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Sarah Blanchard Kyte, The University of Arizona

Sarah Kyte.jpgHigher education is being reshaped by our increasingly data-driven world. While this presents larger questions how universities leverage student data, these efforts are typically led by institutional researchers, business analysts, and external vendors further removed from the day-to-day interactions between students and the practitioners that support them (Campbell, DeBlois, & Oblinger, 2007; Fletcher & Karp, 2015; Gaines, 2014; Higgins, 2017). As a primary point of contact between universities and students, academic advisors are often asked to integrate data-driven tools into their practice but only rarely do the concerns of advisors guide the creation of new approaches to institutional data (Carlton, 2010; Klempin, Grant, & Ramos, 2018; Nutt, 2017; Underwood & Anderson, 2018). However, by bringing the advising perspective to relatively simple analyses of student data, new opportunities can be found to support student pathways with helpful information.

At my institution—The University of Arizona—we recently put such an approach into action to learn more about student pathways between majors and to share our findings in an informative and actionable way with our advising community. In doing so, we aimed to use a data-driven perspective on past student decision-making vis-a-vis majors to improve—even in a small way—how we support students as they navigate their options at our university. Here, I describe in detail some of the thinking behind this effort, the end product, and the process of creating a conversation around this work with the advising community. By sharing this work with a larger audience in this format, we hope to empower and inspire others to consider replicating or extending these efforts at their own institutions.

The Challenge and Opportunity in Supporting Students Changing Majors

At many large institutions of higher education, like the University of Arizona, students are able to choose between a hundred or more different academic programs set within a number of colleges. Consistent with national trends (Leu, 2017), at our university, nearly half of students who have graduated in recent years changed major at least once.

Changing majors can be an exciting moment for students but also one that presents risk. On the one hand, choosing a new major can indicate that the student has found their academic home at the university with a set of accompanying socioemotional benefits including a heightened sense of purpose, belonging, and motivation that are all critical for student persistence (Montag, Campo, Weissman, Walmsley, & Snell, 2012; Soria & Stebleton, 2013; Tinto, 2004). On the other hand, a change of major can disrupt key relationships, including those with advisors who typically serve students according to major (Carmack & Carmack, 2016). Moreover, because graduating hinges on meeting major-specific course requirements, landing within a final major quickly and efficiently is important for timely graduation (Sklar, 2018).

Identifying Tried-and-True Graduation Pathways: Learning from Past Student Behavior

When choosing a major, students have historically drawn on their experiences inside and outside of the classroom, including coursework, faculty, advisor input, career goals, and their growing knowledge about disciplines and industries (Blanchard Kyte & Riegle-Crumb, 2017; Gordon & Steele, 1992; Montag et al., 2012; Ruder & Van Noy, 2018; Sklar, 2018). One additional source of information that students previously did not have access to was objective insight into what change-of-major choices students like them made in the past and how these shaped their graduation outcomes. At our university, we were able to provide this data-driven view of past student pathways—or the student journey from their starting major to their final major—thanks to the availability of student data and some simple analytical tools and know-how.

To provide students and advising professionals with the most useful information about the trends among past students, we established two key criteria for the pathways we wanted to highlight, which we combined in the phrase, “tried-and-true graduation pathways.” First, we identified “tried” pathways by measuring how common each student pathway was among recent students to avoid generalizing about pathways that were in reality, very rare. Second, we identified pathways where students tended to graduate within four years (i.e. “true” pathways). Therefore to be “tried-and-true,” a pathway needed to have been travelled by at least 8 recent students and at least half of the students who tried it—whether 8 or 80—needed to have graduated within four years. These are fairly restrictive criteria; for context, of more than 3,300 total pathways taken by students who first enrolled in 2008 or later, fewer than 500 pathways met the dual criteria for a tried-and-true pathway.

In sharing insights from our analysis with our community, we present data on tried-and-true pathways into two types of major-specific reports that combine a data-driven infographic with positive messaging about changing major. The first type, titled “What else is out there?,” is meant to help students currently in one major think about what other majors they might want to consider. The second type, titled “Welcome to ______!,” offers insight into where students who changed into a particular major started their time at our university. The pertinent data on tried-and-true pathways related to the focal majors are presented using a bubble plot where the size of each bubble shows how common that particular pathway has been and the bubbles are color coded by college. Also included in each report is the percentage of students in that major who changed into or out of the focal major. An example of each type of infographic is shown below.

Kyte infographic.jpg

While we see these infographics as a useful data-point in conversations between advisors and students interested in changing majors, there are several important caveats to keep in mind. First, the tried-and-true designation likely reflects both shared student interest between the starting and ending major but also the alignment between the courses required by each major. In this way, pathways between majors with similar course-requirements are more likely to allow for four-year graduation and therefore,­ be considered tried-and-true. In addition, though we focus on four-year graduation, many more students will go on to graduate with additional time. Most importantly, data is not destiny. Working closely with an advisor is the key to successfully navigating any pathway that a student may choose; therefore, the historical prevalence of particular pathways should not dissuade students from forging the path that is right for them. At the same time, those pursuing off-the-beaten-path changes of major should do so with the support and guidance of their advisors and other mentors.

Putting Data-Driven Insights into Practice

Sharing this information across our campus entailed a collaboration between the analytical team, the Advising Resource Center, the advising community, and the colleges. We began by making this work the focus of one of our ongoing lunch and learn sessions through the Advising Resource Center. The thirty or so academic advisors who attended learned more about the work and its motivation, took a deep dive into interpreting a few of the infographics and worked together to think about different ways to incorporate these insights into various advising activities throughout the student life cycle. In particular, advisors suggested using them during orientation and registration conversations with students interested in exploring related majors, to help undecided students declare a major, in identifying parallel plans for students struggling to thrive in their current major, in major-exploration courses, and during new advisor training.

Interest in this work spread beyond the advising community such that new opportunities to support student pathways were opened up with several colleges and offices across campus. As one small example, the College of Public Health decided to incorporate the “Welcome to Public Health!” infographic in their change-of-major orientation. We also discussed with Curricular Affairs how bringing detailed course-requirement information into the analysis could allow colleges with large populations of undecided students to create more efficient graduation pathways for their students starting in their first semester. Finally, we are working to integrate these data into various online degree-exploration tools offered to prospective and current students.

In the big picture, what we have begun here is one small, but hopefully useful, tool developed in partnership between local experts in advising and analytics. In an era where third-party vendors are using cutting-edge machine learning approaches to mine massive institutional datasets for insights into student success, there is still tremendous unexplored value in small, targeted efforts to understand the pathways carved by students across our universities. Our hope in sharing the story of this work with the wider advising community is that others will consider gathering those simple data points—where students started out, where they ended up, and whether or not they graduated—to learn about the tried-and-true graduation pathways at their own institutions. More importantly, by bringing this new vantage point on the choices and outcomes of students who have come before them into their conversations with current students, advisors can offer additional food-for-thought as they support students’ choices during their postsecondary journey.

Sarah Blanchard Kyte
Senior Research Scientist
Student Success and Retention Innovation
The University of Arizona
skyte@email.arizona.edu

References

Blanchard Kyte, S., & Riegle-Crumb, C. (2017). Perceptions of the social relevance of science: Exploring the implications for gendered patterns in expectations of majoring in STEM fields. Social Sciences, 6(1), 19. https://doi.org/10.3390/socsci6010019

Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007). Academic analytics: A new tool for a new era. EDUCAUSE Review, 42(4), 40–57.

Carlton, D. (2010). What do you think academic advising will be like in another ten years? The Mentor: An Academic Advising Journal, 12(1).

Carmack, A. L., & Carmack, H. J. (2016). S-PASS: Using hand-off communication strategies for academic advising. Academic Advising Today, 39(2). Retrieved from https://www.nacada.ksu.edu/Resources/Academic-Advising-Today/View-Articles/S-PASS-Using-Hand-off-Communication-Strategies-for-Academic-Advising.aspx

Fletcher, J., & Karp, M. (2015). Using technology to reform advising: Insights from colleges. New York, NY: Columbia University, Teachers College, Community College Research Center.

Gaines, T. (2014). Technology and academic advising: Student usage and preferences. NACADA Journal, 34(1), 43–49. https://doi.org/10.12930/NACADA-13-011

Gordon, V. N., & Steele, G. E. (1992). Advising major-changers: Students in transition. NACADA Journal, 12(1), 22–27. https://doi.org/10.12930/0271-9517-12.1.22

Higgins, E. M. (2017). The advising relationship is at the core of academic advising. Academic Advising Today, 40(2). Retrieved from https://www.nacada.ksu.edu/Resources/Academic-Advising-Today/View-Articles/The-Advising-Relationship-is-at-the-Core-of-Academic-Advising.aspx

Klempin, S., Grant, M., & Ramos, M. (2018). Practitioner perspectives on the use of predictive analytics in targeted advising for college students (CCRC Working Paper No. 103). New York, NY: Columbia University, Teachers College, Community College Research Center.

Leu, K. (2017). Beginning college students who change their majors within 3 years of enrollment (Data Point No. NCES 2018434). National Center for Education Statistics. Retrieved from https://nces.ed.gov/datapoints/2018434.asp

Montag, T., Campo, J., Weissman, J., Walmsley, A., & Snell, A. (2012). In their own words: Best practices for advising millenial students about majors. NACADA Journal, 32(2), 26–35. https://doi.org/10.12930/0271-9517-32.2.26

Nutt, C. (2017). Creating a data-driven advising culture: Overcoming three central roadblocks. Academic Advising Today, 40(1). Retrieved from https://www.nacada.ksu.edu/Resources/Academic-Advising-Today/View-Articles/From-the-Executive-Director-Creating-a-Data-Driven-Advising-Culture-Overcoming-Three-Central-Roadblocks.aspx

Ruder, A., & Van Noy, M. (2018). Adjusting expectations: The impact of labor market information on how undergraduates view majors and careers. New Brunswick, NJ: Rutgers Education and Employment Research Center. Retrieved from https://smlr.rutgers.edu/sites/default/files/documents/Centers/eerc_majorchoice_issue_brief_final_0.pdf

Sklar, J. C. (2018). Event history analysis for investigating the likelihood and timing of changing majors. NACADA Journal, 38(1), 47–60. https://doi.org/10.12930/NACADA-17-039

Soria, K. M., & Stebleton, M. (2013). Major decisions: Motivations for selecting a major, satisfaction, and belonging. NACADA Journal, 33(2), 29–43. https://doi.org/10.12930/NACADA-13-018

Tinto, V. (2004). Student retention and graduation: Facing the truth, living with the consequences. Pell Institute for the Study of Opportunity in Higher Education. Retrieved from https://files.eric.ed.gov/fulltext/ED519709.pdf

Underwood, Z. W., & Anderson, M. (2018). Technology and academic advising: A case for embracing change in academic advising. Academic Advising Today, 41(1). Retrieved from https://www.nacada.ksu.edu/Resources/Academic-Advising-Today/View-Articles/Technology-and-Academic-Advising-A-Case-for-Embracing-Change-in-Academic-Advising.aspx

Cite this article using APA style as: Kyte, S.B. (2019, March). Changing major, staying on track: Bringing data-driven insights into tried-and-true graduation pathways to advising. Academic Advising Today, 42(1). Retrieved from [insert url here] 

Posted in: 2019 March 42:1

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