July 26, 2021
July 26, 2021
July 19, 2022
Learning analytics can optimize online instruction; however, the cryptic data from the Learning Management System (LMS) platforms inhibit instructors from improving their courses. Despite the cryptic data, instructors can tailor courses to incorporate more personalized learning using student activity data. This study explores how instructors can leverage learning analytics to create successful online experiences. Specifically, the researchers investigated the types of data Canvas learning analytics provides on student interaction with online content to identify instructional improvement areas.
The researchers examined student activity in the LMS using a case study of an online construction management course taught at a large Mid-Atlantic university. The researchers collected data from the online course during three semesters (Spring 2020, Summer 2020, and Fall 2020). The data collection goal was to observe the students’ activity fluctuations and learn about their interactions with the content pages. Online courses allow students to learn with reduced face-to-face interactions. Learning analytics can help overcome the challenge of not being able to monitor student activity in the classroom.
To analyze the students’ interactions, the researchers explored the course’s entire student activity records by time. Data was analyzed through graphical analyses and student network analyses. One significant finding was that the popular times to engage with the course were 2 PM, 3 PM, and 4 PM. Additionally, the students were most active in posting on the discussion boards around 8 PM and replying to their peers’ posts around 1 PM. Based on the research finding, an instructor could optimize their instruction and engage with their students at the popular course times of 2 PM – 4 PM.
Another application of learning analytics is exploring team dynamics. The researchers examined the effect of students’ activity levels (e.g., low, medium, high) on the team’s performance. The activity levels were separated into three equal groups (e.g., high activity = top 33% of activity). While we haven’t seen a strong correlation, we have discovered associations between individual student activity and the team’s overall performance. As a result of our analysis, in the Spring 2020 course, the team who received the highest group project grade was the only team with two high activity level students. Additionally, in the Summer 2020 course, the team with the lowest group project grade was the team with the largest number of low activity level students. Overall, there is a potential that dividing students into teams based on activity levels could improve team performance outcomes in an online course.
Understanding student online learning and team dynamics is more important than ever during the current pandemic. Learning analytics can help instructors ensure their students understand, engage, and participate in the online course material. Additionally, due to globalization and the popularity of e-learning, this research applies to the development of online courses outside of higher education. Given the rapid transformation from in-person to virtual learning, it is vital for researchers to continue exploring the utility of learning analytics and their implementation into the civil engineering field.
West, P., & Paige, F., & Watts, N. B., & Lee, W. C. (2021, July), Constructing Insights on Learning Analytic Student Activity Data from an Online Undergraduate Construction Management Course Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. 10.18260/1-2--36839
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