Predicting Mathematics Incompetence Effects on the Study of Digital Electronics Among Electrical and Electronic Engineering Students, using Artificial Neural Networks


  • Theodore Oduro-Okyireh, Budi Mulyanti, Dedi Rohendi


Achievement, Activation Function, ANN, Cross-Entropy Error, Digital Electronics, Hidden Layers, Hyperbolic Tangent Function, Mathematics Failure, Multilayer Perceptron Neural Network.


Through proficiency effect analysis, the research aims to identify key engineering mathematics domains that are essential for students to succeed in Digital Electronics course. This investigation employs an artificial neural network (ANN)-based predictive model and focuses on Ghanaian Technical Universities as a case study. The study adopted the quantitative research design where random cluster sampling was used to select a total of 488 final year Higher National Diploma students from four technical universities in Ghana. The data consisted of mathematics achievement test scores and results of their Digital Electronics course. After testing a number of artificial neural network (ANN) architectures, the most accurate model was a multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. The results showed, with high precision, that Functions and Algebra are two critical areas of mathematics that have the greatest impact on students’ performance in Digital Electronics in electrical and electronic engineering studies.


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How to Cite

Theodore Oduro-Okyireh. (2024). Predicting Mathematics Incompetence Effects on the Study of Digital Electronics Among Electrical and Electronic Engineering Students, using Artificial Neural Networks. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1836–1849. Retrieved from



Research Article