Page 42 - Campus Technology, October/November 2019

42 CAMPUS TECHNOLOGY | Oct/Nov 2019 2019 Marist College is not new to learning analytics. In 2011, the Poughkeepsie, NY, liberal arts college participated in the Open Academic Analytics initiative as part of the Educause Next Generation Learning Challenges (NGLC) grant funded by the Bill and Melinda Gates Foundation. In the years since, the college has continued to build on its efforts to create an open source academic early alert system. In 2018, Marist’s Data Science and Analytics Department rolled out a tool offering a dashboard view of academic risk that faculty members can access through the institution’s Sakai-based learning management system (LMS). Dubbed the Marist Universal Student Experience (MUSE), the system provides a proactive rather than a retrospective or reactive mechanism to improve the chances of student success. Marist has done quite a bit of research into Category: Student Systems and Services Institution: Marist College Project: Marist Universal Student Experience (MUSE) Project lead: Edward M. Presutti, assistant director of data science and analytics Tech lineup: Apereo, IBM, MariaDB, Red Hat, RStudio what impacts a student’s success in a specific course, noted Edward Presutti, assistant director of Data Science and Analytics. “Whereas many efforts in higher education address generalized student success across the whole curriculum, this is more focused on how successful they are being in a specific class. The challenge is how to take whatever data you have and convert that into a metric for what a student’s effort might be.” How It Works By clicking on the tool icon for MUSE in the LMS, faculty members can bring up a dashboard of their class. They see a visualization of the students in their class and each student’s corresponding risk status. They can view more detail on an individual student’s activity and/or select an option to e-mail the student with their concerns, along with resources that are available to the student. Presutti emphasized that the design had to be clean and simple. “When we launched this last fall, we wanted to make sure it was not adding to the instructors’ burden and it certainly hasn’t. We wanted to provide them something that is optionable for them to begin with, and secondly is a one-click icon that brings up a view of their classroom and their students instantly.” Professors are now able to clearly see student events as they relate to LMS use. Some professors are using the system to help determine participation metrics and refine their grading schemes regarding class participation. Some early adopters have been very proactive about using the tool, Presutti said. “We did a very small study in the spring that showed that an intervention of any kind — even just notifying the student that they may be at risk — has an impact on their final grade, with some significant performance increases.” Besides studying classroom activities in the LMS, the predictive risk model also pulls other data from the student information system.