A neural network students' performance prediction model (NNSPPM)

• الحاوية / القاعدة: 2013 IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2013
• المؤلف الرئيسي: 2-s2.0-84894188408
• التنسيق: Conference paper
• اللغة: English
• منشور في: 2013
• الوصول للمادة أونلاين: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84894188408&doi=10.1109%2fICSIMA.2013.6717966&partnerID=40&md5=20ad4ef647d924178c64b769d14b2a1a

In the academic industry, students' early performance prediction is important to academic communities so that strategic intervention can be planned before students reach the final semester. This paper presents a study on Artificial Neural Network (ANN) model development in predicting academic performance of engineering students. Cumulative Grade Point Average (CGPA) was used to measure the academic achievement at semester eight. The study was conducted at the Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), Malaysia. Students' results for the fundamental subjects in the first semester were used as independent variables or input predictor variables while CGPA at semester eight was used as the output or the dependent variable. The study was done for two different entry points namely Matriculation and Diploma intakes. Performances of the models were measured using the coefficient of Correlation R and Mean Square Error (MSE). The outcomes from the study showed that fundamental subjects at semester one and three have strong influence in the final CGPA upon graduation. © 2013 IEEE.