(464d) Data-Driven Continuous Improvement Strategies for Asynchronous Online Courses

The COVID-19 pandemic has affected ChE instruction in many ways, including the move of many courses to an asynchronous online format. Asynchronous online courses provide a unique highly-controlled arena for continuous improvement since course lectures, example solutions, etc. are pre-recorded; this means all students in a semester (and if no changes are made, in future semesters as well) receive identical course instruction and content. With this in mind a directed continuous improvement approach which assesses student performance on lecture-level learning objectives can be used to identify specific lectures or other course content which are weak or ineffective; such course content can then be critically reviewed and revised, and the impact of these revisions on student performance quantified. This talk will describe the application of this data-driven continuous improvement strategy for asynchronous online courses, including data collection, identification of weak course content, and results of application of the strategy.