Automated Scoring of Students' Small-Group Discussions to Assess Reading Ability

Autor/inn/en	Kosh, Audra E.; Greene, Jeffrey A.; Murphy, P. Karen; Burdick, Hal; Firetto, Carla M.; Elmore, Jeff
Titel	Automated Scoring of Students' Small-Group Discussions to Assess Reading Ability
Quelle	In: Educational Measurement: Issues and Practice, 37 (2018) 2, S.20-34 (15 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Kosh, Audra E.) Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0731-1745
DOI	10.1111/emip.12174
Schlagwörter	Computer Assisted Testing; Small Group Instruction; Group Discussion; Student Evaluation; Reading Ability; Elementary School Students; Feasibility Studies; Transcripts (Written Records); Content Analysis; Grade 4; Discourse Analysis; Predictor Variables; Multiple Choice Tests; Reading Comprehension; Reading Tests; Scores; Reading Fluency; Hierarchical Linear Modeling; Statistical Analysis + Suchen Sie Ihr Suchwort? Gruppendiskussion; Schulnote; Studentische Bewertung; Reading competence; Lesekompetenz; Inhaltsanalyse; School year 04; 4. Schuljahr; Schuljahr 04; Diskursanalyse; Prädiktor; Multiple choice examinations; Multiple-choice tests, Multiple-choice examinations; Multiple-Choice-Verfahren; Leseverstehen; Lesetest; Statistische Analyse
Abstract	We explored the feasibility of using automated scoring to assess upper-elementary students' reading ability through analysis of transcripts of students' small-group discussions about texts. Participants included 35 fourth-grade students across two classrooms that engaged in a literacy intervention called Quality Talk. During the course of one school year, data were collected at 10 time points for a total of 327 student-text encounters, with a different text discussed at each time point. To explore the possibility of automated scoring, we considered which quantitative discourse variables (e.g., variables to measure language sophistication and latent semantic analysis variables) were the strongest predictors of scores on a multiple-choice and constructed-response reading comprehension test. Convergent validity evidence was collected by comparing automatically calculated quantitative discourse features to scores on a reading fluency test. After examining a variety of discourse features using multilevel modeling, results showed that measures of word rareness and word diversity were the most promising variables to use in automated scoring of students' discussions. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01