the effect of computer-mediated corrective feedback types in an English as a foreign language (EFL) intact class over time. The participants were 64 English majors who were assigned randomly into three treatment conditions that gave and received computer-mediated corrective feedback while writing (track changes, word…
Descriptors: Computer Software, Error Correction, Feedback (Response), English (Second Language)
Leijten, Marielle; Van Waes, Luuk; Ransdell, Sarah – Written Communication, 2010
Error analysis involves detecting, diagnosing, and correcting discrepancies between the text produced so far (TPSF) and the writers mental representation of what the text should be. The use of different writing modes, like keyboard-based word processing and speech recognition, causes different type of errors during text production. While many…
Descriptors: Writing (Composition), Word Processing, Printed Materials, Revision (Written Composition)
Mezei, Peter J.; Heller, Kathryn Wolff – Physical Disabilities: Education and Related Services, 2012
Many students with physical disabilities have difficulty with writing fluency due to motor limitations. One type of assistive technology that has been developed to improve writing speed and accuracy is word prediction software, although there is a paucity of research supporting its use for individuals with physical disabilities. This study used an…
Descriptors: Educational Technology, Assistive Technology, Prediction, Physical Disabilities
Alshaar, Awatif Abu; AbuSeileek, Ali Farhan – JALT CALL Journal, 2013
This study aims to investigate the effect of using concordances and word processors on EFL graduates' performance in academic writing. To achieve this aim, the subjects were asked to use linguistics corpora and word processors to correct their grammatical and spelling mistakes in their written papers in a course that lasted for sixteen weeks. The…
Descriptors: Computational Linguistics, English (Second Language), Second Language Learning, Semi Structured Interviews
Linke, Dirk – Biochemistry and Molecular Biology Education, 2009
In this article, the author talks about the auto correction mode of word processors that leads to a number of problems and describes an example in biochemistry exams that shows how word processors can lead to mistakes in databases and in papers. The author contends that, where this system is applied, spell checking should not be left to a word…
Descriptors: Biochemistry, Word Processing, Spelling, Error Correction
Rimrott, Anne; Heift, Trude – Language Learning & Technology, 2008
This study investigates the performance of a spell checker designed for native writers on misspellings made by second language (L2) learners. It addresses two research questions: 1) What is the correction rate of a generic spell checker for L2 misspellings? 2) What factors influence the correction rate of a generic spell checker for L2…
Descriptors: Word Processing, German, Spelling, Second Language Learning
Potter, Reva; Fuller, Dorothy – English Journal, 2008
Grammar checkers do not claim to teach grammar; they are tools to bring potential problems to the writer's attention. They also offer only formal and Standard English preferences, limiting the freer expression of some literary forms. Without guidance, students may misuse the checker, become frustrated, and feel discouraged. Users must be…
Descriptors: Standard Spoken Usage, Action Research, Grammar, English