Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (6): 1560-1569.doi: 10.23940/ijpe.19.06.p7.15601569

Previous Articles     Next Articles

Data-Driven Student Learning Performance Prediction based on RBF Neural Network

Chunqiao Mia,b,*   

  1. a School of Computer Science and Engineering, Huaihua University, Huaihua, 418000, China
    b Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province, Huaihua, 418000, China
  • Submitted on ;
  • Contact: * E-mail address:
  • About author:Chunqiao Mi received his Ph.D. degree from the College of Information and Electrical Engineering at China Agricultural University, China in 2012. He is currently an associate professor in the School of Computer Science and Engineering at Huaihua University, China. His research interests include data science and educational information technology.
  • Supported by:
    This study is supported by the project "Data-driven study on risk assessment and early warning of learning situation in Hunan Local Universities" (No. 17YBQ087), granted by the Hunan Provincial Philosophy and Social Sciences Foundation.

Abstract: With the expansion of college enrollment in recent years, the quality of students' learning is beginning to decline. At present, education quality governance has become the internal demand of the reform and development of higher education. Learning performance prediction is an important means to effectively resolve the academic crisis and improve the overall education quality. In this study, firstly, the current status and problems about learning performance prediction were analyzed from the perspective of basic data, evaluation indicators, and prediction methods. Secondly, driven by ten items of basic learning situation data, a learning performance prediction model based on the RBF neural network was established, which included three layers in network topology the input layer, hidden layer, and output layer. The activation functions of the hidden layer and output layer were a Gauss radial basis function and linear function, respectively. The modeling process included three steps forward propagation computing prediction loss, error backward propagation adjusting network parameters, and network optimization determining model hyperparameters. The obtained results showed that the trained model had small relative root mean square error values for both the training data and testing data. When comparing the original observation values and model predicted values, it was observed that most of the sample points were evenly distributed on both sides of the diagonal line of the contrast graph, which indicates that the RBF neural network model employed in this study is promising in learning performance prediction. It is of good reference significance for promoting more accurate and efficient learning performance prediction and improving the efficiency and effectiveness of education quality governance.

Key words: learning performance prediction, RBF neural network, education quality governance