Deep Learning for Educational Data Science

With the ever-growing presence of deep artificial neural networks in every facet of modern life, a growing body of researchers in educational data science—a field consisting of various interrelated research communities—have turned their attention to leveraging these powerful algorithms within the education domain. Use cases range from advanced knowledge tracing models that can leverage open-ended student essays or snippets of code to automatic affect and behavior detectors that can identify when a student is frustrated or aimlessly trying to solve problems unproductively—and much more. This chapter provides a brief introduction to deep learning, describes some of its advantages and limitations, presents a survey of its many uses in education, and discusses how it may further shape the field of educational data science.