A structure optimization algorithm of neural networks for pattern learning from educational data

Abstract

Digital technology integration is recognized as an important component in education reformation. Learning patterns of educators’ and students’ perceptions of, beliefs about and experiences in using digital technologies through self-reported questionnaire data is straightforward but difficult, due to the huge-volume, diversified and uncertain data. This chapter demonstrates the use of fuzzy concept representation and neural network to identify unique patterns via questionnaire questions. Fuzzy concept representation is used to quantify survey response and reform response using linguistic expression; while neural network is trained to learn the complex pattern among questionnaire data. Furthermore, to improve the learning performance of the neural network, a novel structure optimization algorithm based on sparse representation is introduced. The proposed algorithm minimizes the residual output error by selecting important neuron connection (weights) from the original structure. The efficiency of the proposed work is evaluated using a state-level student survey. Experimental results show that the proposed algorithm performs favorably compared to traditional approaches.