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Identifying Course Trajectories of High Achieving Engineering Students through Data Analytics

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Conference

2016 ASEE Annual Conference & Exposition

Location

New Orleans, Louisiana

Publication Date

June 26, 2016

Start Date

June 26, 2016

End Date

August 28, 2016

ISBN

978-0-692-68565-5

ISSN

2153-5965

Conference Session

NSF Grantees Poster Session II

Tagged Topic

NSF Grantees Poster Session

Page Count

11

DOI

10.18260/p.25519

Permanent URL

https://peer.asee.org/25519

Download Count

144

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Paper Authors

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Omaima Almatrafi George Mason University

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Aditya Johri George Mason University Orcid 16x16 orcid.org/0000-0001-9018-7574

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Aditya Johri is Associate Professor in the Information Sciences & Technology Department. Dr. Johri studies the use of information and communication technologies (ICT) for learning and knowledge sharing, with a focus on cognition in informal environments. He also examine the role of ICT in supporting distributed work among globally dispersed workers and in furthering social development in emerging economies. He received the U.S. National Science Foundation’s Early Career Award in 2009. He is co-editor of the Cambridge Handbook of Engineering Education Research (CHEER) published by Cambridge University Press, New York, NY. Dr. Johri earned his Ph.D. in Learning Sciences and Technology Design at Stanford University and a B.Eng. in Mechanical Engineering at Delhi College of Engineering.

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Huzefa Rangwala George Mason University

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Jaime Lester George Mason University

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Abstract

In this paper we present findings from a study that compares course trajectories of students who performed well academically and graduated in four years and with those of low achieving student. The goal of this research is to identify factors related to course-taking choices and degree planning that can affect students’ academic performance. The data for the study was collected from three majors within an engineering school at a large public university: civil, environmental, and infrastructure engineering (CEIE), computer science (CS), and information technology (INFT). The data includes more than 13,500 records of 360 students. Analysis shows that low performers postponed some courses until the latter end of their program, which delayed consequence courses and their graduation. We also found that low performers enrolled in multiple courses together at the same semester that their counterparts do not usually take concurrently. The methods used in this paper, frequent pattern mining and visualization, help uncover student pathways and trajectories with direct impact for advising prospective and current students. The findings can also be used to improve engineering programs’ curriculum.

Almatrafi, O., & Johri, A., & Rangwala, H., & Lester, J. (2016, June), Identifying Course Trajectories of High Achieving Engineering Students through Data Analytics Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.25519

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