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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Vilnius University
2020-09-01
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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 |
work_keys_str_mv |
AT jeanrponciano visualanalysisofcontactpatternsinschoolenvironments AT claudiodglinhares visualanalysisofcontactpatternsinschoolenvironments AT saralmelo visualanalysisofcontactpatternsinschoolenvironments AT lucianovlima visualanalysisofcontactpatternsinschoolenvironments AT brunoantravencolo visualanalysisofcontactpatternsinschoolenvironments |
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1724353991126548480 |