Visual Analysis of Contact Patterns in School Environments

Information Visualisation strategies can be applied in a variety of domains. In the context of temporal networks, i.e., networks in which interactions between individuals occur throughout time, efforts have been conducted to develop visual approaches that allow finding interaction patterns, anomalie...

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Main Authors: Jean R. PONCIANO, Claudio D. G. LINHARES, Sara L. MELO, Luciano V. LIMA, Bruno A. N. TRAVENÇOLO
Format: Article
Language:English
Published: Vilnius University 2020-09-01
Series:Informatics in Education
Subjects:
Online Access:https://infedu.vu.lt/journal/INFEDU/article/658/info
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spelling doaj-ada5fb618302483181bc3488328b7c3f2021-01-02T13:22:45ZengVilnius UniversityInformatics in Education1648-58312335-89712020-09-0119345547210.15388/infedu.2020.20Visual Analysis of Contact Patterns in School EnvironmentsJean R. PONCIANO0Claudio D. G. LINHARES1Sara L. MELO2Luciano V. LIMA3Bruno A. N. TRAVENÇOLO4Faculty of Computing, Federal University of Uberlândia, BrazilFaculty of Computing, Federal University of Uberlândia, BrazilFaculty of Electrical Engineering, Federal University of Uberlândia, BrazilFaculty of Electrical Engineering, Federal University of Uberlândia, BrazilFaculty of Computing, Federal University of Uberlândia, BrazilInformation Visualisation strategies can be applied in a variety of domains. In the context of temporal networks, i.e., networks in which interactions between individuals occur throughout time, efforts have been conducted to develop visual approaches that allow finding interaction patterns, anomalies, and other behaviours not previously perceived in the data. This paper presents two case studies involving real-world education networks from a primary school and a high school. For this purpose, we used the Massive Sequence View (MSV) layout with the Community-based Node Ordering (CNO) method, two well established approaches for visual analysis of temporal networks. Our results show that the identified patterns involving students/students and students/teachers represent important information to benefit and support decision making about school management and teaching strategies, especially those related to strategic group formation.https://infedu.vu.lt/journal/INFEDU/article/658/infotemporal networkscollaborative learningvisualisation in educationlearning analyticsdynamic networksnetwork communitiesnode reorderingmassive sequence view
collection DOAJ
language English
format Article
sources DOAJ
author Jean R. PONCIANO
Claudio D. G. LINHARES
Sara L. MELO
Luciano V. LIMA
Bruno A. N. TRAVENÇOLO
spellingShingle Jean R. PONCIANO
Claudio D. G. LINHARES
Sara L. MELO
Luciano V. LIMA
Bruno A. N. TRAVENÇOLO
Visual Analysis of Contact Patterns in School Environments
Informatics in Education
temporal networks
collaborative learning
visualisation in education
learning analytics
dynamic networks
network communities
node reordering
massive sequence view
author_facet Jean R. PONCIANO
Claudio D. G. LINHARES
Sara L. MELO
Luciano V. LIMA
Bruno A. N. TRAVENÇOLO
author_sort Jean R. PONCIANO
title Visual Analysis of Contact Patterns in School Environments
title_short Visual Analysis of Contact Patterns in School Environments
title_full Visual Analysis of Contact Patterns in School Environments
title_fullStr Visual Analysis of Contact Patterns in School Environments
title_full_unstemmed Visual Analysis of Contact Patterns in School Environments
title_sort visual analysis of contact patterns in school environments
publisher Vilnius University
series Informatics in Education
issn 1648-5831
2335-8971
publishDate 2020-09-01
description Information Visualisation strategies can be applied in a variety of domains. In the context of temporal networks, i.e., networks in which interactions between individuals occur throughout time, efforts have been conducted to develop visual approaches that allow finding interaction patterns, anomalies, and other behaviours not previously perceived in the data. This paper presents two case studies involving real-world education networks from a primary school and a high school. For this purpose, we used the Massive Sequence View (MSV) layout with the Community-based Node Ordering (CNO) method, two well established approaches for visual analysis of temporal networks. Our results show that the identified patterns involving students/students and students/teachers represent important information to benefit and support decision making about school management and teaching strategies, especially those related to strategic group formation.
topic temporal networks
collaborative learning
visualisation in education
learning analytics
dynamic networks
network communities
node reordering
massive sequence view
url https://infedu.vu.lt/journal/INFEDU/article/658/info
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