Raising the Bar on Student SuccessThe authors explored student success initiatives across the U.S. and found that some institutions are making good use of the plethora of data stored by institutions, but the gap is widening between those out in front in analytics-informed student success and their peer institutions. “There is a visible shift from using analytics solely to report what has happened as with the performance metrics gleaned from descriptive and diagnostic analytics, to the more insightful and actionable insights and foresight provided by predictive and prescriptive analytics,” said Don Norris. Norris and Baer sum up the difference in these different types of analytics as follows:
|Descriptive Analytics||What has happened?|
|Diagnostic Analytics||Why did it happen?|
|Predictive Analytics||What will happen if past trends and current behaviors continue?|
|Prescriptive Analytics||What is the best that can happen and how can we make it so?|
Connecting the DotsThe difference between those succeeding and those lagging extends well beyond established budgets and resource allocations. “Student analytics work has, up until quite recently, been fragmented and silod, at best,” said Norris. “The institutions winning at this by measurably increasing persistence and graduation are working to connect the dots – pulling the various initiatives and pieces together to create an end-to-end system or process spanning the entire student life cycle.” Cross-functional teams are essential to these efforts and the authors found many examples that were highly effective. “We looked at Aspen Prize winners that were using analytics well to discover commonalities,” said Baer, “and it’s obvious the important role courageous leaders play. The fundamental difference prize-winning colleges have from other institutions is not necessarily resources, according to Baer. “The winning colleges have courageous leaders willing to take the bold moves needed to expose the insights yielded by analysis that are difficult, uncomfortable, or even embarrassing at first to acknowledge,” she said. They build a culture that supports and nurtures the cross-functional teams.
The Obligation of Knowing“Extensive discussions and debates have swirled around what is being called the obligation of knowing,” said Norris. “Now that we see a student’s likelihood of success or failure, are we willing to take all the steps necessary to provide the right help? Are we even if the steps take us beyond our comfort zone? There is a moral imperative and not everyone is ready to accept it.” Baer suggests that once leaders acknowledge that data and analytics are important, they must commit to putting the people behind the positions to build out their institutional capacity. Do they build internally or externally? They need to determine if there are people who can do analytics and predictive work on campus or if they could up-skill some people with short course training or longer certificates and degrees. Or, campuses can work with partners to expand their capacities including campuses that could share analytics work and/or work with consultants and student solution tools. “The single most important thing is to simply start – start building a culture committed to analytics, committed to the truth, committed to leaning into student success,” said Baer. “A small college with limited resources can begin by learning from peers and exploring best practices, then form collaborations or consortiums to buy into economies of scale. They can build capacity by partnering with companies like Civitas Learning that bring in intellectual talent that would be cost-prohibitive for the institutions to add as full-time employees. “ Norris suggests institutions work to build organizational efficiencies by building toward comprehensive frameworks for student success. Examples he cites includes Arizona State University. “ASU is working hard to connect the dots. They had tremendous leadership in Michael Crow who openly committed several years ago to improving diversity, persistence and graduation rates while increasing enrollments. He uses analytics effectively to get there. ASU has not completely perfected their best practice. This work is expeditionary and iterative. But one of the reasons he’s succeeding is the intentionality of his work and goals. It’s not just about using data – it’s about an intentionality to stick with the commitment,” said Norris. Baer says following commitment and intentionality, the next step is for institutions to move from their existing use of descriptive analytics into predictive analytics in order to fully optimize their student success work. Baer and Norris suggest that the platform and work Civitas Learning is doing to create a comprehensive, enterprise-wide analytics approach will be a key ingredient in bringing transformation to existing processes and policies.
Leading ExamplesInstitutions engaged in this work are often reluctant or reticent at first to begin, according to Baer, because of fear of their data being insufficient or flawed. “In fact, it often is flawed or messy,” said Baer. “But as campuses use their data in this work, these data actually improve through scrutiny and cleansing, so the gains become exponential once an institution starts down this path.”
“By leveraging analytics and data science, leading-edge institutions are raising their aspirations to truly “optimize’ student success for individuals or cohorts.” – Dr. Linda Baer“The institutions leading in this work are often utilizing a change management process John Kotter has developed and proven successful to deal with complex, cross-cutting issues that defy management through traditional organizational hierarchies,” said Norris, who cites Kotter’s award-winning Eight Steps Change Process as one of a long list of valuable resources shared in the white paper.
Analtyics Based Interventions to Manage / Mitigate RiskBaer and Norris provide a useful rubric of seven dimensions their research pointed to that leading institutions are using to reduce, manage and/or mitigate risk in their efforts to optimize student success. These include:
- Managing the Student Pipeline
- Eliminate Bottlenecks and Barriers
- Dynamic Interventions to Address Risky Behavior
- Leverage Individual Planning and Advising for Student Success (iPASS)
- Next-Gen Learning
- Big Data/Big Science
- Academic and Employability Success
Integrated Planning and AdvisingBaer and Norris dive deeper by giving examples of the success of iPASS (Integrated Planning and Advising for Student Success) drawn from institutions that have experienced significant culture change and performance improvement in building out their analytics framework. These include Sinclair Community College, Austin Community College, Arizona State University and Georgia State University. “In the white paper we discuss the benchmarking of the characteristics of these important, emerging systems conducted by ECAR (The EDUCAUSE Center for Applied Research). They offer a range of services that seek to realize a comprehensive vision of a technology-enabled and integrated digital environment,” said Baer who included the following capabilities:
- Education planning (identifying the degree and the best path to its achievement),
- Progress tracking (asking whether the learner is on course toward degree completion),
- Advising and counseling (offering services such as mentoring and tutoring), and
- Early-alert systems (initiating proactive intervention with at-risk students)