The student success conversation often gives short shrift to traditional liberal arts institutions. And yet, nearly 1,600 private institutions serve more than three million students nationwide. Just like any other higher education provider, liberal arts institutions must find a way to support these students with the resources necessary to graduate on time.  

In the past 24 months, there has been a distinct shift within higher education in terms of ownership of student success2

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This shift and external pressures are forcing institutional leaders across all segments – including liberal arts – to make student success and retention a strategic priority. In addition, today’s liberal arts institutions face a growing need to make a compelling argument regarding cost vs. value.

The Higher Learning Commission recently released an article1 emphasizing the need for private institutions to engage in effective data collection, predictive analytics, and interventions to support their students in this changing landscape.

“But we collect LOTS of data,” you may say. An EDUCAUSE study found that to be true, in many cases, but that the data was “collected but not connected.”3 In other words, the data is being gathered but useful insights from that data were rarely shared with advisors, faculty, deans, and other staff who could use that data to inform their retention efforts.

To look at data-informed student success, institutions first need to understand the four main types of data, and the very different purposes of each.

  1. Student Data. Basic information about a student, including admissions data, on-campus activity, grades, appointments and meetings, risk factors, and interventions.
  2. Metrics. Data that changes over time, and helps an institution measure progress, efficiency, and efficacy of a variety of initiatives.
  3. Insights. Data that connects the dots to reveal patterns, such as problematic course sequences, or student cohort performance in specific programs.
  4. Evidence. Data that can be used to support decision-making, challenge assumptions, change administrative policies and drive cultural shifts on campus.

In this context, liberal arts institutions need to ask themselves critical questions, such as:

  • How is student data gathered and stored? What kind of data is the most useful for predicting student outcomes, and how can that be modeled? How can student data be shared, securely, with the right people on our campus who might be able to use that data to help students?
  • How do we define success? How do we measure it? What goals can we set for yourself, and how can we measure your progress towards those goals? Are there benchmarks for peer institutions?
  • Is the data that is collected across campus presented to users in a helpful, insightful way, or is it too high-level to answer the most pressing questions? How can we tie what you have to what we need to know?
  • How reliable is our data? Can we lean on it when it comes time to justify reallocating a budget, changing a registration policy or expanding a program?

How is your institution addressing these issues? Let us know on Twitter


1Michael Latham, Randall Stiles and Kaitlin Wilcox. “Student Success at the Liberal Arts College.” Higher Learning Commission, 2017, based on Student Success at the Liberal Arts College: Best Practices in Operations and Research”; paper on findings presented at the 11th Annual National Symposium on Student Retention, Orlando, Florida, November 3, 2015. Retrieved March 16, 2017 from http://cop.hlcommission.org/Student-Success/stiles16.html.

2Gates Bryant. Tyton Partners, “Driving Towards a Degree, The Evolution of Planning and Advising in Higher Education, Part i,” 2016.

3Yanosky, Ronald, with Pam Arroway. “The Analytics Landscape in Higher Education,” 2015. Louisville, CO: EDUCAUSE Center for Analysis and Research

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