Deep Learning for Educational Data Science

As artificial intelligence (AI) continues to penetrate ever deeper into modern life, one particular family of machine learning algorithms--namely, deep neural networks--have come to be seen as the solution to many of the challenges that have stumped more classical algorithms in the past. Modeled loosely on the structure of biological neural networks, artificial neural networks consist of chains of simple mathematical transformations that can model complex non-linear decision boundaries in large problem spaces. In particular, deep neural networks--artificial neural networks that consist of multiple layers of transformations--allow for sufficient complexity to tackle tasks in a wide variety of fields. These models are collectively and more colloquially referred to as deep learning. A growing body of education researchers are now also turning their attention to leveraging the power of deep learning algorithms for the tasks of improving and understanding human learning. Researchers in educational data science, a field consisting of various interrelated research communities such as Educational Data Mining (EDM), Learning Analytics (LA), and AI in Education (AIED), have been involved in this endeavor.