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. 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)(2012)(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.c American Society for Engineering Education, 2017
DEVELOPMENT OF AN EARLY ALERT SYSTEM TO PREDICT STUDENTS AT RISK OF FAILING BASED ON THEIR EARLY COURSE ACTIVITIES AbstractThe emphasis on increasing student retention and graduation rates at institutions of higher education is driving the need for creation and implementation of early alert systems. Such early alert systems could be used in identifying students in academic trouble before 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 identify 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 parameters. Data collected in large sections of Mechanics of Materials course over four semesters were employed 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 (R 2 =0.69). Feasibility of using the model was also validated using over 100 additional data points, which were randomly selected from the initial dataset. Goo...
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