Investigation of Multiple Recognitions Used for EFL Writing in Authentic Contexts

Wu Yuin Hwang, Van Giap Nguyen, Chi Chieh Chin, Siska Wati Dewi Purba, George Ghinea

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Recognition technologies had been prevailing and widely used for EFL learning. We investigated the different recognitions used for EFL writing based on image-to-text, translated speech-to-text, and location-to-text recognitions – ITR, TSTR, and LTR. A quasi-experiment was implemented for 12 weeks in a vocational high school with experimental and control groups in two stages. Pre-test, posttests 1 and 2, questionnaires, and interviews were conducted and analyzed. Experimental learners, who wrote writing based on ITR and TSTR, outperformed control learners who wrote that based on TSTR only. Also, the experimental learners, who wore writing based on ITR, TSTR, and LTR, outperformed the control learners who wrote that based on ITR and TSTR. Particularly, LTR was beneficial for identifying controlling ideas and addressing the writing topics. ITR was beneficial for brainstorming and generating more ideas. TSTR was beneficial for yielding and transferring writing contents into words. The multiple recognitions were beneficial for most EFL writers, especially for low-ability language writers. Most writers were interested in describing based on authentic context learning. However, they complained about the low accuracy of LTR and TSTR and the difficulty of ITR texts when writing. Accordingly, the LTR database with various categories of places, the generation of ITR based on the language abilities of learners, and the higher accuracy of TSTR should be strictly considered when applying multiple recognitions for EFL writing.

Original languageEnglish
Title of host publicationInnovative Technologies and Learning - 5th International Conference, ICITL 2022, Proceedings
EditorsYueh-Min Huang, Shu-Chen Cheng, João Barroso, Frode Eika Sandnes
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages11
ISBN (Print)9783031152726
StatePublished - 2022
Event5th International Conference on Innovative Technologies and Learning, ICITL 2022 - Virtual, Online
Duration: 29 Aug 202231 Aug 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13449 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th International Conference on Innovative Technologies and Learning, ICITL 2022
CityVirtual, Online


  • Adaptive technologies
  • EFL writing
  • Multimedia learning
  • Recognition technology


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