PREDICTING STUDENTS PERFORMANCE THROUGH INTERNET USAGE BY ARTIFICIAL NEURAL NETWORK
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Date
2021-01-30
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Abstract
Students academic performance primarily depends on their internet usage for various activity
purpose. Predicting students academic success is critical for educational institutions. Because
strategic programs can be planned in improving or maintaining students’ performance during the
period of their studies. The estimation in this study is done by a multilayer perceptron neural
network model. 18 parameters which contain students internet usage data captured by a
questionnaire chosen as input layer parameters. Hidden nodes will be determined experimentally.
The output level values which define success of the students. The Back-Propagation algorithm is
used for training of ANN. The mean squares of the errors are used as a performance (error) function
with its goal set to zero. In conclusion, the application done with comparison with other ML
algorithms. It is recommended that a research with same parameters would be better results with
higher participation. In this paper we perform our experiment for school, college and university
students which helps us to indicate them separately.