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EduClust -A Visualization Application for Teaching Clustering Algorithms

Authors :
Fuchs, Johannes
Isenberg, Petra
Bezerianos, Anastasia
Miller, Matthias
Keim, Daniel A.
University of Konstanz
Analysis and Visualization (AVIZ)
Laboratoire de Recherche en Informatique (LRI)
Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)
Interacting with Large Data (ILDA)
Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)
Université Paris-Sud - Paris 11 - Faculté des Sciences (UP11 UFR Sciences)
Université Paris-Sud - Paris 11 (UP11)
Bezerianos, Anastasia
Source :
Eurographics 2019-40th Annual Conference of the European Association for Computer Graphics, Eurographics 2019-40th Annual Conference of the European Association for Computer Graphics, May 2019, Genova, Italy. pp.1-8
Publication Year :
2019
Publisher :
HAL CCSD, 2019.

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.<br />Eurographics 2019 - Education Papers<br />Educate to Visualize<br />9<br />16<br />Johannes Fuchs, Petra Isenberg, Anastasia Bezerianos, Matthias Miller, and Daniel Keim<br />CCS Concepts: Theory of computation --> Unsupervised learning and clustering; Applied computing --> Interactive learning environments

Details

Language :
English
Database :
OpenAIRE
Journal :
Eurographics 2019-40th Annual Conference of the European Association for Computer Graphics, Eurographics 2019-40th Annual Conference of the European Association for Computer Graphics, May 2019, Genova, Italy. pp.1-8
Accession number :
edsair.doi.dedup.....95313eabb3b116d01cc180c8e24183d1