  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
domain of education. 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 come to shape the field of educational data
science.
