A Framework to Support Interdisciplinary Engagement with Learning Analytics

Learning analytics can provide an excellent opportunity for instructors to get an in-depth understanding of students’ learning experiences in a course. However, certain technological challenges, namely limited availability of learning analytics data because of learning management system restrictions, can make accessing this data seem impossible at some institutions. Furthermore, even in cases where instructors have access to a range of student data, there may not be organized efforts to support students across various courses and university experiences. In the current chapter, the authors discuss the issue of learning analytics access and ways to leverage learning analytics data between instructors, and in some cases administrators, to create interdisciplinary opportunities for comprehensive student support. The authors consider the implications of these interactions for students, instructors, and administrators. Additionally, the authors focus on some of the technological infrastructure issues involved with accessing learning analytics and discuss the opportunities available for faculty and staff to take a multi-pronged approach to addressing overall student success.https://scholarworks.wm.edu/educationbookchapters/1045/thumbnail.jp

Learning Analytics Research: Using Meta-Review to Inform Meta-Synthesis Authors

Research in learning analytics is proliferating as scholars continue to find better and more engaging ways to consider how data can help inform evidence-based decisions for learning and learning environments. With well over a thousand articles published in journals and conferences with respect to learning analytics, only a handful or articles exist that attempt to synthesize the research. Further, a meta-review of those articles reveals a lack of consistency in the scope of included studies, the confluence of educational data mining activities and “big data” as a parameter for inclusion, and the reporting of actual strategies and analytic methods used by the included studies. To fill these gaps within existing reviews of learning analytics research, this metasynthesis follows procedures outlined by Cooper to reveal developments of learning analytics research. The results include a number of metrics showing trends and types of learning analytic studies through 2017 that include which fields are publishing and to what extent, what methods and strategies are employed by these studies, and what domains remain largely yet unexplored