The Impact of Automated Feedback on L2 Learners' Written Causal Explanations

Autor/in	Saricaoglu, Aysel
Titel	The Impact of Automated Feedback on L2 Learners' Written Causal Explanations
Quelle	In: ReCALL, 31 (2019) 2, S.189-203 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Saricaoglu, Aysel)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0958-3440
Schlagwörter	Second Language Learning; Second Language Instruction; Feedback (Response); Teaching Methods; Pretests Posttests; Formative Evaluation; Attribution Theory; Writing Improvement; English (Second Language); Essays; Revision (Written Composition); Writing Processes; Outcomes of Education; Video Technology; Writing Instruction; Computer Assisted Instruction + Suchen Sie Ihr Suchwort? Zweitsprachenerwerb; Fremdsprachenunterricht; Teaching method; Lehrmethode; Unterrichtsmethode; English as second language; English; Second Language; Englisch als Zweitsprache; Essay; Aufsatzunterricht; Korrektur; Lernleistung; Schulerfolg; Schreibunterricht; Computer based training; Computerunterstützter Unterricht
Abstract	Even though current technologies allow for automated feedback, evaluating content and generating discourse-specific feedback is still a challenge for automated systems, which explains the gap in research investigating the effect of such feedback. This study explores the impact of automated formative feedback on the improvement of English as a second language (ESL) learners' written causal explanations within two cause-and-effect essays and across pre- and post-tests. Pre- and post-test drafts, feedback reports for first and revised drafts from the automated writing evaluation system, and screen-capturing videos collected from 31 students enrolled in two sections of an advanced-low-level academic writing class were analyzed through descriptive statistics and the Wilcoxon signed-rank test. Findings revealed statistically significant changes in learners' causal explanations within one cause-and-effect essay while no significant improvement was observed across pre- and post-tests. The findings of this study offer not only insights into how to further improve automated discourse-specific feedback but also pedagogical implications for better learning outcomes. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01