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STUDENT SUCCESS terry mills Developing Better Interventions for At-Risk Students A million-dollar grant is helping John Carroll University fine-tune a targeted intervention and early alert system that helps boost student learning and retention. IN 2015, the federal government awarded $60 million to 18 colleges and universities, chosen from a pool of hundreds, to develop innovative approaches for supporting at-risk students. John Carroll University, a four-year liberal arts institution in Ohio with around 3,000 students, was among those chosen, receiving a $1.3 million “First in the World” grant from the U.S. Department of Education. The question at the heart of our project was simple: What processes and procedures could we put in place to improve the first-year expe- rience at John Carroll — an experience inher- ently linked to broader goals like student learn- ing and retention? Coming down the homestretch of the four- year grant, we’ve learned a lot about devel- oping and testing a new first-year experience model. In particular, we’ve discovered a great deal about the value of multidimensional data visualizations for better predicting which students may be the most at risk. Linked Learning for At-Risk Students At the time we were developing the First in the World project, John Carroll was already implementing a new integrative core curricu- lum for its students, including linked courses, also known as student co-enrollment. But stu- dents couldn’t co-enroll until sophomore year. We decided to build on this approach for incoming students. Instead of putting higher- risk first-year students into developmental or remedial courses, we put them directly into integrated learning communities, a concept that had shown promise at the secondary school level. Linked courses were one aspect of this: One course was typically content- based, like science or math, while the other was an application course, like writing or speech. We also hosted faculty development workshops, held discussions from guest speakers and experts, and offered service learning and advanced student advising. Overall, we hoped the communities would help students both stay in school and achieve improved academic outcomes. One way to test this approach would have been to conduct a randomized control trial, but the trouble with that methodology is that many students who needed help wouldn’t get it. We didn’t want to select a random group of students; we wanted to proactively identify students who were at-risk. Regression Discontinuity We opted for an approach called regression discontinuity. We relied on the College Stu- dent Inventory survey, which uses non-cog- nitive indicators like academic motivation 28 CAMPUS TECHNOLOGY | Oct/Nov 2019 

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