title: What are they thinking? Automated analysis of student writing about acid-base chemistry in introductory biology.
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.
authors.affiliation: Center for Engineering Education Research, Michigan State University, East Lansing, MI 48824, USA.
authors.forename: Kevin C
authors.initials: KC
authors.lastname: Haudek
authors.forename: Luanna B
authors.initials: LB
authors.lastname: Prevost
authors.forename: Rosa A
authors.initials: RA
authors.lastname: Moscarella
authors.forename: John
authors.initials: J
authors.lastname: Merrill
authors.forename: Mark
authors.initials: M
authors.lastname: Urban-Lurain
mesh_terms: D000293:Adolescent; D000328:Adult; D001331:Automation; D001695:Biology / Q000193:education* / Q000379:methods*; D003479:Curriculum; D004521:Educational Measurement; D005260:Female; D006801:Humans; D008297:Male; D013334:Students; D013850:Thinking; D014495:Universities; D014956:Writing; D055815:Young Adult