NDLI: Prediction of engineering students' academic performance using Artificial Neural Network and Linear Regression: A comparison

Content Provider	IEEE Xplore Digital Library
Author	Arsad, P.M. Buniyamin, N. Manan, J.-L.A.
Copyright Year	2013
Description	Author affiliation: Fac. of Electr. Eng., Univ. Teknol. Mara, Shah Alam, Malaysia (Arsad, P.M.; Buniyamin, N.) \|\| Mimos Malaysia Berhad, Technology Park, Malaysia (Manan, J.-L.A.)
Abstract	Predicting students' performance is very important if not crucial especially in engineering courses. This is to enable strategic intervention to be carried out before the students reach the higher semesters including the final semester before graduation. This paper presents a comparison study between Artificial Neural Network (ANN) and Linear Regression (LR) in predicting the academic performance. 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' fundamental subjects results at first semester were used as independent variables or input predictor variables while CGPA in the final semester that is at semester 8 is used as the output or the dependent variable. Performances of the models were measured using the coefficient of Correlation R and that of Mean Square Error (MSE). The outcomes of the study from both models indicate a strong correlation between fundamental results for core subjects at semester one or semester three with the final CGPA.
Starting Page	43
Ending Page	48
File Size	454075
Page Count	6
File Format	PDF
e-ISBN	9781479923328
DOI	10.1109/ICEED.2013.6908300
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-04
Publisher Place	Malaysia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electrical engineering ANN Correlation Conferences Linear regression Government LR Artificial neural networks Prediction Academic performance Engineering fundamentals Analysis of variance
Content Type	Text
Resource Type	Article

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