What is student success? Let’s start with the most fundamental definition: completing and receiving a degree. For decades, national completion rates have hovered around 20-30 percent in three years for an associate’s degree and 50-60 percent in six years for undergraduate degrees.
Here’s the problem: The data is not actionable and is enabling low-expectations creep, such as setting expectations that a six-year completion rate is a success metric for first-time full-time students in a four-year program. Low-expectations creep can best be summed up by Eberhardt Rechtin:
“High expectations, because they are unlikely to be fulfilled, define failure… low expectations, because they are likely to be accomplished, define success.”
Our messy definition of success
1. It is very hard to find applied definitions of student success that are not, at the heart, derivatives of failure or risk. A classic student-success initiative begins by collecting data on dropouts and uses that as a proxy for reducing failure, rather than for scaling success.
2. When risk data is analyzed to personalize success plans, too often socio-demographics such as income, zip code, gender, and race become the focus. A very real consequence of this practice is that risk labels are assigned to student sub-groups before they even arrive at an institution. By age 17 or older, students are savvy at recognizing how they are being esteemed by an ecosystem, so this approach is limiting and counter-productive for both the student and the institution.
3. Many definitions deconstruct the student experience into a single process or point of retention. For example, randomly asking representatives from admissions, retention, and career services departments for their definition of or data on student success yields unique, short-term answers.
4. Rarely do definitions incorporate student expectations, or that of their families. Success is a grade, a score, or something done to them, rather than with them.
(Next page: Why we need to collect data on a different subset)
Why is there such a strong reliance on forming definitions of student success based on dropout, risk flagging, and process data? Likely because, despite institutional data being messy, this particular set of data may actually seem more concrete. Being able to nimbly identify the sub-group of students who drop out is not messy, even if establishing causation is. However, identifying and getting clean data on the student sub-group at the highest optimization across the student life cycle is harder.
Collecting data on a high-thriving subset of students would elicit a better definition of success and provide more actionable data for institutions to precisely improve success outcomes for all students.
In 2013, a team lead by D.C.-based Greenwald & Associates, which included experts from research universities and practitioners from a host of different digital and technological advances, developed a protocol to index student success at the individual level in real time, creating the College Optimizer Index (COI). The resulting definition incorporates research on self-efficacy and proven higher ed retention practices leading up to 2013, and adds a new concept of mutual evolution between a student and their learning ecosystem:
“Thriving: When a student experiences the maximum benefits from his or her specific college ecosystem and demonstrates this through heightened social and academic engagement, and a deeper sense of happiness.”
The COI includes 21 different variables that fall under the three broader dimensions of thriving in the definition: social, academic and personal happiness. Responses to a student questionnaire, with the Index embedded, makes it possible to assign students in any institution to a spectrum of thriving. Examples of variables included in the Index:
• Sense of belonging
• Acquiring concrete skills useful in real world
• Becoming comfortable debating ideas with others
• Developing as a person beyond academics
Actionable new data on current students
A critical benefit to using an index to operationalize the definition of thriving is that it measures what is happening in real time, not historically. Combining the COI with a questionnaire tailored to the institution’s ecosystem can reveal which practices, resources, social and academic settings, etc., are driving thriving at the individual level. Instead of only being able to flag risk early on, it becomes possible to flag and scale which practices are working, and to personalize those practices for dozens of characteristics per student.
One example of a correlation: One student population at the center of urgent national success initiatives is young male African Americans. The data from repeated national studies utilizing the COI indicates that these young men are more likely to thrive and persist if they are made aware early and often of how their input and feedback will be welcomed and applied to institutional decisions. The thriving datasets include millions of correlations of this nature, which are constantly updated.
Who gets the data?
The data is helpful to both institutions and students, and should be shared on a constant basis. Students should be rewarded with data early and often as they make discrete decisions or act in specific ways along their educational journey, which can improve their overall outcomes and chance of success.
This hypothesis was tested on tens of thousands of students across the US in a variety of higher education segments from 2015-2017. According to the data, probabilities of thriving for socio-demographically defined sub-groups, most often associated with risk using methods referenced above, are much higher when the data is derived from success, not failure.
Why? Because patterns and correlations for what drives success can be very distinct, prompting a more adaptive approach to student communications, and the “labels” of risk can be replaced with affirmative labels and higher expectations of success across the whole student population.
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