Deep Learning for Educational Data Science

Juan D. Pinto; Luc Paquette

doi:10.1007/978-3-031-64487-0_6

Deep Learning for Educational Data Science

Juan D. Pinto, Luc Paquette

Curriculum and Instruction

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

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.

Original language	English (US)
Title of host publication	Trust and Inclusion in AI-Mediated Education
Subtitle of host publication	Where Human Learning Meets Learning Machines
Editors	Dora Kourkoulou, Anastasia Olga Tzirides, Bill Cope, Mary Kalantzis
Publisher	Springer
Pages	111-139
Number of pages	29
ISBN (Electronic)	9783031644870
ISBN (Print)	9783031644863, 9783031644894
DOIs	https://doi.org/10.1007/978-3-031-64487-0_6
State	Published - Sep 28 2024

Publication series

Name	Postdigital Science and Education
Volume	Part F3835
ISSN (Print)	2662-5326
ISSN (Electronic)	2662-5334

Keywords

AI educational applications
AI use cases
Data-driven insights
Deep learning
Educational data science
Machine learning

ASJC Scopus subject areas

Arts and Humanities (miscellaneous)
Philosophy
Social Sciences (miscellaneous)
Education

Online availability

10.1007/978-3-031-64487-0_6

Library availability

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Cite this

Deep Learning for Educational Data Science. / Pinto, Juan D.; Paquette, Luc.
Trust and Inclusion in AI-Mediated Education: Where Human Learning Meets Learning Machines. ed. / Dora Kourkoulou; Anastasia Olga Tzirides; Bill Cope; Mary Kalantzis. Springer, 2024. p. 111-139 (Postdigital Science and Education; Vol. Part F3835).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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Deep Learning for Educational Data Science

Abstract

Publication series

Keywords

ASJC Scopus subject areas

Online availability

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Trust and Inclusion in AI-Mediated Education: Where Human Learning Meets Learning Machines

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