A Systematic Review of Deep Learning Approaches to Educational Data Mining

• Main Authors: Antonio Hernández-Blanco, Boris Herrera-Flores, David Tomás, Borja Navarro-Colorado
• Format: Article
• Language: English
• Published: Hindawi-Wiley 2019-01-01
• Series: Complexity
• Online Access: http://dx.doi.org/10.1155/2019/1306039

Educational Data Mining (EDM) is a research field that focuses on the application of data mining, machine learning, and statistical methods to detect patterns in large collections of educational data. Different machine learning techniques have been applied in this field over the years, but it has been recently that Deep Learning has gained increasing attention in the educational domain. Deep Learning is a machine learning method based on neural network architectures with multiple layers of processing units, which has been successfully applied to a broad set of problems in the areas of image recognition and natural language processing. This paper surveys the research carried out in Deep Learning techniques applied to EDM, from its origins to the present day. The main goals of this study are to identify the EDM tasks that have benefited from Deep Learning and those that are pending to be explored, to describe the main datasets used, to provide an overview of the key concepts, main architectures, and configurations of Deep Learning and its applications to EDM, and to discuss current state-of-the-art and future directions on this area of research.