Results Reliability Consistency and stability of cluster solution across different samples In both years, three distinct cluster groups identified thus reliability of cluster solution was established Predictive Validity 1.How well do the identified cluster groups predict performance on exams? 2.Is there a difference in exam scores among the cluster groups? 3.What about in terms of other external variables? (i.e. value beliefs, learning beliefs, goal orientations, metacognitive self-regulation) If cluster groups differ based on these external variables, then cluster solution shows predictive validity Conclusion Three distinct affective groups identified: low(at-risk), medium, & high Low group reported lower levels of attitude, self-concept, self- efficacy, and high levels of anxiety Among cluster groups, differences found on: –Exam performance –Motivation, metacognitive self-regulation, and learning strategies females: better organizers; males: better critical thinkers –Females rely more on rehearsal strategies, time manage better, and are more committed and determined on chemistry tasks than males –Females have lower self-efficacy than males goal orientation, task value, learning beliefs, and effort regulation decreased over the semester; self-efficacy decreased and increased again by end of semester; References Berg, C.A.R. Chem. Educ. Res. Pract. 2005, 6 (1), Bloom, B.S. Human Characteristics and School Learning; McGraw-Hill: New York, Gungor, A., Eryılmaz, A., Fakıoglu, T. J Res Sci Teach. 2007, 44(8), Haertel, G.D., Walberg, H.J., & Weinstein, T. Rev Educ Res. 1983, 53(1), Romesburg, H.C. Cluster Analysis for Researchers; Lulu Press: North Carolina, Xu, X., Villafane, S.M., & Lewis, J.E. Chem Educ Res Pract. 2013, 14, Ward, J.H. Journal of American Statistical Association. 1963, 58(301), Zusho, A.; Pintrich, P.R.; Coppola, B. Int. J. Sci. Educ. 2003, 25, Identifying At-Risk Students Using Affective Characteristics in General Chemistry Julia Y.K. Chan and Christopher F. Bauer Introduction Comprehensive models of student learning have been proposed by educational theorists Bloom’s (1976) model illustrate students enter each new learning task with a set of cognitive characteristics and preconceived affective characteristics based on prior experiences students’ achievement motivation, cognitive and motivational processes have been included in later models (Zusho, Pintrich, & Coppola, 2003; Berg, 2005) Structural equation modeling (SEM) procedures have been used to test theoretical models In chemistry, Xu et al (2013) predicted 69% of variance in students’ chem achievement was explained by math ability, prior chem conceptual knowledge, and attitude toward chemistry In physics, Gungor et al. (2007) found the following relationships of physics achievement and various affective characteristics: Attitude (interest, task value) -> motivation (test anxiety, self-efficacy, self-concept, student motivation) -> achievement Research Questions 1) What does the affective profile of students in first semester general chemistry look like? 2) To what extent is there a distinctive relationship between students’ affective profile and exam performance? 3) How do students with different sets of affective characteristics differ in terms of metacognition, motivation, and learning strategies? 4) How do students affective measures change over the course of the semester? Instruments Chemistry Self-Concept Inventory (CSCI) 40-item Likert-style assessment 5 variables: math, chemistry, academic, academic enjoyment, creativity self-concept Attitude to Subject of Chemistry Inventory abridged (ASCIv2) 8-item semantic differential assessment 2 variables: intellectual accessibility, emotional satisfaction Motivated Strategies for Learning Questionnaire (MSLQ) 81-item Likert-style assessment Two parts: motivation, learning strategies 15 variables: learning beliefs, self-efficacy, test anxiety, task value, extrinsic goal orientation, intrinsic goal orientation, self-regulation, organization, elaboration, critical thinking, rehearsal, time & study environment, peer learning, help seeking, effort Design Fall 2008, 2013 (General Chemistry I) In 2008 (N=297); in 2013 (N=306) students participated in study About 2/3 first-year students Majors: bioscience, health sciences, chemistry, liberal arts Surveys and exams were taken throughout semester: Methodology Cluster Analysis Ward’s method Most common and conservative clustering approach Uses Analysis of variance approach to determine distance between cluster groups Uses an agglomerative hierarchical clustering technique Selection of Clustering Variables Chose variables most strongly correlated and/or strong predictors of chem achievement (r = to 0.504, p<0.001; medium to large effect) Two variables selected from each construct: Attitude (emotional satisfaction, intellectual accessibility) Self-concept (chemistry self-concept, math self-concept) Motivation (self-efficacy, test-anxiety) Results Thus, predictive validity of the cluster solution was supported. Proposed model of student learning and achievement outcomes.