Page 43 - Campus Technology, October/November 2019

Financial strain can impact student success, as do other signifiers of engagement. Are students participating in clubs or playing sports and how many sports? Do they have on-campus employment or work-study? Campus employment might indicate engagement, Presutti said, whereas work-study might indicate a financial need. Data Integration Although the dashboard is easy for faculty to use, setting up the process to pull all the data together on a nightly basis to do the analyses requires considerable work behind the scenes. Like most campuses, Marist has a diverse set of systems. “Although we run Banner, not everything is integrated,” Presutti said. “The enrollment management system is not 100 percent integrated with Banner. There are import/export utilities that we have to use with the LMS. The technical challenge is to understand all the diverse systems you have and come up with a mechanism so that you can effectively extract them and coordinate that into a good unit of analysis to use for training.” Because Marist has many student workers in data science, it chose to “roll its own” extract, transform and load (ETL) process using an IBM Netezza appliance. Data science at Marist is also an open source shop. “Our dashboard is now part of the Apereo open source community, as is the research and everything we have done here,” Presutti stressed. “We want to make sure everything we have done is open source, so our whole aggregation process runs on a \[Red Hat\] Linux box.” Other open source technologies in the mix include RStudio and MariaDB. Indeed, the original grant from the Gates Foundation in 2011 required that this learning analytics process would be open source and available to others, so that was a driving factor in how Marist structured its project. Presutti believes the focus on open source tools makes it easier to collaborate with other universities. For instance, through collaboration with North Carolina State University, a predictive model has already been implemented as part of that institution’s student success initiatives. Also through a collaboration with the Joint Information Systems Committee (JISC) in the United Kingdom, a number of institutions there have piloted the models. Although there are some tech hurdles to be overcome, one of the most challenging aspects of the effort is not technical at all. “It is getting buy-in from data custodians and getting the endorsement and support of senior management,” Presutti noted. Certain data you just will never get and you shouldn’t, he added. For instance, healthcare data is highly protected. But other key factors of student success are less obvious — like sports participation. “So when it comes to what we refer to as ethically sensitive data, we have to go through the process of deciding whether it is ethical. That is one of the challenges for any institution.” Next Steps So what is next for learning analytics at Marist? “The next step has to be retention and prescriptive analytics,” Presutti said. “We can identify students that are potentially at risk, but can we know which intervention steps are most beneficial to them? That is really the next direction. We are not there yet with an integrated intervention strategy. But that is where we are going.” Of course, there are already departments on campus that support the student population. “It is not the role of data science to tell them how to do things,” Presutti said. “Whatever we do with them, it can’t be a heavy lift for them. The same way we created the dashboard for faculty, it has to be something easy to use and that doesn’t add to their workload.” David Raths is a freelance writer based in Philadelphia. campustechnology.com 43