Understanding Learner-to-Learner, Learner-to-Instructor, and Learner-to-Content Networks to Support Student Retention in Science Courses
A new project funded as part of a pilot grant from the Unizin Data Consortium for $10,000 awarded to UNO led by the PI: Dr. Tracie Reding, UNO STEM Outreach Coordinator, and College of Education, Health, and Human Sciences, with support from coPIs: Christine Cutucache, PhD (Department of Biology, A&S), Chris Moore, PhD (Department of Physics, A&S), Neal Grandgenett, PhD (Department of Teacher Education, CEHHS), Jason Buzzell (Digital Learning), Casey Nugent (NU-Information Technology), Jyotsna Ramanan (NU-Information Technology).
The coronavirus pandemic has led to dramatic increases in online and blended learning environments, bringing new urgency to the need to better understand how to effectively support student learning in such environments. Additionally, the move to online education and the greater subsequent reliance on EdTech companies has increased privacy concerns. Moreover, it’s well-demonstrated in the literature the need to monitor student interaction within a course to identify potential warning signs of disengagement and likelihood of not completing the course. Therefore, this pilot project evaluates instructor-matched, course-matched student-engagement for courses both in-person and online (fall 2019 and spring 2020). Data for the project are available from the sponsor, via the Unizin Data Platform (UDP). The UDP allows Reding and team to apply social network analysis (SNA) to determine the types of course-specific interactions, using digital trace data, such as discussion board posts.
This information provides an algorithm for effective engagement and retention within the course, be that learner-to-learner, learner-to-instructor, and learner-to-content. The next iteration of this project will build a bona fide dashboard for instructors to monitor engagement and support students more comprehensively. Ultimately, the longer-term successful implementation will also result in the verification and refinement of a dissemination of innovations model within an undergraduate STEM context that can be replicated, modified, and used in various higher education contexts regarding innovation diffusion.