Analyzing student performance using evolutionary artificial neural network algorithm

  • Authors

    • A S. Arunachalam
    • T Velmurugan
    2018-05-07
    https://doi.org/10.14419/ijet.v7i2.26.12537
  • Educational Data Mining, Artificial Neural Network, Probabilistic Neural Networks, Evolutionary Artificial Neural Network.

  • Abstract

    Educational Data Mining (EDM) and Learning Systematic (LS) research have appeared as motivating areas of research, which are clarifying beneficial understanding from educational databases for many purposes such as predicting student’s success factor. The ability to predict a student’s performance can be beneficial in modern educational systems. This research work aims at developing an evolutionary approach based on genetic algorithm and the artificial neural network. The traditional artificial neural network lacks predicting student performance due to the poor modeling structure and the capability of assigning proper weights to each node under the hidden layer. This problem is overwhelmed with the aid of genetic algorithm optimization approach which produces appropriate fitness function evaluation in each iteration of the learning process. The performances gradually increase the accuracy of the prediction and classification more precisely.

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  • How to Cite

    S. Arunachalam, A., & Velmurugan, T. (2018). Analyzing student performance using evolutionary artificial neural network algorithm. International Journal of Engineering & Technology, 7(2.26), 67-73. https://doi.org/10.14419/ijet.v7i2.26.12537

    Received date: 2018-05-06

    Accepted date: 2018-05-06

    Published date: 2018-05-07