A Radial Basis Function Neural Network Prediction Model Based on Association Rules

Abstract

As a rapidly growing field of learning analysis technology, learning prediction has been developed by many researchers from different angles and educational environments. Different prediction models have their own characteristics and have produced different results. However, each model has their limitations in adaptability and generalization. In this concept paper, we proposed a radial basis function neural network prediction model based on association rules. The predictors of the model are not pre-determined. They are selected by mining the items with a strong correlation between the predicted results.