Assessing Long-Term Effects of Inquiry-Based Learning: A Case Study from College Mathematics

Marina Kogan Sandra L. Laursen 0 ) Ethnography & Evaluation Research, University of Colorado Boulder , 580 UCB, Boulder, CO 80309-0580, USA As student-centered approaches to teaching and learning are more widely applied, researchers must assess the outcomes of these interventions across a range of courses and institutions. As an example of such assessment, this study examined the impact of inquiry-based learning (IBL) in college mathematics on undergraduates' subsequent grades and course selection at two institutions. Insight is gained upon disaggregating results by course type (IBL vs. non-IBL), by gender, and by prior mathematics achievement level. In particular, the impact of IBL on previously low-achieving students' grades is sizable and persistent. The authors offer some methodological advice to guide future such studies. - Marina Kogan has bachelors degrees in sociology/anthropology and computer science from City University of New York and an M.A. in sociology from the University of Illinois Urbana-Champaign. She conducted this work as a researcher at Ethnography & Evaluation Research at the University of Colorado Boulder and is now pursuing a doctorate in computer science from the University of Colorado Boulder, with research interests in the application of computer modeling and data mining methods to social problems. Sandra L. Laursen has undergraduate degrees in chemistry and French from Grinnell College and a Ph.D. in chemistry from the University of California-Berkeley. She is the Co-director of Ethnography & Evaluation Research at the University of Colorado Boulder and leads evaluation and research studies of higher education and career paths in science, engineering, and mathematics. She can be reached at . To date, the most persuasive studies of active learning have examined student outcomes within a single course at one or several institutions (e.g., Deslauriers et al., 2011; Kwon, Rasmussen & Allen, 2005; and studies analyzed by Froyd, 2008, and Ruiz-Primo et al., 2011). However, as active learning approaches are applied more broadly, evaluating their outcomes presents new methodological challenges. Measures to assess effectiveness must be general enough to apply across different classrooms and institutions. A common testthe most direct method of evaluating classroom learningmay not be available or applicable. Students course grades and course-taking patternstheir choices to pursue (or not) subsequent courses in a disciplineoffer broad and arguably objective measures for evaluating the effects of an educational intervention. While grading standards differ across instructors, courses, and campuses, grades have a fairly stable social meaning (Pattison, Grodsky & Muller, 2013). As part of students academic transcripts, grades become lasting records of achievement. Like grades, course-taking patterns apply to varied academic contexts; they may reflect students sustained or lost interest in a discipline following an initial experience. Several recent studies have used various grade- and course-taking measures to evaluate the success of an educational intervention, including final grades and pass/fail rates (e.g., Dubetz et al., 2008; Tai, Sadler & Mintzes, 2006; Tien, Roth & Kampmeier, 2002), the next grade in a course sequence (e.g., Farrell, Moog, & Spencer, 1999; Gafney & Varma-Nelson, 2008), grades in multiple subsequent courses (e.g., De Paola, 2009; Weinberg, Hashimoto, & Fleisher, 2009), and enrollment in higher level electives (Carrell & West, 2010). Mostrom and Blumberg (2012) suggested that student-centered courses are subject to accusations of grade inflation because the course has lost content or rigor or because different assessment methods enable students to do better. They also argued that grade improvement may in fact measure real improvement in learning. Measures that focus on subsequent courses avoid this issue because students who did and did not experience the intervention all take the same later courses. Moreover, such measures can detect valued and lasting impact on students learning, academic success, or academic choices (Derting & Ebert-May, 2010). This study examined undergraduates grades and course-taking following an inquirybased learning (IBL) experience in college mathematics. In the context of mathematics, IBL approaches engage students in exploring mathematical problems, proposing and testing conjectures, developing proofs or solutions, and explaining their ideas. As students learn new concepts through argumentation, they also come to see mathematics as a creative human endeavor to which they can contribute (Rasmussen & Kwon, 2007). Consistent with current socio-constructivist views of learning, IBL methods emphasize individual knowledge construction supported by peer social interactions (Ambrose et al., 2010; Cobb, Yackel & McCain, 2000; Davis, Maher & Noddings, 1990). In this article we report our analysis of student academic records for patterns in grades and course-taking among students who had earlier taken an IBL mathematics course or comparative, non-IBL course taught with other methods. We focus on results for two groups often under-served by traditionally taught college mathematics courses, women and low-achieving students. The academic records study was one element of a large, mixed-methods study of IBL mathematics as implemented at four universities hosting IBL Math Centers (Laursen, Hassi & Hough, 2013; Laursen, Hassi, Kogan, Hunter & Weston, 2011; Laursen, Hassi, Kogan & Weston, 2013). Observation, survey, interview, and test data were gathered from over 100 sections of 40 courses aimed at varied levels and audiences. First we describe results of classroom observations which establish that IBL was a student-centered, educational intervention. We then outline the methods used to study subsequent grades and course-taking for students who had completed an IBL course or its non-IBL counterpart (for details see Laursen et al., 2011). Setting and Courses Each of the four institutions selected and developed its IBL courses independently and labeled them as IBL or non-IBL based on instructor participation in their grant-funded IBL Center. The courses were well established, having been taught several times prior to our data collection in 2009. To check these labels and to establish whether observed differences in student outcomes were meaningful, we carried out over 300 hours of classroom observation of 42 course sections, having received human subjects approval from our Universitys Institutional Review Board and that of each study site where required. The results showed that, despite variation among courses and instructors, several key characteristics differentiated the IBL courses from the non-IBL courses. On average, about 60% of class time in IBL courses was spent on student-centered activities such as small group work, student presentation of problems at the board, or whole-class discussion, while in non-IBL (...truncated)