This project aims to study the gaps that arise in understanding and supporting self-regulated learning in the newly emerging online learning space, as demonstrated by the varied behaviors of different populations of students. In particular, we will address questions of how learning management system traces indicate successful self-regulated learning in semi-synchronous online programs when compared to traditional on-campus learning. some research has shown differences in digital artifact interaction behaviors for distance learning students when compared to traditional classroom cohorts, which indicates that different course structures could improve outcomes for these different groups. In this project, I would like to focus on how video and quiz interaction behaviors can demonstrate spaced vs. massed practice techniques.
You, as the DLA student, will be taking a lead role in extracting and analyzing large sets of student data. This will involve learning both data management and data analytics skills at the direction of the project manager (David Quigley). There will be ample opportunity to develop and evaluate your own theories of what may be occurring.
DLA student must have completed an introductory computing course (CSCI 1300, CSCI 1310, ECEN 1030, or similar). Student must be able to regularly attend (in person or remotely) weekly project meetings (to be scheduled). Student must be able to work independently and be willing to ask for support as needed.
Upper level data science computing experience (e.g. CSCI 3022) preferred. Experience or interest in psychology, education / learning sciences, and related fields is preferred.
David Quigley (faculty)