Multidimensional Approach Based on Deep Learning to Improve the Prediction Performance of DNN Models

Mohamed El Fouki, Noura Aknin, Kamal Eddine El Kadiri


The most of collected data samples from E-learning systems consist of correlated information caused by overlapping input instances, which decrease the classifier credibility and reliability. This paper presents an improved classification model based on Deep Learning and Principal Component Analysis (PCA) method as its use in reducing the dimensionality of data. By this task, we introduce the best learning process to extract just the useful parameters that describe students’ per-formances in an E-learning system. One of the primary goals of this technique is to help earlier in detecting the dropouts and discovering of students who need special attention, so that the teachers could provide the appropriate counseling at the right time. This study presents the proposal approach and its algorithms. In addition, it shows how deep neural network was modeled in the training phase, and how PCA helps in the elimination of correlated information in our dataset to increase the classifier performance. Finally, we introduce an example of an appli-cation of the method in a data mining scenario, find out more references for fur-ther information.


Educational Data Mining (EDM); Classification; Deep Neural Network (DNN); Deep Learning; Principal Component Analysis (PCA)

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Copyright (c) 2019 Mohamed EL FOUKI, Noura Aknin, Kamal Eddine El Kadiri

International Journal of Emerging Technologies in Learning (iJET) – eISSN: 1863-0383
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