Leveraging Analytics in Community Colleges

Analytics, according to Don Norris and Linda Baer, are tools for making data-driven decisions that can contribute positively to student success and institutional effectiveness.¹ While some corporate entities have used analytics to make decisions about customers for quite some time,² the use of analytics is relatively new in higher education.³ Nonetheless, analytics have the potential to aid community colleges in achieving accountability measures issued by federal, state, and local entities. Early postsecondary adopters of analytics have gained insights through the use of academic, learning, and predictive analytics, noted Tod Treat.⁴ In this article I intend to define terms related to analytics, present a few postsecondary analytics examples, and offer additional readings and resources to support community college leaders in assessing analytics' usefulness for two-year institutions.

Key Terms

Analytics encompasses a number of terms, so I have provided the following table to explicate the various constructs and aid in understanding the information provided in this article.

Synthesizing and Relating Key Terms

Analytics are powerful processes of data analysis that have the potential to aid in informed decision making.¹⁵ Academic and learning analytics have specific purposes: Academic analytics differ from learning analytics in that learning analytics relate specifically to data analyzed for the purpose of addressing teaching and learning goals,¹⁶ while academic analytics center on data analyzed for the purpose of making better operational and financial decisions.¹⁷ Predictive analytics takes both types of analytics a step further by identifying trends to forecast the future, which can assist with academic, operational, and financial planning.¹⁸ Data mining refers to extracting data from a particular system to generate analytics,¹⁹ and data integrity should be considered when storing data in a data warehouse. Dashboards provide an interface for viewing the outputs of analytics²⁰ potentially generated using data from a data warehouse, while a big-data approach that incorporates analytics can further enhance business intelligence.

Using Cases to Make a Case for Analytics

Note that a number of stakeholders have charged community colleges to graduate more students.²¹ Community colleges operate with limited financial and human resources while providing open access to higher education, and they maintain multiple missions related to both academic preparation and workforce development. These complexities contribute to institutional leaders' motivation to explore how analytics can add value to community colleges.

Examples from two- and four-year institutions of higher education shed light on how analytics can pay off when managed effectively. Rio Salado College, Harvard University, and Austin Peay State University are a few of the institutions using analytics to support student success and institutional effectiveness.

Case: Rio Salado College

One of the most notable implementations of analytics in higher education occurs at Rio Salado College. Part of the Maricopa Community Colleges, Rio Salado operates a model that includes an in-house–developed course management system, accelerated educational programs, and the ability for students to begin a course nearly any week of the year.²² Rio Salado can predetermine student risk based on reduced engagement and provide intervening responses. Specifically, "Rio Salado is using analytics to predict at-risk behavior using activity factors such as log-ins and site engagement."²³ Students have access to a dashboard, RioCompass, to monitor their progress toward degree completion,²⁴ and instructors have access to a dashboard to regularly monitor available student analytics.²⁵ This allows instructors to address student needs more expeditiously and aids in promoting retention.

Case: Harvard University

Harvard mines classroom data using a system called Learning Catalytics, a web-based platform developed by the Mazur Group at Harvard that supports peer instruction. Students log in to the interactive classroom from a computer or mobile device; the professor produces a problem generated on the students' screen for them to answer; the system analyzes the answers and decides how to pair students as study partners; messaging appears on the students' screens indicating who they will work with; and the professor receives a map of information displaying how the students did so the professor can decide who to assist.²⁶ Rich data produced by such a system would enable community college educators to adjust instructional practices to provide customized and direct support to learners.

Case: Austin Peay State University

Austin Peay State University (APSU) has taken substantial steps in the areas of predictive analytics and dashboards. Like other institutions, the campus has a wealth of information, but analyzing and using this information makes the difference for timely and well-informed decision making.²⁷ For this reason APSU's Office of the Provost has collaborated across various units to "add workflows to automate the flow of changes of major, changes of grade and course substitutions and create real-time dashboards that show current course enrollments and predict the eventual sizes of classes and campus population."²⁸

APSU has established itself as a high-tech academic institution, having added mobile apps and a course recommendation system (Degree Compass²⁹) that employs predictive analytics to help pair students with courses based on areas of personal talent and program of study. APSU asserts that "this system akin to Pandora, Amazon, or Netflix employs a grade prediction model that combines hundreds of thousands of past course grades to make each individualized recommendation."³⁰ Drawing from corporate models, APSU has elevated expectations related to how higher education institutions can effectively support students and conduct business operations.

Critical Perspectives about Analytics

Critics of practices related to analytics such as data mining liken it to standardized testing in K–12, arguing that tracking student data in this way makes for a sterile analysis void of creativity. Arguments include that "counting clicks within a learning management system runs the risk of bringing that kind of deadly standardization into higher education."³¹ Other critical perspectives relate to profiling students, which could lead to biased behaviors and expectations.³² Still others have highlighted concerns about affordability, misuse of data, data regulations, ignorance regarding data usage, data inaccuracies, individual privacy, insufficient return on investment, business-like practices in higher education, and inability to measure higher education performance.³³ Yet, the ultimate question is, who owns the data collected and has a say in how it is used?³⁴

Analytics and Community Colleges

While critics of analytics in colleges and universities raise important points, expectations that did not exist at the inception of higher education require the integration of new and highly sophisticated practices to support student success. Moreover, when it comes to leveraging technology to enhance community colleges, institutional leaders must keep one eye on the present and another on the future. Without exception, analytics are critical to the future success of community colleges. Albert Einstein said, “We cannot solve our problems with the same level of thinking that created them.” Likewise, today’s challenges require next-level tools. Community colleges must commit resources to facilitate the effective acquisition, compilation, manipulation, presentation, and storage of analytics that provide valuable insights, while doing so in such a way as to ensure data integrity and security.

In a recent article Malcolm Brown shared his vision of the trajectories of six digital technologies affecting the future of higher education,³⁵ for example, all of which can use analytics for greater impact. Furthermore, the gains from using analytics have the potential to outweigh the negative implications. Integrated Planning and Advising Services (IPAS) systems offer an excellent example: Effective IPAS systems could be considered intrusive in ways that concern critics of student data analytics, yet their effective implementation on campuses and integration with existing campus technology (such as student information systems) adds a powerful tool for supporting student success and can shorten the journey to graduation. In the end, a technology strategy inclusive of analytics and combined with an unwavering focus on student achievement might prove an essential precursor to student success at all colleges.

Notes

1. Don M. Norris and Linda L. Baer, "Building Organizational Capacity for Analytics," EDUCAUSE white paper (February 2013).
2. T. H. Davenport and J. G. Harris, Competing on Analytics (Cambridge, MA: Harvard Business School Press, 2007).
3. Angela van Barneveld, Kimberly E. Arnold, and John P. Campbell, "Analytics in Higher Education: Establishing a Common Language," ELI (2012).
4. Tod Treat, "4Bs or not 4Bs: Bricks, bytes, brains, and bandwidth," New Directions for Community Colleges, Vol. 2011, No. 154 (Summer 2011): 5–15.
5. Don Norris, Linda Baer, and Michael Offerman, (2009). "A National Agenda for Action Analytics," white paper of outcomes from the National Symposium on Action Analytics, held September 21–23 in St. Paul, Minnesota (November 15, 2009), 1.
6. van Barneveld, Arnold, and Campbell, "Analytics in Higher Education," 8.
7. Louis Soares, "The Rise of Big Data," EDUCAUSE Review, Vol. 47, No. 3 (May/June 2012): 60–61; see 60.
8. Gartner IT Glossary, Business Intelligence, n.d.
9. Darrell M. West, "Big Data for Education: Data Mining, Data Analytics, and Web Dashboards," Brookings Research, September 4, 2012; 6.
10. Michael Teeling, "What is data integrity? Learn how to ensure database data integrity via checks, tests, & best practices," Veracode blog post, May 14, 2012; paragraph 1.
11. Rudy Miranda and Eka Miranda, "Management Report for Marketing in Higher Education Based on Data Warehouse and Data Mining," International Journal of Multimedia and Ubiquitous Engineering, Vol. 10, No. 4 (2015): 291–302; 292.
12. Leah Lang and Judith A. Pirani, "BI Reporting, Data Warehouse Systems, and Beyond," CDS Spotlight Report, April 23, 2014; 4.
13. van Barneveld, Arnold, and Campbell, "Analytics in Higher Education," 8.
14. Wayne W. Eckersen, "Predictive Analytics: Extending the Value of Your Data Warehousing Investment," First Quarter 2007, TDWI Best Practices Report (2007), 5.
15. Norris, Baer, and Offerman, "A National Agenda for Action Analytics."
16. van Barneveld, Arnold, and Campbell, "Analytics in Higher Education."
17. Ibid.
18. Eckersen, "Predictive Analytics."
19. Miranda and Miranda, "Management Report."
20. West, "Big Data for Education."
21. American Association of Community Colleges, "Reclaiming the American Dream: Community Colleges and the Nation's Future," A Report from the 21st-Century Commission on the Future of Community Colleges, 2012; and Lumina Foundation, A stronger nation through higher education, annual report (2015).
22. Treat, "4Bs or not 4Bs."
23. Ibid., 11.
24. Jennifer McGrath, "RioAchieve: Using technology to increase retention and completion at scale," Next Gen Learning Blog, Next Generation Learning Challenges, December 17, 2014.
25. Treat, "4Bs or not 4Bs."
26. Marc Parry, "Colleges Mine Data to Tailor Students' Experience," Chronicle of Higher Education, December 11, 2011.
27. Austin Peay State University, "Predictive analytics, workflows, dashboards, and mobile apps," n.d. [http://www.apsu.edu/academic-affairs/predictive-analytics-workflows-dashboards-and-mobile-apps]
28. Ibid., paragraph 1.
29. Tristan Denley, "Austin Peay State University: Degree Compass," EDUCAUSE Review, September 5, 2012.
30. Austin Peay State University, paragraph 3.
31. Parry, "Colleges Mine Data," paragraph 10.
32. Rebecca Ferguson, "Learning analytics: drivers, developments and challenges," International Journal of Technology Enhanced Learning, Vol. 4, No. 5/6 (2012): 304–317.
33. Jacqueline Bichsel, Analytics in Higher Education: Benefits, Barriers, Progress, and Recommendations (Research Report) (Louisville, CO: EDUCAUSE Center for Applied Research, August 2012). available from
34. Kyle M. L. Jones, John Thomson, and Kimberly Arnold, "Questions of Data Ownership on Campus," EDUCAUSE Review, August 25, 2014.
35. Malcolm Brown, "Six Trajectories for Digital Technology in Higher Education," EDUCAUSE Review, June 22, 2015.

Treca Stark is manager of the Technology Resource Center at Prince George's Community College, where she provides oversight and coordination for technology training resources. In addition to presenting at various conferences and receiving awards throughout her career, she serves as an EDUCAUSE Reviewer and is a certified Higher Education Peer Reviewer through Quality Matters. Stark earned a master's of education in Educational Administration from the University of New Orleans, a graduate certificate in e-learning from George Mason University, and a bachelor of arts with distinction in Political Science from Loyola University New Orleans. She is pursuing a doctorate of education in Higher Education–Community College Leadership at Morgan State University.