Writing Quality, Knowledge, and Comprehension Correlates of Human and Automated Essay Scoring [Konferenzbericht] Paper presented at the International Florida Artificial Intelligence Research Society Conference (27th, 2014).

Autor/inn/en	Roscoe, Rod D.; Crossley, Scott A.; Snow, Erica L.; Varner, Laura K.; McNamara, Danielle S.
Titel	Writing Quality, Knowledge, and Comprehension Correlates of Human and Automated Essay Scoring [Konferenzbericht] Paper presented at the International Florida Artificial Intelligence Research Society Conference (27th, 2014).
Quelle	(2014), (6 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Correlation; Essays; Scoring; Writing Evaluation; Evaluators; Construct Validity; Intelligent Tutoring Systems; Writing Skills; Reading Comprehension; Vocabulary Skills; High School Students; Scoring Rubrics; Undergraduate Students; Reading Tests; Knowledge Level; Computer Software; Gates MacGinitie Reading Tests + Suchen Sie Ihr Suchwort? Korrelation; Essay; Aufsatzunterricht; Bewertung; Intelligentes Tutorsystem; Writing skill; Schreibfertigkeit; Leseverstehen; Aktiver Wortschatz; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin; Scoring formulas; Auswertungsbogen; Lesetest; Wissensbasis
Abstract	Automated essay scoring tools are often criticized on the basis of construct validity. Specifically, it has been argued that computational scoring algorithms may be unaligned to higher-level indicators of quality writing, such as writers' demonstrated knowledge and understanding of the essay topics. In this paper, we consider how and whether the scoring algorithms within an intelligent writing tutor correlate with measures of writing proficiency and students' general knowledge, reading comprehension, and vocabulary skill. Results indicate that the computational algorithms, although less attuned to knowledge and comprehension factors than human raters, were marginally related to such variables. Implications for improving automated scoring and intelligent tutoring of writing are briefly discussed. [This paper was published in: "Proceedings of the Twenty-Seventh International Florida Artificial Intelligence Research Society Conference" (p.393-398). Association for the Advancement of Artificial Intelligence, 2014.] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01