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

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...

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Main Authors:	K., Kadirgama, M. M., Noor, M. S. M., Sani, M. M., Rahman, M. R. M., Rejab
Format:	Conference or Workshop Item
Language:	English
Published:	2009
Subjects:	LB2300 Higher Education QD Chemistry
Online Access:	http://umpir.ump.edu.my/id/eprint/1424/ http://umpir.ump.edu.my/id/eprint/1424/1/2009_P_IEEC09_K.Kadirgama_M.M.Noor-Conference-.pdf

id	ump-1424
recordtype	eprints
spelling	ump-14242018-01-23T04:24:00Z http://umpir.ump.edu.my/id/eprint/1424/ Development of Genetic Algorithms and Multilayer Perceptron Neural Network (Mpnn) Model To Study The Student Performance InThermodynamics K., Kadirgama M. M., Noor M. S. M., Sani M. M., Rahman M. R. M., Rejab LB2300 Higher Education QD Chemistry 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. 2009 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/1424/1/2009_P_IEEC09_K.Kadirgama_M.M.Noor-Conference-.pdf 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)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	LB2300 Higher Education QD Chemistry
spellingShingle	LB2300 Higher Education QD Chemistry K., Kadirgama M. M., Noor M. S. M., Sani M. M., Rahman M. R. M., Rejab Development of Genetic Algorithms and Multilayer Perceptron Neural Network (Mpnn) Model To Study The Student Performance InThermodynamics
description	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.
format	Conference or Workshop Item
author	K., Kadirgama M. M., Noor M. S. M., Sani M. M., Rahman M. R. M., Rejab
author_facet	K., Kadirgama M. M., Noor M. S. M., Sani M. M., Rahman M. R. M., Rejab
author_sort	K., Kadirgama
title	Development of Genetic Algorithms and Multilayer Perceptron Neural Network (Mpnn) Model To Study The Student Performance InThermodynamics
title_short	Development of Genetic Algorithms and Multilayer Perceptron Neural Network (Mpnn) Model To Study The Student Performance InThermodynamics
title_full	Development of Genetic Algorithms and Multilayer Perceptron Neural Network (Mpnn) Model To Study The Student Performance InThermodynamics
title_fullStr	Development of Genetic Algorithms and Multilayer Perceptron Neural Network (Mpnn) Model To Study The Student Performance InThermodynamics
title_full_unstemmed	Development of Genetic Algorithms and Multilayer Perceptron Neural Network (Mpnn) Model To Study The Student Performance InThermodynamics
title_sort	development of genetic algorithms and multilayer perceptron neural network (mpnn) model to study the student performance inthermodynamics
publishDate	2009
url	http://umpir.ump.edu.my/id/eprint/1424/ http://umpir.ump.edu.my/id/eprint/1424/1/2009_P_IEEC09_K.Kadirgama_M.M.Noor-Conference-.pdf
first_indexed	2023-09-18T21:54:33Z
last_indexed	2023-09-18T21:54:33Z
_version_	1777413991815970816

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

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