Exploring Factors that Influence Computer Science Introductory Course Students to Persist in the Major Lecia J. Barker School of Information University of Texas Austin, Texas 78712 USA +1 512 471 3821 lecia@ischool.utexas.edu Charlie McDowell Computer Science Department University of California Santa Cruz, CA 95064 USA +1 831 459 4772 charlie@soe.ucsc.edu Kimberly Kalahar National Center for Women & IT University of Colorado Boulder, CO 80309 USA +1 303 735 6004 kalahark@colorado.edu ABSTRACT This paper describes an exploratory study to identify which environmental and student factors best predict intention to persist in the computer science major. The findings can be used to make decisions about initiatives for increasing retention. Eight indices of student characteristics and perceptions were developed using the research-based Student Experience of the Major Survey: student-student interaction; student-faculty interaction; collaborative learning opportunities; pace/workload/prior experience with programming; teaching assistants; classroom climate/pedagogy; meaningful assignments; and racism/sexism. A linear regression revealed that student-student interaction was the most powerful predictor of students’ intention to persist in the major beyond the introductory course. Other factors predicting intention to persist were pace/workload/prior experience and male gender. The findings suggest that computer science departments interested in increasing retention of students set structured expectations for student-student interaction in ways that integrate peer involvement as a mainstream activity rather than making it optional or extracurricular. They also suggest departments find ways to manage programming experience gaps in CS1. Categories and Subject Descriptors K.3.2 [Computer and Information Science Education] Computer Science Education General Terms Measurement, Human Factors. Keywords Retention; attrition; persistence; student-student interaction; peer interaction; gender; pace; experience gap; “Student Experience of the Major”; regression analysis. 1. INTRODUCTION Which factors best predict student intention to persist in an undergraduate computer science major? This study answers that question for one computer science undergraduate major using a research-informed survey and linear regression. The publicly available assessment instrument, the Student Experience of the Major Survey [4], was developed in collaboration between researchers at the Bren School of Information and Computer Sciences at the University of California-Irvine and the National Center for Women & Information Technology. Funded by the U.S. National Science Foundation, the survey has been customized for use by several other CS departments for identifying the strongest predictors of student persistence in the major. This study presents the survey results from one institution and uses linear regression to identify the strongest predictors of intention to major in CS at that institution. The use of regression allows decision makers to select solutions with greatest potential for impact in their own department. 2. LITERATURE ON STUDENT PERSISTENCE Theories of and research on student outcomes in higher education suggest that students’ individual characteristics combine with and are influenced by the educational environment to produce student achievement. Astin’s Input-Environment-Output model [1] has influenced most conceptual frameworks for accounting for and planning programs for student outcomes in institutions of higher education (e.g., [2][3][4][10][12][15][17] ). Factors related to student persistence generally include student background characteristics (e.g., gender, race/ethnicity, pre-college educational experiences); institutional characteristics (e.g., size, selectivity); student-faculty and student peer interaction; student satisfaction with the learning environment; and students’ ability to be involved in the academic experience (both educational and social) [2][12]. At the department or program level, those factors that faculty and administrators can control are likely to be of the greatest interest; these include student-faculty and student peer interaction, student interaction with the learning environment, and student engagement with the academic community. Studies conducted in CS are consistent with research in higher education institutional studies, though CS has features that also make it unique (e.g., presence or lack of programming experience; tendency to be unattractive to women). Factors related to persistence that have been studied (cf. [4][6][7][10]) include the positive impact of prior experience with programming (shown to be positively associated with success in introductory courses) as well as pace and workload; a negative relationship between perceived low grades and persistence (with women leaving at higher rates than men, even with the same grades); a perception of low social relevance or meaningfulness of curricula and assignments; low levels of student-faculty interaction, including faculty attitudes, feedback, encouragement, mentoring, and career advice; problems associated with student-student interaction, such as not feeling like one belongs, a heavy focus on individualized learning, or lack of access to peer support networks; issues of pedagogy, such as the positive influence of collaborative learning © ACM, 2009. This is the authors’ version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is published in ACM SIGCSE Bulletin, 41(2), pp. 282-286.