Constructing Insights on Learning Analytic Student Activity Data from an Online Undergraduate Construction Management Course

Paige West; Frederick Paige; Natasha B. Watts; Walter C. Lee

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Conference: 2021 ASEE Virtual Annual Conference Content Access
Location: Virtual Conference
Publication Date: July 26, 2021
Start Date: July 26, 2021
End Date: July 19, 2022
Conference Session: Capitalizing on COVID: Using This Disruptor to Change the Educational Model
Tagged Division: Civil Engineering
Page Count: 19
DOI: 10.18260/1-2--36839
Permanent URL: https://peer.asee.org/36839
Download Count: 448

Paper Authors

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Paige West Virginia Polytechnic Institute and State University orcid.org/0000-0002-8712-0026

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Paige West is pursuing her Master's in Civil Engineering at Virginia Tech. She has a B.S. in Civil Engineering also from Virginia Tech. Her research focuses on the utility of learning analytic data in online engineering courses. Specifically, how instructors can leverage the data to improve engagement and encourage more interactions between the instructors, students, and content in their online courses.

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Frederick Paige Virginia Polytechnic Institute and State University orcid.org/0000-0002-3380-6444

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Dr. Frederick (“Freddy”) Paige is the Assistant Director of the Virginia Center for Housing Research and an Assistant Professor at Virginia Tech in the Vecellio Construction Engineering and Management Program. Dr. Paige’s main scholarship goal is to create the knowledge needed to develop an informed public that lives in a sustainable built environment. Previous work with a variety of utility companies, sustainability non-profits, and educational institutions has provided Dr. Paige with a versatile toolkit of knowledge and skills needed to address a diverse range of civil engineering issues. His main area of interest is high efficiency homes and sustainable communities. Dr. Paige completed his PhD in Civil Engineering at Clemson University, where he also received his MS and BS degrees in Civil Engineering.

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Natasha B. Watts Virginia Polytechnic Institute and State University

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As Director of Cardinal Education and the Associate Director of Online Learning in the College of Engineering at Virginia Tech, Natasha provides college-level leadership for the design, development, implementation, and evaluation of distance learning initiatives. Watts is the main point of contact for the Cardinal Education Program (formerly Commonwealth Graduate Engineering, CGEP).

Before coming to Virginia Tech, Natasha worked as an Assistant Professor and Program Coordinator for Visual Communication and Computer Information at Hazard Community and Technical College. Watts began her career at Appalshop, a non-profit media arts center located in the coalfields of Eastern Kentucky, serving as a director, educator, filmmaker, and youth media trainer. For the last ten years, her work has focused on placed-based visual learning and distance learning methodologies to facilitate rural classroom equality. Watts is passionate about distance learning, accessibility, and Appalachia. She believes there is a classroom for everyone.

Natasha has a Bachelor’s Degree in Broadcasting and Electronic Media, with a minor in Appalachian Studies from Eastern Kentucky University. A Master’s of Science in Education with an emphasis on occupational training and development from Eastern Kentucky University, and a Doctorate in Educational Technology and Leadership from Morehead State University.

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Walter C. Lee Virginia Polytechnic Institute and State University orcid.org/0000-0001-5082-1411

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Dr. Walter Lee is an associate professor in the Department of Engineering Education and the assistant director for research in the Center for the Enhancement of Engineering Diversity (CEED), both at Virginia Tech. His research interests include co-curricular support, student success and retention, and diversity. Lee received his Ph.D in engineering education from Virginia Tech, his M.S. in industrial & systems engineering from Virginia Tech, and his B.S. in industrial engineering from Clemson University.

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Abstract

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.

Citation
Format

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

TY - CPAPER
AB - 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.

AU - Paige West
AU - Frederick Paige
AU - Natasha B. Watts
AU - Walter C. Lee
CY - Virtual Conference
DA - 2021/07/26
PB - ASEE Conferences
TI - Constructing Insights on Learning Analytic Student Activity Data from an Online Undergraduate Construction Management Course
UR - https://peer.asee.org/36839
DO - 10.18260/1-2--36839
ER -