Data is present everywhere. From the moment you wake up to check your phone to what you ask Alexa or Siri. In the U.S alone, households consumed an average of 268.7 gigabytes (GB) of data a month. During the school day, 78 percent of students use technology devices, according to eSchool News. Universities can harness this data in new and unique ways. It can help campuses develop capabilities and systems to serve students with personalized messages and support. Additionally, with predictive analytics, it can also lead to addressing factors that can help increase the chances of student success. Yet with all this data, campuses need to know how to use this data in a way that promotes effective and efficient practices, while addressing the needs of the students’ experiences and security as their starting point. In this article, we’ll go over best practices that can help universities enable student success and serve a diverse population.
Predictive Analytics
Many higher ed institutions are seeing the benefit of analyzing student data to improve the quality of services they offer. Analyzing past student data to predict what current and prospective students might do has given higher ed institutions more targeted recruiting and use of institutional aid. InAnalytics in Higher Education: Establishing a Common Language, Hawkins, and Watson caution that, “analytics is not a one-size-fits-all endeavor and that one has to consider that analytics is a goal-directed practice.”Ideally, vendors can facilitate the ethical use of data all of the way through the student life cycle. Vendors can help ensure that data is complete and integrated correctly to diminish the chances of misidentifying students. They can be transparent about their algorithms and test them for disparate impact on student populations. They can be flexible with permissions and use reasonable security protocols to help preserve student privacy and security. They can train staff on the correct interpretation of data and on the dangers of implicit bias.
