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Education Data Science: Past, Present, Future

delete2021-10-18
delete15
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OA
AI
D
Daniel A. McFarland *
S
Saurabh Khanna
B
Benjamin W. Domingue
Z
Zachary A. Pardos
DOI:10.1177/23328584211052055delete
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Abstract

Abstract

En 中文
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.
Keywords:
data science
network analysis
natural language processing
machine learning
learning analytics
data mining

Journal

AERA Open cover
AERA Open
IF:
3.6
Papers:
891
Citations:
2.3K

Organization

S
Stanford University
Scholars:
9.6W
Papers: 8.2W
Citations: 17.0W
University of California System cover
University of California System
Scholars:
37.5W
Papers: 33.7W
Citations: 6.6K