What Are They Thinking? Automated Analysis of Student Writing about Acid-Base Chemistry in Introductory Biology

Autor/inn/en	Haudek, Kevin C.; Prevost, Luanna B.; Moscarella, Rosa A.; Merrill, John; Urban-Lurain, Mark
Titel	What Are They Thinking? Automated Analysis of Student Writing about Acid-Base Chemistry in Introductory Biology
Quelle	In: CBE - Life Sciences Education, 11 (2012) 3, S.283-293 (11 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1931-7913
DOI	10.1187/cbe.11-08-0084
Schlagwörter	Chemistry; Biology; Introductory Courses; Science Instruction; College Science; Undergraduate Students; Computer Uses in Education; Scoring; Content Area Writing; Automation; Computer Software; Accuracy; Classification; Statistical Analysis; Discriminant Analysis; Interviews; Interrater Reliability + Suchen Sie Ihr Suchwort? Chemie; Biologie; Einführungskurs; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Computernutzung; Bewertung; Schriftliche Übung; Classification system; Klassifikation; Klassifikationssystem; Statistische Analyse; Diskriminanzanalyse; Interviewing; Interviewtechnik; Interrater-Reliabilität
Abstract	Students' writing can provide better insight into their thinking than can multiple-choice questions. However, resource constraints often prevent faculty from using writing assessments in large undergraduate science courses. We investigated the use of computer software to analyze student writing and to uncover student ideas about chemistry in an introductory biology course. Students were asked to predict acid-base behavior of biological functional groups and to explain their answers. Student explanations were rated by two independent raters. Responses were also analyzed using SPSS Text Analysis for Surveys and a custom library of science-related terms and lexical categories relevant to the assessment item. These analyses revealed conceptual connections made by students, student difficulties explaining these topics, and the heterogeneity of student ideas. We validated the lexical analysis by correlating student interviews with the lexical analysis. We used discriminant analysis to create classification functions that identified seven key lexical categories that predict expert scoring (interrater reliability with experts = 0.899). This study suggests that computerized lexical analysis may be useful for automatically categorizing large numbers of student open-ended responses. Lexical analysis provides instructors unique insights into student thinking and a whole-class perspective that are difficult to obtain from multiple-choice questions or reading individual responses. (Contains 6 tables and 2 figures.) (As Provided).
Anmerkungen	American Society for Cell Biology. 8120 Woodmont Avenue Suite 750, Bethesda, MD 20814-2762. Tel: 301-347-9300; Fax: 301-347-9310; e-mail: ascbinfo@ascb.org; Website: http://www.ascb.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10