Footprints in the sky: using student track logs from a "bird's eye view" virtual field trip to enhance learning

Research into virtual field trips (VFTs) started in the 1990s but, only recently, the maturing technology of devices and networks has made them viable options for educational settings. By considering an experiment, the learning benefits of logging the movement of students within a VFT are shown. The data are visualized by two techniques: “animated path maps” are dynamic animations of students' movement in a VFT; “paint spray maps” show where students concentrated their visual attention and are static. A technique for producing these visualizations is described and the educational use of tracking data in VFTs is critically discussed

Travails in the third dimension: a critical evaluation of three-dimensional geographical visualization

Several broad questions are posed about the role of the third dimension in data visualization. First, how far have we come in developing effective 3D displays for the analysis of spatial and other data? Second, when is it appropriate to use 3D techniques in visualising data, which 3D techniques are most appropriate for particular applications, and when might 2D approaches be more appropriate? (Indeed, is 3D always better than 2D?) Third, what can we learn from other communities in which 3D graphics and visualization technologies have been developed? And finally, what are the key R&D challenges in making effective use of the third dimension for visualising data across the spatial and related sciences? Answers to these questions will be based on several lines of evidence: the extensive literature on data and information visualization; visual perception research; computer games technology; and the author’s experiments with a prototype 3D data visualization system

Travails in the third dimension: a critical evaluation of three-dimensional geographical visualization

Several broad questions are posed about the role of the third dimension in data visualization. First, how far have we come in developing effective 3D displays for the analysis of spatial and other data? Second, when is it appropriate to use 3D techniques in visualising data, which 3D techniques are most appropriate for particular applications, and when might 2D approaches be more appropriate? (Indeed, is 3D always better than 2D?) Third, what can we learn from other communities in which 3D graphics and visualization technologies have been developed? And finally, what are the key R&D challenges in making effective use of the third dimension for visualising data across the spatial and related sciences? Answers to these questions will be based on several lines of evidence: the extensive literature on data and information visualization; visual perception research; computer games technology; and the author’s experiments with a prototype 3D data visualization system

Soluciones visuales Interactivas aplicadas a grandes volúmenes de datos de entornos 3D de aprendizaje y prácticas

[ES] Este documento presenta el trabajo de investigación asociado al desarrollo de una propuesta de Visualización de Información apropiada para representar grandes cantidades de datos sobre movimientos de usuarios entre regiones de un Mundo Virtual de Aprendizaje y Prácticas y los patrones de exploración que en ellos se producen. Este trabajo abarca todas las fases que incluyen en la propuesta de visualización: desde la identificación de las fuentes de datos, la extracción y procesado de la información, la aplicación de algoritmos de Minería de Datos para extraer conocimiento de la información, hasta la propuesta final de una solución visual que represente la información y el conocimiento que encierran los datos, y el conjunto de características y mecanismos que debe incorporar dicha visualización

Trends in the Salience of Data Collected in a Multi User Virtual Environment: an Exploratory Study

In this study, by exploring patterns in the degree of physical salience of the data the students collected, I investigated the relationship between the level of students’ tendency to frame explanations in terms of complex patterns and evidence of how they attend to and select data in support of their developing understandings of causal relationships. I accomplished this by analyzing longitudinal data collected as part of a larger study of 143 7th grade students (clustered within 36 teams, 5 teachers, and 2 schools in the same Northeastern school district) as they navigated and collected data in an ecosystems-based multi-user virtual environment curriculum known as the EcoMUVE Pond module (Metcalf, Kamarainen, Tutwiler, Grotzer, Dede, 2011) . Using individual growth modeling (Singer & Willett, 2003) I found no direct link between student pre-intervention tendency to offer explanations containing complex causal components and patterns of physical salience-driven data collection (average physical salience level, number of low physical salience data points collected, and proportion of low physical salience data points collected), though prior science content knowledge did affect the initial status and rate of change of outcomes in the average physical salience level and proportion of low physical salience data collected over time. The findings of this study suggest two issues for consideration about the use of MUVEs to study student data collection behaviors in complex spaces. Firstly, the structure of the curriculum in which the MUVE is embedded might have a direct effect on what types of data students choose to collect. This undercuts our ability to make inferences about student-driven decisions to collect specific types of data, and suggests that a more open-ended curricular model might be better suited to this type of inquiry. Secondly, differences between teachers’ choices in how to facilitate the units likely contribute to the variance in student data collection behaviors between students with different teachers. This foreshadows external validity issues in studies that use behaviors of students within a single class to develop “detectors” of student latent traits (e.g., Baker, Corbett, Roll, Koedinger, 2008)

Data Mining and Visualization on Live Chat Data for E-commerce Business

The purpose of this paper is to design, build and evaluate an interactive visualization tool for data analysts to analyze as well as interact with the live chat data from a corporate website for customer relationship management. Sales lead and customer support are the major purposes of the live chat service. Data mining technologies are applied to classify the chat data into categories that can help marketing and sales teams to target their potential customers more accurately and efficiently. By interacting with the web visualization tool, data analysts will have the capability to obtain valuable information about customers' concerns and buying interests on their products and solutions. The results indicate that chat classification achieves higher accuracy on major class "Lead" but lower accuracy on minor classes due to the imbalanced distribution of dataset as well as human bias when manually labeling the training data. Based on the analytic results of chat visualization, data analysts gain the knowledge gap between customers' concern and the information provided on the corporate website, and propose new ideas to improve their digital marketing approaches as well.Master of Science in Information Scienc

Spatial Interaction for Immersive Mixed-Reality Visualizations

Growing amounts of data, both in personal and professional settings, have caused an increased interest in data visualization and visual analytics. Especially for inherently three-dimensional data, immersive technologies such as virtual and augmented reality and advanced, natural interaction techniques have been shown to facilitate data analysis. Furthermore, in such use cases, the physical environment often plays an important role, both by directly influencing the data and by serving as context for the analysis. Therefore, there has been a trend to bring data visualization into new, immersive environments and to make use of the physical surroundings, leading to a surge in mixed-reality visualization research. One of the resulting challenges, however, is the design of user interaction for these often complex systems. In my thesis, I address this challenge by investigating interaction for immersive mixed-reality visualizations regarding three core research questions: 1) What are promising types of immersive mixed-reality visualizations, and how can advanced interaction concepts be applied to them? 2) How does spatial interaction benefit these visualizations and how should such interactions be designed? 3) How can spatial interaction in these immersive environments be analyzed and evaluated? To address the first question, I examine how various visualizations such as 3D node-link diagrams and volume visualizations can be adapted for immersive mixed-reality settings and how they stand to benefit from advanced interaction concepts. For the second question, I study how spatial interaction in particular can help to explore data in mixed reality. There, I look into spatial device interaction in comparison to touch input, the use of additional mobile devices as input controllers, and the potential of transparent interaction panels. Finally, to address the third question, I present my research on how user interaction in immersive mixed-reality environments can be analyzed directly in the original, real-world locations, and how this can provide new insights. Overall, with my research, I contribute interaction and visualization concepts, software prototypes, and findings from several user studies on how spatial interaction techniques can support the exploration of immersive mixed-reality visualizations.Zunehmende Datenmengen, sowohl im privaten als auch im beruflichen Umfeld, führen zu einem zunehmenden Interesse an Datenvisualisierung und visueller Analyse. Insbesondere bei inhärent dreidimensionalen Daten haben sich immersive Technologien wie Virtual und Augmented Reality sowie moderne, natürliche Interaktionstechniken als hilfreich für die Datenanalyse erwiesen. Darüber hinaus spielt in solchen Anwendungsfällen die physische Umgebung oft eine wichtige Rolle, da sie sowohl die Daten direkt beeinflusst als auch als Kontext für die Analyse dient. Daher gibt es einen Trend, die Datenvisualisierung in neue, immersive Umgebungen zu bringen und die physische Umgebung zu nutzen, was zu einem Anstieg der Forschung im Bereich Mixed-Reality-Visualisierung geführt hat. Eine der daraus resultierenden Herausforderungen ist jedoch die Gestaltung der Benutzerinteraktion für diese oft komplexen Systeme. In meiner Dissertation beschäftige ich mich mit dieser Herausforderung, indem ich die Interaktion für immersive Mixed-Reality-Visualisierungen im Hinblick auf drei zentrale Forschungsfragen untersuche: 1) Was sind vielversprechende Arten von immersiven Mixed-Reality-Visualisierungen, und wie können fortschrittliche Interaktionskonzepte auf sie angewendet werden? 2) Wie profitieren diese Visualisierungen von räumlicher Interaktion und wie sollten solche Interaktionen gestaltet werden? 3) Wie kann räumliche Interaktion in diesen immersiven Umgebungen analysiert und ausgewertet werden? Um die erste Frage zu beantworten, untersuche ich, wie verschiedene Visualisierungen wie 3D-Node-Link-Diagramme oder Volumenvisualisierungen für immersive Mixed-Reality-Umgebungen angepasst werden können und wie sie von fortgeschrittenen Interaktionskonzepten profitieren. Für die zweite Frage untersuche ich, wie insbesondere die räumliche Interaktion bei der Exploration von Daten in Mixed Reality helfen kann. Dabei betrachte ich die Interaktion mit räumlichen Geräten im Vergleich zur Touch-Eingabe, die Verwendung zusätzlicher mobiler Geräte als Controller und das Potenzial transparenter Interaktionspanels. Um die dritte Frage zu beantworten, stelle ich schließlich meine Forschung darüber vor, wie Benutzerinteraktion in immersiver Mixed-Reality direkt in der realen Umgebung analysiert werden kann und wie dies neue Erkenntnisse liefern kann. Insgesamt trage ich mit meiner Forschung durch Interaktions- und Visualisierungskonzepte, Software-Prototypen und Ergebnisse aus mehreren Nutzerstudien zu der Frage bei, wie räumliche Interaktionstechniken die Erkundung von immersiven Mixed-Reality-Visualisierungen unterstützen können

On data-driven systems analyzing, supporting and enhancing users’ interaction and experience

[EN]The research areas of Human-Computer Interaction and Software Architectures have been traditionally treated separately, but in the literature, many authors made efforts to merge them to build better software systems. One of the common gaps between software engineering and usability is the lack of strategies to apply usability principles in the initial design of software architectures. Including these principles since the early phases of software design would help to avoid later architectural changes to include user experience requirements. The combination of both fields (software architectures and Human-Computer Interaction) would contribute to building better interactive software that should include the best from both the systems and user-centered designs. In that combination, the software architectures should enclose the fundamental structure and ideas of the system to offer the desired quality based on sound design decisions. Moreover, the information kept within a system is an opportunity to extract knowledge about the system itself, its components, the software included, the users or the interaction occurring inside. The knowledge gained from the information generated in a software environment can be used to improve the system itself, its software, the users’ experience, and the results. So, the combination of the areas of Knowledge Discovery and Human-Computer Interaction offers ideal conditions to address Human-Computer-Interaction-related challenges. The Human-Computer Interaction focuses on human intelligence, the Knowledge Discovery in computational intelligence, and the combination of both can raise the support of human intelligence with machine intelligence to discover new insights in a world crowded of data. This Ph.D. Thesis deals with these kinds of challenges: how approaches like data-driven software architectures (using Knowledge Discovery techniques) can help to improve the users' interaction and experience within an interactive system. Specifically, it deals with how to improve the human-computer interaction processes of different kind of stakeholders to improve different aspects such as the user experience or the easiness to accomplish a specific task. Several research actions and experiments support this investigation. These research actions included performing a systematic literature review and mapping of the literature that was aimed at finding how the software architectures in the literature have been used to support, analyze or enhance the human-computer interaction. Also, the actions included work on four different research scenarios that presented common challenges in the Human- Computer Interaction knowledge area. The case studies that fit into the scenarios selected were chosen based on the Human-Computer Interaction challenges they present, and on the authors’ accessibility to them. The four case studies were: an educational laboratory virtual world, a Massive Open Online Course and the social networks where the students discuss and learn, a system that includes very large web forms, and an environment where programmers develop code in the context of quantum computing. The development of the experiences involved the review of more than 2700 papers (only in the literature review phase), the analysis of the interaction of 6000 users in four different contexts or the analysis of 500,000 quantum computing programs. As outcomes from the experiences, some solutions are presented regarding the minimal software artifacts to include in software architectures, the behavior they should exhibit, the features desired in the extended software architecture, some analytic workflows and approaches to use, or the different kinds of feedback needed to reinforce the users’ interaction and experience. The results achieved led to the conclusion that, despite this is not a standard practice in the literature, the software environments should embrace Knowledge Discovery and datadriven principles to analyze and respond appropriately to the users’ needs and improve or support the interaction. To adopt Knowledge Discovery and data-driven principles, the software environments need to extend their software architectures to cover also the challenges related to Human-Computer Interaction. Finally, to tackle the current challenges related to the users’ interaction and experience and aiming to automate the software response to users’ actions, desires, and behaviors, the interactive systems should also include intelligent behaviors through embracing the Artificial Intelligence procedures and techniques

On Data-driven systems analyzing, supporting and enhancing users’ interaction and experience

Tesis doctoral en inglés y resumen extendido en español[EN] The research areas of Human-Computer Interaction and Software Architectures have been traditionally treated separately, but in the literature, many authors made efforts to merge them to build better software systems. One of the common gaps between software engineering and usability is the lack of strategies to apply usability principles in the initial design of software architectures. Including these principles since the early phases of software design would help to avoid later architectural changes to include user experience requirements. The combination of both fields (software architectures and Human-Computer Interaction) would contribute to building better interactive software that should include the best from both the systems and user-centered designs. In that combination, the software architectures should enclose the fundamental structure and ideas of the system to offer the desired quality based on sound design decisions. Moreover, the information kept within a system is an opportunity to extract knowledge about the system itself, its components, the software included, the users or the interaction occurring inside. The knowledge gained from the information generated in a software environment can be used to improve the system itself, its software, the users’ experience, and the results. So, the combination of the areas of Knowledge Discovery and Human-Computer Interaction offers ideal conditions to address Human-Computer-Interaction-related challenges. The Human-Computer Interaction focuses on human intelligence, the Knowledge Discovery in computational intelligence, and the combination of both can raise the support of human intelligence with machine intelligence to discover new insights in a world crowded of data. This Ph.D. Thesis deals with these kinds of challenges: how approaches like data-driven software architectures (using Knowledge Discovery techniques) can help to improve the users' interaction and experience within an interactive system. Specifically, it deals with how to improve the human-computer interaction processes of different kind of stakeholders to improve different aspects such as the user experience or the easiness to accomplish a specific task. Several research actions and experiments support this investigation. These research actions included performing a systematic literature review and mapping of the literature that was aimed at finding how the software architectures in the literature have been used to support, analyze or enhance the human-computer interaction. Also, the actions included work on four different research scenarios that presented common challenges in the Human-Computer Interaction knowledge area. The case studies that fit into the scenarios selected were chosen based on the Human-Computer Interaction challenges they present, and on the authors’ accessibility to them. The four case studies were: an educational laboratory virtual world, a Massive Open Online Course and the social networks where the students discuss and learn, a system that includes very large web forms, and an environment where programmers develop code in the context of quantum computing. The development of the experiences involved the review of more than 2700 papers (only in the literature review phase), the analysis of the interaction of 6000 users in four different contexts or the analysis of 500,000 quantum computing programs. As outcomes from the experiences, some solutions are presented regarding the minimal software artifacts to include in software architectures, the behavior they should exhibit, the features desired in the extended software architecture, some analytic workflows and approaches to use, or the different kinds of feedback needed to reinforce the users’ interaction and experience. The results achieved led to the conclusion that, despite this is not a standard practice in the literature, the software environments should embrace Knowledge Discovery and data-driven principles to analyze and respond appropriately to the users’ needs and improve or support the interaction. To adopt Knowledge Discovery and data-driven principles, the software environments need to extend their software architectures to cover also the challenges related to Human-Computer Interaction. Finally, to tackle the current challenges related to the users’ interaction and experience and aiming to automate the software response to users’ actions, desires, and behaviors, the interactive systems should also include intelligent behaviors through embracing the Artificial Intelligence procedures and techniques