There are ways that data can help, and not help, in the educational process. Reading interaction data on student performance versus learning outcomes is valuable, but what if data goes beyond reading? This session will highlight examples of institutional data use, platform categories, and machine learning versus human decisions.
Universities are seeing the potentialthat the vast quantities of dataproduced in their IT systems hold for addressing strategic challenges facing academia today. Data can be helpful in certain areas, but not necessarily as helpful in others. This session will examine a sampling of data insights from different kinds of data. This includes reading interaction data and student performance versus learning outcomes. We will cover data types that can be obtained and examples of actions taken from the data. VitalSource currently passes institutions a wide variety of data, and Acrobatiq by VitalSource expands the options for intervention strategies. The session will touch on machine learning as it intersects with human decisions.