Automated Essay Feedback Generation and Its Impact on Revision

Autor/inn/en	Liu, Ming; Li, Yi; Xu, Weiwei; Liu, Li
Titel	Automated Essay Feedback Generation and Its Impact on Revision
Quelle	In: IEEE Transactions on Learning Technologies, 10 (2017) 4, S.502-513 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Liu, Ming)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1939-1382
DOI	10.1109/TLT.2016.2612659
Schlagwörter	Foreign Countries; College Students; Second Language Learning; English (Second Language); Majors (Students); Writing Evaluation; Essays; Feedback (Response); Computer Mediated Communication; Computer Assisted Testing; Scoring; Grammar; Spelling; Sentences; Text Structure; Rhetoric; Connected Discourse; Classification; Error Correction; Integrated Learning Systems; Feasibility Studies; Writing Instruction; Correlation; Interrater Reliability; Automation; Writing Improvement; Student Attitudes; Self Evaluation (Individuals); China + Suchen Sie Ihr Suchwort? Ausland; Collegestudent; Zweitsprachenerwerb; English as second language; English; Second Language; Englisch als Zweitsprache; Essay; Aufsatzunterricht; Computerkonferenz; Bewertung; Grammatik; Schreibweise; Sentence analysis; Satzanalyse; Textstruktur; Rhetorik; Classification system; Klassifikation; Klassifikationssystem; Korrektur; Schreibunterricht; Korrelation; Interrater-Reliabilität; Schülerverhalten
Abstract	Writing an essay is a very important skill for students to master, but a difficult task for them to overcome. It is particularly true for English as Second Language (ESL) students in China. It would be very useful if students could receive timely and effective feedback about their writing. Automatic essay feedback generation is a challenging task, which requires understanding the relationship between the text features of the essay and feedback. In this study, we first analyzed 1,290 teacher comments on their 327 English-major students and annotated the feedback on seven aspects of writing, including the "grammar, spelling, sentence diversity, structure, organization, supporting ideas, coherence, and conclusion", for each paper. Then, an automatic feedback classification experiment was conducted with the machine learning approach. Finally, we investigated the impact of the system generated-indirect corrective feedback (ICF) and human teachers' direct corrective feedback (DCF) in two English writing classes (in ICF class; in DCF class) at a key Chinese university through a web-based assignment management system. The study results indicated the feasibility of this approach that system generated ICF can be as useful as direct comments made by the teachers in terms of improving the quality of the content regarding to the "structure, organization, supporting ideas, coherence, and conclusion", and encouraging students to spend more time on self-correction. (As Provided).
Anmerkungen	Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4620076
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01