Intelligent data analysis using artificial neural networks for decision making in the education domain

Abstract

Nowadays, applying educational intelligent data analysis (EIDA) seems relevant for improving the educational process based on big data. It implies developing and improving the methods of processing collected data in educational institutions to understand academic issues better. Over the past decades, artificial neural networks (ANNs) have been recognized as the most prominent techniques for learning analytics. In this work, we systematized the recent scientific literature in EIDA with ANNs. The paper analyzes the applications of ANN to EIDA and discusses the computational issues in the EIDA domain. According to the investigation, most educational data mining tasks are addressed by controlled learning models, such as classification, regression, and time-series prediction. Most in-depth methods used in the EIDA domain are traditional types of ANN. Well-known techniques such as multi-year perceptron and deep long short‐term memory networks have been mainly used for classification and prediction tasks within the education sphere. However, the difficulty of interpreting the results produced by ANNs has also been a challenge for intelligent data practitioners in any domain, including education.