Development of Genetic Algorithms and Multilayer Perceptron Neural Network (Mpnn) Model To Study The Student Performance InThermodynamics

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K., Kadirgama and M. M., Noor and M. S. M., Sani and M. M., Rahman and M. R. M., Rejab (2009) Development of Genetic Algorithms and Multilayer Perceptron Neural Network (Mpnn) Model To Study The Student Performance InThermodynamics. In: International Engineering Education Conference, 16-18 May 2009 , Madinah, Kingdom of Saudi Arabia. . (Unpublished)



Student performance is very crucial to any educational institution. The neural network and genetic algorithms (GA) method were used to measure student performance in Thermodynamic at Faculty of Mechanical Engineering, University Malaysia Pahang (UMP). Randomly 65 mechanical engineering students with two different cohorts were picked to analysis their performance in these subjects with 5 variables which are Test 1, Test 2, Assignments, Final Examination and Quizzes. The analysis was done to measure the student performance in Thermodynamic I which final grade was used as the tools. The models show that Test 1 and Test 2 plays major role in the student final grade. Meanwhile assignments and quizzes play as a booster to their performances. Those who performance well in their testes, will maintain the momentum in their final. It’s proven that the early of the syllabus as fundamental knowledge must be strong, if the students want to do well in Thermodynamic I. The artificial intelligent model can be used for further investigate of the subject performance with include more predictor such as age, CGPA, gender and etc.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Dr. Kumaran Kadirgama (K.Kadirgama) Muhamad Mat Noor (M.M.Noor) Mohd Shahrir Bin Mohd Sani (M.S.M.Sani) Profesor Dr. Md. Mustafizur Rahman (M.M.Rahman) Dr. Mohd Ruzaimi Mat Rejab (M.R.M.Rejab)
Uncontrolled Keywords:Thermodynamics I, Statistical, Genetic Algorithms, Test
Subjects:L Education > LB Theory and practice of education > LB2300 Higher Education
Q Science > QD Chemistry
Divisions:Faculty of Mechanical Engineering
ID Code:1424
Deposited By: Zairi Ibrahim
Deposited On:25 Jul 2011 08:55
Last Modified:23 Jan 2018 12:24

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