Understanding Student Interactions Through Learning Analytics from an Online Engineering Case Study Course

Student interactions in learning environments are vital for learning development. The growth of online learning in higher education has led stakeholders to question how to identify student interactions with course material and increase the quality and value of the learning experience. This research focused on leveraging existing learning analytics from the Canvas Learning Management System (LMS) to identify course interactions and make data-informed course design decisions. Learning analytics were collected from 113 students in three course sections of an online construction management course. Three surveys were also distributed to each course section to gather the students' perceptions of the learning methods and their interactions for assistance. An exploratory graphical analysis visually depicted student interactions in the online course through the students' hourly and weekly interaction levels, page visits, and discussion board activity. A paired t-test was used to statistically compare the survey responses on the students' perceptions of the learning methods. The learning analytics results showed the students' interaction levels peaked in the afternoon and evening hours, and their weekly interactions and page visits lessened after the midterm exam. Additionally, based on Pearson's correlation test, the discussion board interactions significantly correlated with student performance. Lastly, the surveys showed that students found watching the lecture videos and reading the lecture slides to be the most helpful methods when learning the course material. These results have important implications for online stakeholders as learning analytics and student perceptions can inform online course design to facilitate student, instructor, and content interactions.