Show & Tell Presentation Trialing of ICT-mediated feedback types in an EFL process writing class: Students’ perspective more

Sun, Jun 6, 10:00-11:15 Asia/Tokyo

The present study features a trialing of three feedback types in the essay composition component of a weekly EFL writing course in a private high school in western Tokyo, in the fall and winter of SY 2020-2021. The feedback types were administered on three ICT-mediated applications using a WiFi-enabled Chromebook for cycle 1; and the participants' computing device of choice (Bring Your Own Device) for cycle 2. The feedback types were sequenced as follows: (1) automated feedback through English Listening and Speaking Testing (ELST); (2) teacher feedback through Classi LMS's Questionnaire application; and (3) peer feedback through Google Docs and Google Sheets applications. A process approach to essay composition was implemented. The Action Research (AR) design was adapted in the study. Answers to four research questions were sought: (1) How effective is AI-generated feedback provided on the AWE application of ELST in revising students’ rough drafts?; (2) How effective is teacher feedback provided on Classi LMS's Questionnaire application in improving students’ revised drafts?; (3) How effective is peer feedback provided on Google Docs and Google Sheets in editing students’ final drafts?; and (4) How much did students’ perspective on automated, teacher, and peer feedback change after undergoing the study? Utilizing a qualitative research format, data was gathered using: (a) pre- and post-study student surveys; (b) teacher field observation notes; (c) student learning reflection logs; and (d) essay drafts overall mean scores of respondents. Results found that automated, teacher, and peer feedback types were all moderately effective in improving student-writers’ drafts.