Page 28 - Campus Technology, January/February 2020
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also be done because it is the right thing to do! Most universities understand the requirements around captioning, which have been around for a long time. However, many folks do not under- stand audio descriptions, and 2020 will be the year that educational technologists need to dig in and learn about accessibility and all the intrica- cies that are associated with not only video but also the entire array of accessible instructional
Perez: Analytics are no longer nice to have but a must-have. Institutions that are not using ana- lytics that are learner-centric will be missing out. Data needs to be used from the student information system, the learning management system and any other third-party integrations to tell us the full story of what the students are or are not doing. We need to have data to make in- formed decisions. Without good data, we can’t create models to look for predictive analytics. And we need predictive analytics to help retain students and help them graduate in a timely manner. Ed tech data is the holy grail in terms of helping students persist and graduate; we need to use the data as much as possible to help stu- dents succeed.
Frazee: Building upon increasingly accurate CAMPUS TECHNOLOGY | Jan/Feb 2020
intelligence generated from students’ learning management system and student information system data — with random forest, latent class analysis and other machine learning models con- tinually yielding promising outcomes and com- pelling results — 2020 will mark the year that higher education learning analytics moves into the plateau of productivity.
Burns: In 2020, it’ll no longer be a question of whether an institution is considering the use of predictive analytics to inform advising. Instead, questions will be about how data is being lever- aged to transform your campus culture for the better. How an institution uses data determines their success. In 2020, every institution should know the top 10 indicators that a student is at risk for dropping out. If we know those indica- tors, we can begin to redesign our processes and intervene before it’s too late.
We now know that campus decisions around the implementation of predictive analytics are more important than the vendor/platform itself. I hope we get to a place where campuses aren’t blaming platforms for failing, and instead they un- derstand that internal decisions about who leads, what information they have, how much institu- tional support they get, and the change manage- ment approach they utilize have way more to do with your likelihood of success than if you choose A, B, or C vendor.

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