June 15, 2019
June 15, 2019
June 19, 2019
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. https://peer.asee.org/32002
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