Open AccessAn Improved Early Student’s Academic Performance Prediction Using Deep Learning
Author(s) -
Nida Aslam,
Irfan Ullah Khan,
Leena H. Alamri,
Ranim S. Almuslim
Publication year - 2021
Publication title -
international journal of emerging technologies in learning/international journal: emerging technologies in learning
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.454
H-Index - 24
eISSN - 1868-8799
pISSN - 1863-0383
DOI - 10.3991/ijet.v16i12.20699
Subject(s) - oversampling , portuguese , artificial intelligence , computer science , data set , set (abstract data type) , deep learning , task (project management) , machine learning , recall , training set , mathematics education , mathematics , engineering , psychology , computer network , philosophy , linguistics , systems engineering , bandwidth (computing) , cognitive psychology , programming language
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.