This is a guest post by Andy Miller, Director of Academic Advising & Retention, Concordia University Wisconsin
The rising trend in the use of data analytics for student success is a rather interesting phenomenon, especially as we consider the skillsets necessary for mastering each domain. On one hand, we have the ‘thinkers’ who objectively review the empirical representation of students, while on the other are the ‘feelers’ who embrace the individual experiences of each student. Does improvement in one domain require the subjugation of the other? As we (student success professionals) begin viewing students through the lens of big data, do we risk treating students as just a number?
I talked about this in my recent presentation at the Blackboard Analytics Symposium
At Concordia University Wisconsin, specifically the Academic Advising Office, we have begun using data to better inform our practice. To get here has taken a fair amount of trial and error. With Blackboard Intelligence, you have the double-edged sword of limitless possibilities. My initial presentation of Blackboard’s analytics platform to my staff was like Bobby Flay opening a pantry of raw ingredients and asking the customer what they wanted for lunch: all the ingredients were present to make some magical dishes, but because they were not ‘packaged’ in any tangible, recognizable format, the consumers were a bit overwhelmed.
With this in mind, I looked at what ‘out of the box’ reports the Student Management module for Blackboard Intelligence already had. One element that struck a chord was the ‘risk indicators’ element (pic 1. below). By identifying risk factors prior to the start of the semester, we could proactively serve our advisees and hopefully mitigate some of the challenges they would likely face. For the past 3 semesters, we have taken a developmental approach to these conversations, subtly inquiring about the risk indicators and helping each student identify solutions to those challenges.
Not surprisingly, some students are more reticent, while others boldly refuse our help. For those who neglect to respond, after multiple outreach attempts, we refer them to our Falcon Academic Support Team (FAST). This is an academic equivalent of our behavioral intervention team. FAST is comprised of the professional advisors, our Assistant Vice President of Academics, Dean of Students, Athletics Advisor, Learning Resource Center representative and myself.
Students are referred to FAST by either a faculty academic referral form or a professional advisor.
The referral is often provoked by a student’s academic behavior, typically excessive absences or poor performance. When their name is brought up at FAST, we look at the student holistically: is this behavior isolated to one class? Is it systemic across their academic history? To what extent are they engaged socially? Prior to this, we would only have one part of a story. By gathering information on familial challenges, conduct issues, or non-engagement with Disability Support, we begin to unveil a more robust picture of the myriad factors impacting each students’ educational experience. This qualitative information is augmented by our FAST Dashboard, an amalgamation of some basic Blackboard Intelligence reports packaged in an easy-to-digest format (picture 2, below).
By having the ‘full pantry of ingredients’ we were able to craft a custom dashboard, ultimately enhancing our effectiveness. The other report we use frequently in FAST is the attendance report through the SQL server (figure 3 below).
One of the unintended benefits of this report, in particular, is the way in which our monitoring of student attendance has not only influenced student behavior but has also impacted faculty behavior. As they begin to hear that this information is being used to enhance student engagement and student success, they have assured us they will make more intentional efforts to keep their attendance current each week. By and large, they have! By shoring up many of the inconsistencies in recording data (i.e., taking and recording attendance) we can begin to more intentionally analyze attendance as a potential risk factor.
What I find most exciting about this, is we can begin to identify risk based on behavior, not just demographic characteristics or “high stakes” outcomes like grades. These ‘just in time data’ are continually enhancing our delivery of individualized student support. In short, we have married the quantitative with the qualitative to enhance the student experience, using numbers to ensure our students are not treated as one.
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