Presenter:
Peter Holub
Student engagement with course content is essential to the educational experience (Garrison, Anderson, & Archer, 2000). According to Scheffel et al. (2014), instructors should be aware of how their students interact with course content, learning materials and resources provided in their online classes.
The Course Analytics tool available in the Canvas Learning Management System provides information on student interaction with online course content. Instructors who use Course Analytics have access to student interaction with course resources and participation metrics across all devices, including Canvas mobile apps (Canvas, 2022). Course analytics not only provide the instructor an opportunity to monitor student interaction with course content, but to also identify at-risk students and enhance student engagement (Naujokaitiene’ et al., 2020).
The current literature is mixed on the impact of learning analytics on student success. Some researchers report strong correlations between learner interaction with course content and academic performance (Gomez-Aguilar et al., 2015; Ellis, Han & Pardo, 2017; Zacharis, 2015). Other researchers report a lack of empirical evidence for predicting student performance using learning analytics (Agudo-Peregrina et al., 2014; Iglesias-Pradas et al., 2015; Strang, 2015; Xing et al. , 2015). However, those who reported weak correlations admit the use of analytics warrant further investigation, including the need to assess student familiarity with online course technology, class size, and other “interesting patterns hidden in educational data sets” (Chatti et al. 2012, p.10).
A study funded by an NSU HPD Educational Research Grant was conducted in hopes to extend the current research by measuring the correlation between a student’s overall course grade and Canvas’ Course Analytics data of student interaction with online course materials. Anonymized Course Analytics data from Canvas courses taught by a single professor in the Dr. Pallavi Patel School of Health Sciences Department of Health Science at Nova Southeastern University from 2017 – 2022 was collected and analyzed (n = 400). Descriptive statistics, correlation, regression techniques, ANOVA and cluster analysis techniques was used to explore possible correlations between student performance and engagement within and between bachelor's, master's and doctoral level courses, class size, semester and year offered.
The presentation will share results of that study, with a focus on adding to the online instructors’ understanding of how the Canvas Course Analytics tool may be used to a). monitor student interaction with course content and b). reimagine teaching practices and curriculum development.
