A Dynamic Early Warning Method of Student Study Failure Risk based on Fuzzy Synthetic Evaluation

Mi, Chunqiao

doi:10.23940/ijpe.18.04.p6.639646

Cited by 1 publication

(1 citation statement)

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“…The other category is machine learning approaches. For example, decision tree and association rules [13], support vector machine [14], Bayesian algorithm [15], BP artificial neural network [16], genetic algorithm [17], and fuzzy comprehensive evaluation [18] were all applied in the prediction of students' learning performance and identification of at-risk students with learning difficulties. From these, it can be noted that research on learning performance prediction methods has shown a trend of algorithmization and automation.…”

Section: The Research On Prediction Algorithmsmentioning

confidence: 99%

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

Mi¹

2019

IJPE

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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.

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