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EduClust - A Visualization Application for Teaching Clustering Algorithms

Authors: Fuchs, Johannes; Isenberg, Petra; Bezerianos, Anastasia; Miller, Matthias; Keim, Daniel;

EduClust - A Visualization Application for Teaching Clustering Algorithms

Abstract

We present EduClust, a visualization application for teaching clustering algorithms. EduClust is an online application that combines visualizations, interactions, and animations to facilitate the understanding and teaching of clustering steps, parameters, and procedures. Traditional classroom settings aim for cognitive processes like remembering and understanding. We designed EduClust for expanded educational objectives like applying and evaluating. Educators can use the tool in class to show the effect of different clustering parameters on various datasets while animating through each algorithm's steps, but also use the tool to prepare traditional teaching material quickly by exporting animations and images. Students, on the other hand, benefit from the ability to compare and contrast the influence of clustering parameters on different datasets, while seeing technical details such as pseudocode and step-by-step explanations.

CCS Concepts: Theory of computation --> Unsupervised learning and clustering; Applied computing --> Interactive learning environments

Johannes Fuchs, Petra Isenberg, Anastasia Bezerianos, Matthias Miller, and Daniel Keim

Eurographics 2019 - Education Papers

Educate to Visualize

16

9

Countries
Germany, France
Keywords

Information Visualization, Applied computing, Interactive learning environments, [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], info:eu-repo/classification/ddc/004, Unsupervised learning and clustering, Theory of computation

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green