Leveraging Instructional Design to Measure Student Learning

The power of big data is being leveraged across higher education to make predictions on student success in a course, to allow students to get to degree completion more quickly, and even to measure student sentiment within discussion forums. The term learning analytics is broadly applied to the use of data in this fashion. However, the question, “Is the student meeting the learning outcomes?” is a more difficult question to unpack given the complexities of the data needed. Higher education has an opportunity to richly define learning analytics moving forward, potentially answering the following questions. 1) How do we leverage data to provide students with access to improve their own paths to successful degree completion, while still allowing them the encounter meaningful, but potentially unplanned, learning experiences? 2) How can student demographic and performance data be tied to instructional materials to provide learners with the most effective learning objects? 3) How can instructional designers leverage learning data to influence iterations on course design? Higher education has an opportunity to define learning analytics beyond the number of clicks in the LMS. While this is one behavior that can impact student performance in a course, it is not a true measure of student learning. The future is ours to define, how will instructional designers empower this discussion?