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Development of an Early Alert System to Predict Students At Risk of Failing Based on Their Early Course Activities

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Conference

2017 ASEE Annual Conference & Exposition

Location

Columbus, Ohio

Publication Date

June 24, 2017

Start Date

June 24, 2017

End Date

June 28, 2017

Conference Session

Predicting Student Success

Tagged Division

Educational Research and Methods

Page Count

20

DOI

10.18260/1-2--28166

Permanent URL

https://peer.asee.org/28166

Download Count

345

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Paper Authors

biography

Seyedhamed Sadati Missouri University of Science & Technology

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Seyedhamed Sadati is a PhD candidate of Civil Engineering at Missouri University of Science and Technology. His expertise are in the field of concrete technology, with a focus on durability of reinforced concrete structures and optimization of sustainable concrete materials for transportation infrastructure. He has served as the co-instructor of the "Transportation Engineering" course for two years at the Department of Civil, Architectural, and Environmental Engineering at Missouri University of Science and Technology.

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biography

Nicolas Ali Libre Missouri University of Science & Technology

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Nicolas Ali Libre, PhD, is an assistant teaching professor of Civil Engineering in Missouri University of Science and Technology.He received his B.S. (2001), M.S. (2003) and Ph.D. (2009) in civil engineering with emphasis in structural engineering, all from the University of Tehran, Iran.

His research interests and experience are in the field of computational mechanics, applied mathematics and cement-based composite materials. During his post-doc in the Department of Mathematics at Hong Kong Baptist University (2010-2011) he focused on developing meshfree numerical methods. Given his multidisciplinary background, he was appointed as the director of research in the Construction Materials Institute (2011-2013) at the University of Tehran and assistant professor at Islamic Azad University. In that capacity, he had the opportunity of leading several industry-related research projects and mentoring graduate and undergraduate students.

Over the span of his career, Dr. Libre has authored and co-authored over 17 peer-reviewed journal articles and over 50 conference papers. He has advised and co-advised 7 graduate students and mentored over 20 undergraduate students. He has collaborated with scholars from several countries, including Iran, China, Slovenia, Canada, and the US. He also served as a reviewer for 6 journals and 5 conferences.

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Abstract

The emphasis on increasing student retention and graduation rates at institutions of higher education is driving the need for creation and implementation of early alert system. Such an early alert system could be used in identifying students in academic trouble before it results into failure. Early identification of students who are at a risk of dropping or failing a course will help instructors to adapt their course delivering techniques with student’s learning styles and improve overall performance of a class. This paper discusses an early alert system to predict students who are at risk of failure based on their activity at the beginning of semester. The proposed alert system considers various indicators, including the homework assignments and the mid-term exam corresponding to the first quarter, along with in-class participation as input values. Data collected in large sections of Mechanics of Materials course over four semesters were used for development and validation of the early alert system. The data analysis showed that the proposed model is capable of predicting the final scores of the students with an acceptable accuracy (R2=0.69). Feasibility of using the model was also validated using over 100 additional data points, which were randomly selected from the initial dataset. Good correlation was observed between the data and model predictions, with over 94% of the data points falling within the limits of a 90% confidence interval. The proposed model has possible implications in the similar engineering courses provided that the required data is collected during early semester activities. This tool enables the instructor to detect and reach out to the at-risk student and provide proactive assistance to students so that they are able to succeed in the course. Proactive assistance may include referrals to appropriate resources, providing tailored activities to improve the weakness of students and one-to-one academic skill building workshops.

Sadati, S., & Libre, N. A. (2017, June), Development of an Early Alert System to Predict Students At Risk of Failing Based on Their Early Course Activities Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--28166

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