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Effect of Class Absenteeism on Grade Performance: A Probabilistic Neural Net (PNN)-based GA-trained Model

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2012 ASEE Annual Conference & Exposition


San Antonio, Texas

Publication Date

June 10, 2012

Start Date

June 10, 2012

End Date

June 13, 2012



Conference Session

Understanding Our Students II

Tagged Division

Educational Research and Methods

Page Count


Page Numbers

25.498.1 - 25.498.9



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Paper Authors


Mohammed E. Haque P.E. Texas A&M University

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Mohammed E. Haque, Ph.D., P.E., is a professor of construction science at Texas A&M University at College Station, Texas. He has more than 20 years of professional engineering experience in analysis, design, and investigation of building, bridges, and tunnel structural projects for various city and state governments as well as private sector. Haque is a registered Professional Engineer in the states of New York, Pennsylvania, and Michigan, and a member of ASEE, ASCE, and ACI. Haque received a B.S.C.E. from Bangladesh University of Engineering and Technology, a M.S.C.E. and a Ph.D. in civil/structural engineering from New Jersey Institute of Technology, Newark, N.J. His research interests include fracture mechanics of engineering materials, composite materials and advanced construction materials, architectural/construction visualization and animation, computer applications in structural analysis and design, artificial neural network applications, knowledge based expert system developments, application based software developments, and buildings/infrastructure/bridges/tunnels. inspection and database management systems.

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Effect of Class Absenteeism on Grade Performance: A ProbabilisticNeural Net (PNN) based GA trained model AbstractMost faculty inherently believe that students who frequently miss class significantlyincrease their likelihood of poor grades by doing so. The purpose of this research was todevelop a Probabilistic Neural Net (PNN) based Genetic Algorithm to assess therelationship between absenteeism and student grade performance in a structural systemscourse taught by the author. The model was trained to classify the outcomes (pass/fail) of130 students using records of class attendance and end-of-course final grades. Therelative importance/weight of attendance on final grades was then determined. It wasfound that course attendance and grade performance were positively correlated. Themodel was then used to accurately predict the success rate of a new group of 80 studentsbased on provided attendance records. Overall, this research shows that the developedPNN based GA model can be used to predict the outcome of student performance in thestructural systems class based on anticipated class absence patterns.

Haque, M. E. (2012, June), Effect of Class Absenteeism on Grade Performance: A Probabilistic Neural Net (PNN)-based GA-trained Model Paper presented at 2012 ASEE Annual Conference & Exposition, San Antonio, Texas. 10.18260/1-2--21256

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