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. / Master of Science / In an online course, students click on lecture pages to watch lecture videos, they use discussion boards to post and reply to their peers, and they visit their courses at whatever time suits them. These interactions are difficult for an instructor to identify. Therefore, making it harder for them to engage with the students, determine which students are at-risk for failing, or develop their courses based on the students' interactions. This research study leverages learning analytics to identify student interactions in an online construction management course to improve academic decision-making and course design. Learning analytics are interaction data collected from a course that includes every student's interaction with the course material (e.g., page clicks, discussion posts and replies). Additionally, surveys were distributed to each of the three online construction management course sections used in this study to gather the students' thoughts about the available learning methods (e.g., video lectures, lecture slides). The learning analytics results showed that student interaction fluctuates by the hour and lessens after the midterm exam. The survey results found watching the lecture videos and reading the lecture slides were the most helpful learning methods. The capabilities of learning analytics must be addressed by online stakeholders when developing future online courses. The growth of online learning is inevitable, and the results of this paper suggest that learning analytics can identify unnoticed student interaction patterns and influence future online course design.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/112386 |
| Date | 14 May 2021 |
| Creators | West, Paige Meredith |
| Contributors | Civil and Environmental Engineering, Paige, Frederick Eugene, Lee, Walter Curtis, Watts, Natasha Brooke, Scales, Glenda R. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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