Abstract
Nowadays due to technological revolution huge amount of data is generated in every fields including education as well. Extracting the useful insights from consequential data is a very critical task. Moreover, advancement in the deep learning techniques resulted in the effective prediction and analysis of data. In our proposed study deep learning model is be used for predicting the student’s academic performance. Experiments were performed using the two courses da-ta i.e., mathematics and Portuguese course. The data set contains demograph-ic, social, educational and students course grade data. The data set suffers from the imbalance, SMOTE (synthetic minority oversampling technique) is used. We evaluate the performance of the proposed model using several fea-ture sets and evaluation measures such as precision, recall, F-score, and ac-curacy. The result showed the significance of the proposed deep learning mod-el in early prediction of the students’ academic performance. The model achieved an accuracy of 0.964 for Portuguese course data set and 0.932 using mathematics course data set. Similarly, the precision of 0.99 for Portuguese and 0.94 for mathematics.
Highlights
Education is one of the fundamental components in the development of the country
The data sets suffer from data imbalance and which increases the risk of model overfitting
We developed a dense Deep Learning (DL) model to predict the student’s grades using the mathematics and Portuguese language course grades data set
Summary
Education is one of the fundamental components in the development of the country. In the last decade, there is a progression in the educational field such as development of electronic educational applications, learning management system, automated assessment systems, and gamification in learning [1], [2], [3]. Papadakis et al [1] proposed a tool for the assessment of educational mobile applications for the kindergarten children under the age of 3-6 using thirteen items. These electronic applications generate the paramount of data day by day. Due to the large size and complex nature, previously the data was unused This data could aid in analyzing the quality of the education, early prediction of student’s failure and dropout. It can help policy makers and educational administrators to find the best strategies to enhance the quality of education. One of the challenges is to gain an insight of this huge amount of data for early prediction of student’s performance and to explore the factors that led to the failure
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