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A Study of Several Classification Algorithms to Predict Students’ Learning Performance

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2019 ASEE Annual Conference & Exposition


Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

October 19, 2019

Conference Session

ET Pedagogy I

Tagged Division

Engineering Technology

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Paper Authors


SriUdaya Damuluri George Mason University

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SriUdaya Damuluri is a graduate student in Data Analytics Engineering at George Mason University. Her research interests include big data, predictive analytics, and machine learning. She earned her bachelor’s degree from Jawaharlal Nehru Technological University.

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Pouyan Ahmadi George Mason University

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Pouyan Ahmadi is an Assistant Professor in the Department of Information Sciences and Technology.

His research interests include cooperative communications and networking, cross-layer design of wireless networks, relay deployment and selection in wireless networks. He has devised several relay-selection strategies in cooperative communication by combining routing and cooperative diversity with the consideration of a realistic channel model that can be applied to WLAN and LTE networks.

Dr. Ahmadi earned his Ph.D. in Electrical and Computer Engineering at George Mason University, M.S. in Architecture of Computer Systems at Iran University of Science and Technology, and B.S. in Computer Engineering at Azad University.

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Khondkar Islam George Mason University

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Khondkar Islam is an Associate Professor and Associate Chair for Undergraduate Studies in the Department of Information Sciences and Technology at George Mason University (GMU).

His research interests include distributed and peer-to-peer systems, overlay and wireless networks, network security, engineering education, and distance education for instructor training and student learning.

Dr. Islam earned his Ph.D. and B.S. at George Mason University, and M.S. at American University.

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Identifying students who need better pedagogical support is an invaluable asset for any academic institution. The main objective of this study is to predict the students’ performance and thereby maximize their learning productivity. We focus on the students’ past academic performance to predict their future results. This is done by analyzing the various factors of course material and students’ online behavior from the Learning Management System (LMS). We also analyze several predictors that contribute to the overall student performance from the data collected. To determine the efficient model that is more accurate and precise, we compare the performance of four well-known machine learning classification algorithms. The 2017 and 2018 academic year data collected consists of user patterns, navigational behavior and the students’ daily activities from the LMS, Blackboard (Bb) Learn of the Undergraduate IT program within the Information Sciences and Technology (IST) Department at George Mason University (GMU). This comparison effort will help us confirm the most effective algorithm to identify students’ who are at risk of failing a class so that academic advisors/instructors can offer better academic guidance and support.

Keywords: Classification algorithms, navigational behavior, performance prediction, Learning Management System.

Damuluri, S., & Ahmadi, P., & Islam, K. (2019, June), A Study of Several Classification Algorithms to Predict Students’ Learning Performance Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32002

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