The Entry Qualifications and Gender Analysis of Student Performance by using Artificial Intelligent Method

M. M., Noor and K., Kadirgama and M. M., Rahman and M. R. M., Rejab and M. S. M., Sani and M. Y., Taib (2009) The Entry Qualifications and Gender Analysis of Student Performance by using Artificial Intelligent Method. In: Malaysian Technical Universities Conference on Engineering and Technology , 20-22 June 2009 , MS Garden Hotel, Kuantan, Pahang, Malaysia. . (Unpublished)


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Entry qualifications are very important for the educational institution or educational providers to ensure quality graduate been produced. This paper presents the influence of gender, entry qualification and entry results towards the student performance in university. Total of 65 students were randomly selected in faculty of mechanical engineering, University Malaysia Pahang. Entries qualifications are from Foundation Program, Higher Certificate of Malaysian Education (STPM) and Diploma Certificate. STPM is form six examinations in secondary school level. Multilayer Perceptron Neural Network (MPNN) method was used to measure and predict the student’s performance. Result from the study shows that gender not significant role but entry results plays important role. Good entry results student normally maintain their performance throughout the study and become excellent graduates. MPNN is an important tool to study the different type of variables for student performance.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Proceedings of MUCEET2009 Abu Bakar Rosli (A. B. Rosli) Muhamad Mat Noor (M. M. Noor) Dr. Kumaran Kadirgama (K. Kadirgama) Dr. Mohd Ruzaimi Mat Rejab (M. R. M. Rejab) Mohd Shahrir Bin Mohd Sani (M. S. M. Sani) Mohd Yusof Bin Taib (M. Y. Taib)
Uncontrolled Keywords: Multilayer Perceptron Neural Network (MPNN), gender, entry qualificatin
Subjects: L Education > LB Theory and practice of education > LB2300 Higher Education
Faculty/Division: Faculty of Mechanical Engineering
Depositing User: Mr. Zairi Ibrahim
Date Deposited: 25 Jul 2011 02:13
Last Modified: 23 Jan 2018 02:00
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