Opportunities to apply data mining techniques to analyze educational data and improve learning are increasing. A multitude of data are being produced by institutional technology, e-learning resources, and online and virtual courses. These data could be used by educators to analyze and understand the learning behaviors of students. The obtained data are raw data that must be analyzed, requiring educational data mining to predict useful information about students, such as academic performance, among other things. Many researchers have used traditional machine learning to predict the academic performance of students, and very little research has been conducted on the architecture of convolutional neural networks (CNNs) in the context of the pedagogical domain. We built a hybrid 2D CNN model by combining two different 2D CNN models to predict academic performance. Our sample comprised 1D data, so we transformed it to 2D image data to test the performance of our hybrid model. We compared the performance of our model with that of different traditional baseline models. Our model outperformed baseline models, such as k-nearest neighbor, naïve Bayes, decision trees, and logistic regression, in terms of accuracy.
From 1996 to 2000, he was a Hardware Design Engineer with Compaq, where he specialized in board layout for high-availability redundant array of independent disks (RAID) controllers. His current research interests include robotics, real-time control-system implementation, rapid prototyping for real-time systems, and modeling and analysis of mechatronic systems
Dr. Mohammadi-Aragh investigates the use of digital systems to measure and support engineering education, specifically through learning analytics and the pedagogical uses of digital systems. She also investigates fundamental questions critical to improving undergraduate engineering degree pathways.. She earned her Ph.D. in Engineering Education from Virginia Tech. In 2013, Dr. Mohammadi-Aragh was honored as a promising new engineering education researcher when she was selected as an ASEE Educational Research and Methods Division Apprentice Faculty.
Dr. Mohammadi-Aragh investigates the formation of engineers during their undergraduate degree program, and the use of computing to measure and support that formation. She earned her Ph.D. in Engineering Education from Virginia Tech. In 2013, Dr. Mohammadi-Aragh was honored as a promising new engineering education researcher when she was selected as an ASEE Educational Research and Methods Division Apprentice Faculty.
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