Education Data Science: Past, Present, Future

This AERA Open special topic concerns the large emerging research area of education data science (EDS). In a narrow sense, EDS applies statistics and computational techniques to educational phenomena and questions. In a broader sense, it is an umbrella for a fleet of new computational techniques bei...

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Bibliographic Details
Published inAERA open Vol. 7; no. 1
Main Authors McFarland, Daniel A., Khanna, Saurabh, Domingue, Benjamin W., Pardos, Zachary A.
Format Journal Article
LanguageEnglish
Published Los Angeles, CA SAGE Publications 2021
Sage Publications Ltd
SAGE Publishing
Subjects
Artificial Intelligence
Computation
Data Collection
Data science
Definitions
Education
Educational History
Educational Research
Educational Trends
Learning Analytics
Measurement
Natural Language Processing
Network Analysis
Prediction
Statistics
network analysis
data science
natural language processing
machine learning
learning analytics
data mining
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Summary:This AERA Open special topic concerns the large emerging research area of education data science (EDS). In a narrow sense, EDS applies statistics and computational techniques to educational phenomena and questions. In a broader sense, it is an umbrella for a fleet of new computational techniques being used to identify new forms of data, measures, descriptives, predictions, and experiments in education. Not only are old research questions being analyzed in new ways but also new questions are emerging based on novel data and discoveries from EDS techniques. This overview defines the emerging field of education data science and discusses 12 articles that illustrate an AERA-angle on EDS. Our overview relates a variety of promises EDS poses for the field of education as well as the areas where EDS scholars could successfully focus going forward.
ISSN:2332-8584
2332-8584
DOI:10.1177/23328584211052055