Visual Analysis of Contact Patterns in School Environments

Ponciano, Jean R.; Linhares, Cláudio D. G.; Melo, Sara Luzia de; Lima, Luciano Vieira; Travençolo, Bruno Augusto Nassif

doi:10.15388/infedu.2020.20

Cited by 10 publications

(10 citation statements)

References 37 publications

(46 reference statements)

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“…Finally, the category with the fewest papers was clustering. In this case, data analysis techniques were applied to assess the student-student and student-teacher interaction to see how the information extracted from clustering analysis can affect teaching strategies, especially those related to strategic group formation and school management ( Ponciano et al, 2020 ). Lailiyah et al (2019) benefited from clustering algorithms to identify student behavior and preferences in a high school context.…”

Section: Resultsmentioning

confidence: 99%

Applications of Learning Analytics in High Schools: A Systematic Literature Review

Sousa

Alexandre

Mello

et al. 2021

Front. Artif. Intell.

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Learning analytics aims to analyze data from students and learning environments to support learning at different levels. Although learning analytics is a recent field, it reached a high level of maturity, especially in its applications for higher education. However, little of the research in learning analytics targets other educational levels, such as high school. This paper reports the results of a systematic literature review (SLR) focused on the adoption of learning analytics in high schools. More specifically, the SLR followed four steps: the search, selection of relevant studies, critical assessment, and the extraction of the relevant field, which included the main goals, approaches, techniques, and challenges of adopting learning analytics in high school. The results show that, in this context, learning analytics applications are focused on small-scale initiatives rather than institutional adoption. Based on the findings of this study, in combination with the literature, this paper proposes future directions of research and development in order to scale up learning analytics applications in high schools.

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Section: Resultsmentioning

confidence: 99%

Applications of Learning Analytics in High Schools: A Systematic Literature Review

Sousa

Alexandre

Mello

et al. 2021

Front. Artif. Intell.

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“…DyNetVis is a freely available 1 and open-source 2 software developed in Java programming language to support multiple platforms. Our software has been successfully used for exploring networks in a variety of research areas, such as social network analysis [7], [11], healthcare [5], [7], [18], school [5], [19] and corporate environments [5], [11]. DyNetVis is also a powerful tool for simulating and analyzing dynamic processes (such as epidemics), as demonstrated in [8].…”

Section: Software Descriptionmentioning

confidence: 99%

DyNetVis - An interactive software to visualize structure and epidemics on temporal networks

Linhares

Ponciano

Paiva

et al. 2020

2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

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The study of complex networks, especially temporal networks, increased over the last years. Understanding patterns, trends, and anomalies in these networks, as well as simulating and analyzing dynamic processes (e.g., infection spread dynamics in social networks), are not trivial tasks. Information Visualization techniques offer significant potential to assist the user in these analyses. This paper presents an extended version of Dynamic Network Visualization (DyNetVis), a freely available and opensource interactive software to perform visual analysis of temporal networks. It provides four visualization techniques, structural, temporal, matrix, and community layouts, and a number of state-of-the-art methods to interact with each of these layouts. DyNetVis also implements dynamic processes, including standard epidemic models. It is a computational tool to study and explore networks in diverse domains.

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“…As it can be seen, the node ordering influences the generated layout and, consequently, the identification of patterns. Reprinted from(PONCIANO et al, 2020) c ○2020 Vilnius University Institute of Data Science and Digital Technologies.…”

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confidence: 99%

“…The presentation of the visual analysis method for evaluating different community detection algorithms (Section 5.1) is based on(LINHARES et al, 2020). Reprinted/adapted by permission from Springer Nature Customer Service Centre GmbH: Springer Nature Multimedia Tools and Applications, Visual analysis for evaluation of community detection algorithms by Claudio D. G. Linhares, Jean R. Ponciano et al c ○ 2020.…”

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confidence: 99%

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Improving visual analysis of streaming networks

Ponciano¹

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Temporal networks (also known as dynamic networks) are often used to model connections that occur over time between parts of a system by using nodes and edges. In temporal networks, all nodes, edges, and times, are known and available to be used in the analysis. However, in several real-world applications, data are produced in a massive and continuous way, which is known as data stream. In this case, the volume of data may be so large that the storage may be impossible and mining tasks become more challenging. In streaming temporal networks, edges are continuously arriving in non-stationary distribution. In both temporal and streaming temporal networks, patterns related to node and edge activity are typically irregular in time, which makes the visualization of such networks helpful to gain insights about network structure and dynamics. Nevertheless, the non-stationary distribution of incoming data increases complexity and turns the streaming temporal network visualization even more challenging. Several visualization layouts have been proposed, but they all have limitations. The main challenge in this context is the amount of visual information, that increases depending on the network size and density, and causes visual clutter due to edge overlap, fine temporal resolution, and node proximity. In this thesis, we propose methods to enhance the visualization of streaming temporal networks through the manipulation of the three network dimensions, namely node, edge, and time. Specifically, we propose: (i) CNO, a visual scalable node ordering method; (ii) SEVis, a streaming edge sampling method; and (iii) a streaming method that adapts the temporal resolution according to local levels of node activity. We also present a comparative study considering the combination of these methods. We show through case studies with real-world networks that each of these methods greatly improves layout readability, thus leading to a fast and reliable decision making.

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Visual Analysis of Contact Patterns in School Environments

Cited by 10 publications

References 37 publications

Applications of Learning Analytics in High Schools: A Systematic Literature Review

Applications of Learning Analytics in High Schools: A Systematic Literature Review

DyNetVis - An interactive software to visualize structure and epidemics on temporal networks

Improving visual analysis of streaming networks

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