International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 10, No. 6, 8 Dec. 2020
Cover page and Table of Contents: PDF (size: 229KB)
Predicting Students' Academic Performance in Educational Data Mining Based on Deep Learning Using TensorFlow
Full Text (PDF, 229KB), PP.27-33
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Author(s)
Mussa S. Abubakari
1,*
Fatchul Arifin
1
Gilbert G. Hungilo
2
Index Terms
Classification, Data Mining Techniques, Educational Data Mining, Neural Network Algorithm, Predictive Model.
Abstract
The study was aimed to create a predictive model for predicting students’ academic performance based on a neural network algorithm. This is because recently, educational data mining has become very helpful in decision making in an educational context and hence improving students’ academic outcomes. This study implemented a Neural Network algorithm as a data mining technique to extract knowledge patterns from student’s dataset consisting of 480 instances (students) with 16 attributes for each student. The classification metric used is accuracy as the model quality measurement. The accuracy result was below 60% when the Adam model optimizer was used. Although, after applying the Stochastic Gradient Descent optimizer and dropout technique, the accuracy increased to more than 75%. The final stable accuracy obtained was 76.8% which is a satisfactory result. This indicates that the suggested NN model can be reliable for prediction, especially in social science studies.
Cite This Paper
Mussa S. Abubakaria, Fatchul Arifin, Gilbert G. Hungilo. " Predicting Students' Academic Performance in Educational Data Mining Based on Deep Learning Using TensorFlow ", International Journal of Education and Management Engineering (IJEME), Vol.10, No.6, pp.27-33, 2020. DOI: 10.5815/ijeme.2020.06.04
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