Deep Learning for Educational Data Science
<|reference_start|>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 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.<|reference_end|>
@article{pinto2024deep,
    title={Deep Learning for Educational Data Science},
    author={Juan D. Pinto, Luc Paquette},
    journal={arXiv preprint arXiv:2404.19675},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.19675},
    primaryClass={cs.CY cs.AI cs.LG}
}