A Hybrid Machine Learning Model for Grade Prediction in Online Engineering Education





machine learning, artificial neural network, AI, grade predictive modelling, CAD, COVID-19, online learning, hybrid model


Facing the disruption caused by COVID-19 pandemic, the emergence of imposed and exclusive online learning revealed challenges for researchers worldwide, as of reforming curricula shortly and of collecting data accumulated by monitoring stu-dents’ commitment and academic performance. With this pool of data, this research explores grade prediction in a first-semester mechanical engineering CAD module, after testing the performance of the reform of specific curricula. A hybrid model has been created, based on 35 variables having been filtered out of statistical analysis and shown to be strongly correlated to students’ academic performance in the specif-ic online module during the first semester of the academic year 2020-2021. The hy-brid model consists of a Generalized Linear Model. It’s fitting errors are used as an extra predictor to an artificial neural network. The architecture of the neural network can be described by the following sizes: size of the input layer (36), size of the hidden layer (1) and size of the output layer (1). Since new factors are revealed to affect students’ academic achievements, the model has been trained in the 70% of the participants to predict the grade of the remaining 30%. The model has therefore been divided into three subsets, with a training set of 70% of the sample and one hidden layer predicting the test set (15%) and the validation set (15%). The final form of the trained hybrid model resulted in a coefficient of determination equal to 1 (R = 1). This means that the data fitting process resulted in a 100% success rate, in terms of associating the independent variables with the dependent variable (grade).

Author Biographies

Zoe Kanetaki, University of West AtticaAthens, Greece.

Zoe Kanetaki is a Lecturer in the Department of Mechanical Engineering, University of West Attica. She received her degree in Architecture from E.S.A. and her M.Sc. degree in Urbanism and Regional Planning from NTUA. Her re-search interests include online learning, data analysis, engineering education and CAD.

Constantinos Stergiou, University of West AtticaAthens, Greece.

Constantinos Stergiou is Professor and Head of the Mechanical Engineering Department at the University of West Attica. He received his degree in Mechanical Engineering from NTUA, Greece and his Ph.D. from Technische Universität Darmstadt. His research interests lie in the field of Engineering Design, CAD/CAM/CAE and Additive Manufacturing.

Georgios Bekas, University of West AtticaAthens, Greece.

Georgios Bekas holds a PhD in Civil and Structural Engineering. His research interests include Operations Research, Machine Learning, and optimization of Civil and Energy Engineering works.

Christos Troussas, University of West AtticaAthens, Greece.

Christos Troussas is a Post-doctoral Researcher in the Department of Informatics and Computer Engineering, University of West Attica, Greece. He received the B.Sc., M.Sc., and Ph.D. degrees in Informatics from the Department of Informatics, University of Piraeus, Greece. His current research interests include software engineering, multiagent systems, adaptive HCI, and artificial intelligence.

Cleo Sgouropoulou, University of West AtticaAthens, Greece.

Cleo Sgouropoulou is Vice Rector of the University of West Attica, Greece and Professor in the Department of Informatics and Computer Engineering of the same University. She received the B.Sc. and Ph.D. degrees s in electrical and computer engineering from the Department of Electrical and Computer Engineering, NTUA, Greece. Her research interests include artificial intelligence in education and software engineering.




How to Cite

Kanetaki, Z., Stergiou, C., Bekas, G., Troussas, C., & Sgouropoulou, C. (2022). A Hybrid Machine Learning Model for Grade Prediction in Online Engineering Education. International Journal of Engineering Pedagogy (iJEP), 12(3), pp. 4–24. https://doi.org/10.3991/ijep.v12i3.23873