Clustering Algorithms in an Educational Context: An Automatic Comparative Approach

Clustering Algorithms in an Educational Context: An Automatic Comparative Approach

Despite an increasing consensus regarding the significance of properly identifying the most suitable clustering method for a given problem, a surprising amount of educational research, including both educational data mining (EDM) and learning analytics (LA), neglects this critical task. This shortco...

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Bibliographic Details
Main Authors: Danial Hooshyar, Yeongwook Yang, Margus Pedaste, Yueh-Min Huang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Educational context
clustering methods
multiple criteria decision-making
educational data mining
learning analytics
Online Access:https://ieeexplore.ieee.org/document/9162041/

Internet

https://ieeexplore.ieee.org/document/9162041/

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