Roman, Melea R.2022-05-052022-05-052022https://hdl.handle.net/2097/42203The push toward online learning in response to the Covid-19 pandemic has provided an opportunity to evaluate student behavior and outcomes in asynchronous and synchronous mathematics courses with an unprecedented population of students. In this study, data is collected from Canvas tracking student utilization of lecture videos. This data is analyzed under the lens of principal component analysis and partitioning around medoids to cluster students into behavioral groups. This data is then compared with grade outcomes to provide insight into possible behavioral best-practice for asynchronous and synchronous online learning in Elementary Differential Equations and History of Mathematics courses.en-US© the author. This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).http://rightsstatements.org/vocab/InC/1.0/Educational data miningOnline learningMathematics educationThesis