AAA Spark Meeting of Regions

Using Data Analytics to Identify and Serve at-risk and Underrepresented Students

• In Event: Effective Learning Strategies Poster Session B
  In Poster Session: Poster Sessions B: Room 3

Abstract

The ongoing Pandemic entails heavy reliance on online teaching which has presented challenges as well as opportunities. One opportunity is rich data analytics readily available at learning platforms, media space, and on zoom. With a few clicks, you have each student’s percentage completion data on reading, watching e-lectures, guided examples, in addition to time spent on each homework and homework breakdown scores. Based on data analytics, an instructor or a teaching assistant emails a reminder to students who fall behind and offers individual help on zoom in a timely manner. These individual help sessions give the educator a chance to know more about a student’s life challenges so he or she can offer advice, encouragement, or refer students to professional consultation services. Another set of data is a learning/cognitive style survey that report four dimensions of student’ learning styles including sequential versus global cognitive processing style. For students with strong global cognitive styles, following a sequential strategy may be out of their comfort zone and hence need more help. In addition, course assessment should be well-balanced that requires both type of information processing. Targeted efforts based on data analytics have been effective in improving at-risk and underrepresented students’ academic performance.

Author

• Li Zhang, University of Illinois at Urbana Champaign