By: Rudy M. Molina, Jr., Ph.D. & Paul E. Mabrey, III, Ph.D.
"Field Notes" is an occasional Connect column covering practical and philosophical issues facing admissions and registrar professionals. The columns are authored by various AACRAO members. If you have an idea for a column and would like to contribute, please email the editor at communications@aacrao.org.org.
Data and data analytics continue to make headlines across higher education. The frequency and alarm level of these has only accelerated with the explosion of artificial intelligence and products such as Chat GPT in the last year. It is easy for any higher
education professional, novice or seasoned veteran, to feel overwhelmed, apathetic, optimistic, and even apocalyptical when encountering these discussions daily. In our third and final article for this student success series, we focus on leveraging
data and data analytics for actionable insights with the goal of providing clarity and direction rather than adding to the cacophony of the postsecondary data landscape.
An important starting point is establishing a shared understanding of data analytics. In their
Joint Statement on Analytics, the Association of Institutional Research, EDUCAUSE, and the National Association of College and University Business Officers define analytics as “the use of data, statistical analysis, and explanatory and predictive models to gain insight and act on complex issues.” Note the emphasis here on use, insight, and action. Too often we are collecting data for data sake. Maybe it is simply that we have always collected that data, we are worried about what happens if we don’t collect that data, or we collect as much data as we can, “just in case.” While often well-intended, these motivations for data collection (without use, purpose, or intended action) can erode trust and credibility, generate resistance to data analytics, or even result in the misuse of data.
Numerous institutions have become known for their ability to collect, analyze, and act on data for improvement, particularly around equitable student success.
Georgia State University is probably an institutional exemplar. They closed equity gaps by leveraging data analytics to identify opportunities for institutional improvement (e.g., summer melt, completion grants) while simultaneously monitoring 800+ data-informed risk factors that can initiate early intervention and support. Syracuse University is another institution doing great work with analytics to improve retention and student success. Dr. Kal Srinivas, Director of Retention, shared that more than just the use of analytics,
culture change was critical and this started with faculty. She said, “they [faculty] are integral to student success.” National organizations and alliances have taken notice of institutional data use as well. For instance, the Association of Public & Land-Grant Universities (APLU) generated
14 case studies where institutions have utilized student data toward actionable insights for student success. The University Innovation Alliance has also published playbooks,
like this one on proactive advising, offering advice and best practices informed by institutional collaboration, experimentation, and data analysis.
The impressive examples and models from institutions doing this work for the last 10+ years are inspiring but can seem daunting, overwhelming, and even impossible. Gratefully, the institutions, national associations, professional organizations, and
the individuals that constitute them have been generous with their insights and wisdom. For example,
Associations have developed data maturity frameworks, models, and assessments to help institutions along the analytics journey
.
As demonstrated throughout this article series on data, analytics, and student success,
data collection and analysis should be guided by your institutional, social, and educational context and not for data's sake.
At James Madison University, our team has used three basic questions to help us identify the core issue and remain focused on viable solutions. In short, we refer to it as P/RQ/Data. They include:
P - What is the core problem?
RQ - How can the problem statement be translated into a research question or a series of related questions?
Data - What data and related information will be required to provide insight into the research questions?
As we move through the phases of a given project (designing, piloting, implementing, and reflecting) we frequently return to these questions:
What, if anything, will be done with the data collected? How will this data be used, by whom, and for whom?
How will collecting this data help us answer our research questions?
How labor-intensive will mining and cleaning the data be and whose labor?
How is this aligned with the departmental, divisional, or university mission, vision, and values?
And most importantly, what impact might this have on different student experiences of success, retention, and graduation?
No matter where you or your institution are in or on the data journey, keep in mind that “the decision context is very much as important as, if not more important than, the data alone.”