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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
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)
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
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
Figueredo, Lauren; Varnhagen, Connie K. – Reading Psychology, 2005
We investigated expectations regarding a writer's responsibility to proofread text for spelling errors when using a word processor. Undergraduate students read an essay and completed a questionnaire regarding their perceptions of the author and the quality of the essay. They then manipulated type of spelling error (no error, homophone error,…
Descriptors: Undergraduate Students, Spelling, Word Processing, Error Patterns

Dalton, Bridget; And Others – Journal of Special Education Technology, 1990
Two fourth grade boys with learning disabilities learned to use the Bank Street Writer III word processing and spelling checker program to edit spelling errors. Both boys learned to manage the spell-checking process and increased the spelling accuracy of their edited texts. Differences in their learning processes and the tool's limitations are…
Descriptors: Editing, Error Analysis (Language), Error Correction, Intermediate Grades

McNaughton, David; And Others – Learning Disabilities Research and Practice, 1997
Investigated the impact of integrated proofreading strategy training, which combined a computer-based spelling checker and student strategies, on the proofreading of three secondary students with learning disabilities. Students showed an increase in strategy use and percentage of spelling errors corrected on controlled proofreading materials and…
Descriptors: Computer Uses in Education, Editing, Error Correction, Learning Disabilities
Benjamin, Amy – Eye on Education, 2005
Do you spend entirely too much time correcting your students' papers? Do your students' essays and term papers take side trips to nowhere? Is their writing riddled with mechanical errors? Do their lab reports and essays lack specificity and clarity? Writing in the Content Areas, Second Edition is for middle and high school content area teachers…
Descriptors: Writing Improvement, Essays, Teachers, Word Processing