Education
Editors: Gitta Domik and Scott Owen
Information Visualization Courses for Students with a Computer Science Background Andreas Kerren Linnaeus University
E
ducation is important to any emerging and rapidly evolving discipline. This is certainly the case for information visualization, with its emphasis on the exploratory development of knowledge. Over the past years, the visual analysis of data has become the focus of many people in academia and industry. Consequently, students are more aware of visualization techniques’ importance in addressing problems such as the big-data challenge or information overload. This is reflected by the increasing number of students who want to register for information visualization courses at Linnaeus University (LNU). We started with only six students in 2007; today, we regularly have more than 20 students registered for our basic information visualization course. Teaching information visualization poses two main challenges. On one hand, we must introduce suitable technical foundations that require previous knowledge in various areas of computer science, such as computer graphics, mathematics, or human-computer interaction. On the other hand, teaching principles of visualization users’ perceptual and cognitive capabilities is equally important and a prerequisite for developing effective, useful visualization approaches. We’ve addressed these challenges in two information visualization courses for computer science students with programming experience. Here, we briefly describe the syllabi, exercises, and practices we developed for these courses. Similar reports exist for related fields such as visual analytics1 or scientific visualization.2
The LNU academic year runs on the quarter system. We offer two master’s courses (http://cs.lnu. se/isovis/courses) that each take place in the winter and spring terms: March/April 2013
■■
Information Visualization (7.5 ECTS credits) and Applied Information Visualization (7.5 ECTS credits).
(ECTS stands for European Credit Transfer and Accumulation System.) We’ve provided these courses since 2008. (In 2007, for organizational reasons, a general course called Current Topics in Computer Science covered the first course’s content.) Originally, we called these courses Information Visualization I and II. We renamed them because we noticed that students tend to avoid courses with higher numbers. Each course consists of 10 90-minute lectures; lectures occur once or twice a week. Students must have programming knowledge to finish the practical exercises successfully. Information Visualization requires 90 credits in computer science, including basic courses in programming and data structures and computer graphics, or the equivalent. Applied Information Visualization requires successful completion of Information Visualization or an equivalent. Despite the relatively high requirements, motivating students to take these courses isn’t difficult. The courses’ general learning goals are that students should ■■
■■
■■
The Courses
12
■■
■■
■■
understand the basic principles that have influenced information visualization tools’ development (such as perception or visualization pipelines), have an overview of fundamental techniques and systems, be able to choose suitable visualization techniques for specific tasks, be able to critically evaluate and improve various approaches, and have the background knowledge necessary to develop new, innovative visualizations.
Published by the IEEE Computer Society
0272-1716/13/$31.00 © 2013 IEEE
Table 1. Information Visualization Syllabus. It’s important to give students the opportunity for critical reflection and to show the most recent research directions. We discuss each technical approach and tool with respect to its value, usefulness, and (if applicable) commercial use. Of course, this is challenging and sometimes a bit subjective because of missing quality metrics or missing evaluations of tools and techniques.
The Syllabi Many ways of organizing the course content exist.3 Our courses’ syllabi (see Tables 1 and 2) were influenced partly by courses at Georgia Tech and the Technical University of Vienna and by standard textbooks such as Information Visualization: Perception for Design4 and Readings in Information Visualization: Using Vision to Think.5 We also recommend other books or chapters to students for further reading and enrichment—for example, Human-Centered Visualization Environments6 and Information Visualization: Design for Interaction.7
Lecture
Topic
1
Introduction and motivation
2&3
Perception theory and cognition
4
Information visualization basics
5–7
Interaction
8
1D, 2D, 3D, and multidimensional data visualization
9 & 10
Hierarchy visualization
Table 2. Applied Information Visualization Syllabus. Lecture
Topic
1
Introduction and information visualization toolkits
2
Text and document visualization
3
Visualization of networks, including applications
4
Web visualization and biological-data visualization
5
Time series visualization
6&7
Software visualization
8
Visualizations for end users and collaboration
9
Visual analytics
10
Evaluation and the top 10 information visualization challenges
Information Visualization This course’s first four lectures discuss basic knowledge important for designing information visualizations and analyzing information visualization concepts. We introduce the field, give motivations for it, and present traditional and modern examples. Then, we discuss perception and cognitive issues. We provide information about the perception of colors and textures, preattentive visual processing, and Gestalt laws. This course component is based mainly on Information Visualization: Perception for Design. The fourth lecture describes basics such as the information visualization reference model (data tables, visual mapping, interaction, and so on) and data types and dimensionality. This lecture is based mostly on Readings in Information Visualization: Using Vision to Think. The remaining six lectures mainly present technical fundamentals. We first explain interaction and then the visual structures for different data types; this lets us directly refer to interaction techniques without additional explanations. Students accept this order and have no problem understanding the differences or correlations. We discuss the most important interaction techniques—for example, dynamic queries, zoom and pan, overview and detail, and techniques related to focus plus context. To exemplify the techniques, we use both early and recent research papers. The early papers often give more focused examples; the recent papers present more complex or hybrid techniques.
The lectures on visual structures introduce visualization techniques for multivariate data (projection-, axis-, glyph-, and pixel-based techniques) and hierarchies (node-link and space-filling approaches), mostly on the basis of research papers. We save special data types for the next course.
Applied Information Visualization This course’s first lecture summarizes the first course’s most important elements and presents current toolkits such as D3 (http://d3js.org) or Processing (http://processing.org), which the students can use in the practical exercises. The course focuses on special cases and domains and the practical application of the knowledge gained in the first course. So, we address specific data types, such as text and networks, and continue with visualization techniques for domains such as biological data, the Web, and software. Resources for these lectures are current papers and articles and the second part of Human-Centered Visualization Environments. Recently, we added a lecture on visualization for end users and collaboration and a lecture on visual analytics. We did this because of our own research interests and these fields’ increasing importance and popularity. The course concludes with a short presentation on evaluation (methodologies and qualitative and quantitative techniques) and the most important information visualization challenges. The discussion of challenges gives an idea of the field’s current state and open problems. Furthermore, IEEE Computer Graphics and Applications
13
Education
it regularly motivates some students to choose a thesis topic offered by our research group.
Study Materials Our lecture notes are based mainly on our PowerPoint slides; students can download them from the course webpages (access is restricted for copyright reasons). The lectures cite many research papers, some of which we selected from peer-reviewed conference proceedings or journals for compulsory assigned reading. These readings provide further insight into a specific topic, such as software visualization or visual analytics. Videos of interaction scenarios showing information visualization tools’ usability and interaction capabilities are essential, as are tool demos. They’re especially necessary to explain the different interaction techniques and their interplay with visual structures. It’s fun to keep an eye on the students during such demonstrations, and these activities motivate the students to ask deeper questions. However, videos and demonstration tools aren’t always available. One large collection of educational resources is Georgia Tech’s HCC (Human-Centered Computing) Education Digital Library (http://hccedl.cc.gatech.edu). Such repositories also facilitate the students’ preparation for lectures and exams because they can watch the videos again at home. Currently, we provide our videos via Blackboard, a course management system that LNU uses.
Exercises and Grading Both courses are supported by a teaching assistant (TA) who designs and organizes the exercises in close collaboration with the responsible instructor. For Information Visualization, the exercises are more theoretical and conceptual. For example, students may choose from several interesting datasets (for instance, publicly available election results or finance data). Then, they choose a suitable information visualization technique or combination of techniques and discuss their design choices’ pros and cons. These exercises typically result in a brief written report evaluating the techniques and their effectiveness. Another typical exercise involves considering perception theory’s concrete consequences for visualization approaches. The last exercise usually involves pairs of students implementing a simple technique—for example, a basic treemap visualization with a few interaction features. In Applied Information Visualization, the students implement a more complex visualization tool with more advanced interaction possibilities. So, the exercises are actually small software 14
March/April 2013
projects. First, pairs of students get a nontrivial (typically multivariate) dataset from the TA. They give a short presentation explaining their fundamental visualization idea and the planned implementation. This presentation prevents them from focusing too much on low-level details and ensures that the resulting tools are effective. After implementing the tools, the students present and discuss them. Over the past years, we’ve often changed the courses’ exercises. In 2007, each student chose a research paper describing a recent approach, from a group of papers that we preselected. The student then reimplemented the fundamental approach (not all the functionalities) and presented the resulting tool. The instructors liked this exercise, primarily because it related closely to their research, but it was too challenging for many students. The evaluation of theoretical exercises is straightforward but contains a nontrivial justification by the TA. There are often several correct answers or no correct answer for a specific task, such as identifying a design choice’s advantages and disadvantages. This leads to more complex discussions with students and more detailed feedback. We grade the practical exercises on the basis of the implementation’s overall quality. That is, the instructor and TA evaluate the time needed, the aesthetic aspects, the level of effort (complexity), the usability, the useful features the tool provides to analyze the chosen dataset, and the oral presentation’s quality. Our courses end with a 30-minute individual oral exam. The final grade is a combination of the exercise and exam grades.
Course Reflection and Success Indicators A key challenge of teaching these courses is the students’ international background. Each student brings his or her own knowledge and level of education, even if he or she has fulfilled the course requirements and has a solid background in computer science or related disciplines. In this context, the lectures and oral exam aren’t a problem for students, but many students find the exercises challenging. You might expect that culture-specific habits or language characteristics might lead to problems—for instance, in the description and meaning of colors. However, we’ve never considered those issues a problem. An exception might be the students’ different experiences with tasks demanding critical thinking. On the other hand, their math background and programming skills might influence the exercise results more. Student feedback has been positive overall, especially regarding the course content. A further
Figure 1. The Network Lens supports the visualization of multivariate node attributes in the context of an underlying network. The student thesis on this topic led to a published conference paper.9
success indicator is our students’ interest in continuing with a thesis related to our information visualization research. After they finish their theses, we encourage the best students to coauthor papers for publication. Those papers are regularly accepted as posters or fully fledged research papers at well-known visualization conferences.8,9 Figure 1 shows a screenshot of a thesis project. Another positive development is the increasing interest in our courses from students from other fields, such as signal processing or informatics. This interest probably results from information visualizations’ increased presence on the Internet. Because those students typically have no programming skills, they can’t register for the courses. We’re thinking about changing the requirements for Information Visualization and modifying the exercises so that students without a computer science background can take it.
W
e hope this account of our experiences will help others to set up information visualization courses or to relate their own courses to ours.
References 1. N. Elmqvist and D.S. Ebert, “Leveraging Multi disciplinarity in a Visual Analytics Graduate Course,” IEEE Computer Graphics and Applications, vol. 32, no. 3, 2012, pp. 84–87. 2. M. Rotard, D. Weiskopf, and T. Ertl, “Curriculum for a Course on Scientific Visualization,” 2004;
www.vis.uni-stuttgart.de/~weiskopf/publications/ cge_vis04.pdf. 3. A. Kerren, J.T. Stasko, and J. Dykes, “Teaching Information Visualization,” Information Visualization: Human-Centered Issues and Perspectives, LNCS 4950, Springer, 2008, pp. 65–91. 4. C. Ware, Information Visualization: Perception for Design, 2nd ed., Morgan Kaufmann, 2004. 5. S. Card, J. Mackinlay, and B. Shneiderman, eds., Readings in Information Visualization: Using Vision to Think, Morgan Kaufmann, 1999. 6. A. Kerren, A. Ebert, and J. Meyer, eds., HumanCentered Visualization Environments, LNCS 4417, Springer, 2007. 7. R. Spence, Information Visualization: Design for Inter action, 2nd ed., Prentice Hall, 2007. 8. I. Jusufi, A. Kerren, and Y. Wang, “A New Radial Space-Filling Visualization Approach for Planar stGraphs,” poster abstract presented at 2012 IEEE VisWeek, 2012; http://cs.lnu.se/isovis/pubs/docs/ kerren-visweek12.pdf. 9. I. Jusufi, Y. Dingjie, and A. Kerren, “The Network Lens: Interactive Exploration of Multivariate Networks Using Visual Filtering,” Proc. 14th Int’l Conf. Information Visualization (IV 10), IEEE CS, 2010, pp. 35–42. Andreas Kerren is a professor of computer science at Linnaeus University’s School of Computer Science, Physics and Mathematics. He also heads the university’s Information and Software Visualization group. Contact him at kerren@ acm.org. Contact department editors Gitta Domik at domik@ uni-paderborn.de and Scott Owen at [email protected]. IEEE Computer Graphics and Applications
15
Editors: Gitta Domik and Scott Owen
Information Visualization Courses for Students with a Computer Science Background Andreas Kerren Linnaeus University
E
ducation is important to any emerging and rapidly evolving discipline. This is certainly the case for information visualization, with its emphasis on the exploratory development of knowledge. Over the past years, the visual analysis of data has become the focus of many people in academia and industry. Consequently, students are more aware of visualization techniques’ importance in addressing problems such as the big-data challenge or information overload. This is reflected by the increasing number of students who want to register for information visualization courses at Linnaeus University (LNU). We started with only six students in 2007; today, we regularly have more than 20 students registered for our basic information visualization course. Teaching information visualization poses two main challenges. On one hand, we must introduce suitable technical foundations that require previous knowledge in various areas of computer science, such as computer graphics, mathematics, or human-computer interaction. On the other hand, teaching principles of visualization users’ perceptual and cognitive capabilities is equally important and a prerequisite for developing effective, useful visualization approaches. We’ve addressed these challenges in two information visualization courses for computer science students with programming experience. Here, we briefly describe the syllabi, exercises, and practices we developed for these courses. Similar reports exist for related fields such as visual analytics1 or scientific visualization.2
The LNU academic year runs on the quarter system. We offer two master’s courses (http://cs.lnu. se/isovis/courses) that each take place in the winter and spring terms: March/April 2013
■■
Information Visualization (7.5 ECTS credits) and Applied Information Visualization (7.5 ECTS credits).
(ECTS stands for European Credit Transfer and Accumulation System.) We’ve provided these courses since 2008. (In 2007, for organizational reasons, a general course called Current Topics in Computer Science covered the first course’s content.) Originally, we called these courses Information Visualization I and II. We renamed them because we noticed that students tend to avoid courses with higher numbers. Each course consists of 10 90-minute lectures; lectures occur once or twice a week. Students must have programming knowledge to finish the practical exercises successfully. Information Visualization requires 90 credits in computer science, including basic courses in programming and data structures and computer graphics, or the equivalent. Applied Information Visualization requires successful completion of Information Visualization or an equivalent. Despite the relatively high requirements, motivating students to take these courses isn’t difficult. The courses’ general learning goals are that students should ■■
■■
■■
The Courses
12
■■
■■
■■
understand the basic principles that have influenced information visualization tools’ development (such as perception or visualization pipelines), have an overview of fundamental techniques and systems, be able to choose suitable visualization techniques for specific tasks, be able to critically evaluate and improve various approaches, and have the background knowledge necessary to develop new, innovative visualizations.
Published by the IEEE Computer Society
0272-1716/13/$31.00 © 2013 IEEE
Table 1. Information Visualization Syllabus. It’s important to give students the opportunity for critical reflection and to show the most recent research directions. We discuss each technical approach and tool with respect to its value, usefulness, and (if applicable) commercial use. Of course, this is challenging and sometimes a bit subjective because of missing quality metrics or missing evaluations of tools and techniques.
The Syllabi Many ways of organizing the course content exist.3 Our courses’ syllabi (see Tables 1 and 2) were influenced partly by courses at Georgia Tech and the Technical University of Vienna and by standard textbooks such as Information Visualization: Perception for Design4 and Readings in Information Visualization: Using Vision to Think.5 We also recommend other books or chapters to students for further reading and enrichment—for example, Human-Centered Visualization Environments6 and Information Visualization: Design for Interaction.7
Lecture
Topic
1
Introduction and motivation
2&3
Perception theory and cognition
4
Information visualization basics
5–7
Interaction
8
1D, 2D, 3D, and multidimensional data visualization
9 & 10
Hierarchy visualization
Table 2. Applied Information Visualization Syllabus. Lecture
Topic
1
Introduction and information visualization toolkits
2
Text and document visualization
3
Visualization of networks, including applications
4
Web visualization and biological-data visualization
5
Time series visualization
6&7
Software visualization
8
Visualizations for end users and collaboration
9
Visual analytics
10
Evaluation and the top 10 information visualization challenges
Information Visualization This course’s first four lectures discuss basic knowledge important for designing information visualizations and analyzing information visualization concepts. We introduce the field, give motivations for it, and present traditional and modern examples. Then, we discuss perception and cognitive issues. We provide information about the perception of colors and textures, preattentive visual processing, and Gestalt laws. This course component is based mainly on Information Visualization: Perception for Design. The fourth lecture describes basics such as the information visualization reference model (data tables, visual mapping, interaction, and so on) and data types and dimensionality. This lecture is based mostly on Readings in Information Visualization: Using Vision to Think. The remaining six lectures mainly present technical fundamentals. We first explain interaction and then the visual structures for different data types; this lets us directly refer to interaction techniques without additional explanations. Students accept this order and have no problem understanding the differences or correlations. We discuss the most important interaction techniques—for example, dynamic queries, zoom and pan, overview and detail, and techniques related to focus plus context. To exemplify the techniques, we use both early and recent research papers. The early papers often give more focused examples; the recent papers present more complex or hybrid techniques.
The lectures on visual structures introduce visualization techniques for multivariate data (projection-, axis-, glyph-, and pixel-based techniques) and hierarchies (node-link and space-filling approaches), mostly on the basis of research papers. We save special data types for the next course.
Applied Information Visualization This course’s first lecture summarizes the first course’s most important elements and presents current toolkits such as D3 (http://d3js.org) or Processing (http://processing.org), which the students can use in the practical exercises. The course focuses on special cases and domains and the practical application of the knowledge gained in the first course. So, we address specific data types, such as text and networks, and continue with visualization techniques for domains such as biological data, the Web, and software. Resources for these lectures are current papers and articles and the second part of Human-Centered Visualization Environments. Recently, we added a lecture on visualization for end users and collaboration and a lecture on visual analytics. We did this because of our own research interests and these fields’ increasing importance and popularity. The course concludes with a short presentation on evaluation (methodologies and qualitative and quantitative techniques) and the most important information visualization challenges. The discussion of challenges gives an idea of the field’s current state and open problems. Furthermore, IEEE Computer Graphics and Applications
13
Education
it regularly motivates some students to choose a thesis topic offered by our research group.
Study Materials Our lecture notes are based mainly on our PowerPoint slides; students can download them from the course webpages (access is restricted for copyright reasons). The lectures cite many research papers, some of which we selected from peer-reviewed conference proceedings or journals for compulsory assigned reading. These readings provide further insight into a specific topic, such as software visualization or visual analytics. Videos of interaction scenarios showing information visualization tools’ usability and interaction capabilities are essential, as are tool demos. They’re especially necessary to explain the different interaction techniques and their interplay with visual structures. It’s fun to keep an eye on the students during such demonstrations, and these activities motivate the students to ask deeper questions. However, videos and demonstration tools aren’t always available. One large collection of educational resources is Georgia Tech’s HCC (Human-Centered Computing) Education Digital Library (http://hccedl.cc.gatech.edu). Such repositories also facilitate the students’ preparation for lectures and exams because they can watch the videos again at home. Currently, we provide our videos via Blackboard, a course management system that LNU uses.
Exercises and Grading Both courses are supported by a teaching assistant (TA) who designs and organizes the exercises in close collaboration with the responsible instructor. For Information Visualization, the exercises are more theoretical and conceptual. For example, students may choose from several interesting datasets (for instance, publicly available election results or finance data). Then, they choose a suitable information visualization technique or combination of techniques and discuss their design choices’ pros and cons. These exercises typically result in a brief written report evaluating the techniques and their effectiveness. Another typical exercise involves considering perception theory’s concrete consequences for visualization approaches. The last exercise usually involves pairs of students implementing a simple technique—for example, a basic treemap visualization with a few interaction features. In Applied Information Visualization, the students implement a more complex visualization tool with more advanced interaction possibilities. So, the exercises are actually small software 14
March/April 2013
projects. First, pairs of students get a nontrivial (typically multivariate) dataset from the TA. They give a short presentation explaining their fundamental visualization idea and the planned implementation. This presentation prevents them from focusing too much on low-level details and ensures that the resulting tools are effective. After implementing the tools, the students present and discuss them. Over the past years, we’ve often changed the courses’ exercises. In 2007, each student chose a research paper describing a recent approach, from a group of papers that we preselected. The student then reimplemented the fundamental approach (not all the functionalities) and presented the resulting tool. The instructors liked this exercise, primarily because it related closely to their research, but it was too challenging for many students. The evaluation of theoretical exercises is straightforward but contains a nontrivial justification by the TA. There are often several correct answers or no correct answer for a specific task, such as identifying a design choice’s advantages and disadvantages. This leads to more complex discussions with students and more detailed feedback. We grade the practical exercises on the basis of the implementation’s overall quality. That is, the instructor and TA evaluate the time needed, the aesthetic aspects, the level of effort (complexity), the usability, the useful features the tool provides to analyze the chosen dataset, and the oral presentation’s quality. Our courses end with a 30-minute individual oral exam. The final grade is a combination of the exercise and exam grades.
Course Reflection and Success Indicators A key challenge of teaching these courses is the students’ international background. Each student brings his or her own knowledge and level of education, even if he or she has fulfilled the course requirements and has a solid background in computer science or related disciplines. In this context, the lectures and oral exam aren’t a problem for students, but many students find the exercises challenging. You might expect that culture-specific habits or language characteristics might lead to problems—for instance, in the description and meaning of colors. However, we’ve never considered those issues a problem. An exception might be the students’ different experiences with tasks demanding critical thinking. On the other hand, their math background and programming skills might influence the exercise results more. Student feedback has been positive overall, especially regarding the course content. A further
Figure 1. The Network Lens supports the visualization of multivariate node attributes in the context of an underlying network. The student thesis on this topic led to a published conference paper.9
success indicator is our students’ interest in continuing with a thesis related to our information visualization research. After they finish their theses, we encourage the best students to coauthor papers for publication. Those papers are regularly accepted as posters or fully fledged research papers at well-known visualization conferences.8,9 Figure 1 shows a screenshot of a thesis project. Another positive development is the increasing interest in our courses from students from other fields, such as signal processing or informatics. This interest probably results from information visualizations’ increased presence on the Internet. Because those students typically have no programming skills, they can’t register for the courses. We’re thinking about changing the requirements for Information Visualization and modifying the exercises so that students without a computer science background can take it.
W
e hope this account of our experiences will help others to set up information visualization courses or to relate their own courses to ours.
References 1. N. Elmqvist and D.S. Ebert, “Leveraging Multi disciplinarity in a Visual Analytics Graduate Course,” IEEE Computer Graphics and Applications, vol. 32, no. 3, 2012, pp. 84–87. 2. M. Rotard, D. Weiskopf, and T. Ertl, “Curriculum for a Course on Scientific Visualization,” 2004;
www.vis.uni-stuttgart.de/~weiskopf/publications/ cge_vis04.pdf. 3. A. Kerren, J.T. Stasko, and J. Dykes, “Teaching Information Visualization,” Information Visualization: Human-Centered Issues and Perspectives, LNCS 4950, Springer, 2008, pp. 65–91. 4. C. Ware, Information Visualization: Perception for Design, 2nd ed., Morgan Kaufmann, 2004. 5. S. Card, J. Mackinlay, and B. Shneiderman, eds., Readings in Information Visualization: Using Vision to Think, Morgan Kaufmann, 1999. 6. A. Kerren, A. Ebert, and J. Meyer, eds., HumanCentered Visualization Environments, LNCS 4417, Springer, 2007. 7. R. Spence, Information Visualization: Design for Inter action, 2nd ed., Prentice Hall, 2007. 8. I. Jusufi, A. Kerren, and Y. Wang, “A New Radial Space-Filling Visualization Approach for Planar stGraphs,” poster abstract presented at 2012 IEEE VisWeek, 2012; http://cs.lnu.se/isovis/pubs/docs/ kerren-visweek12.pdf. 9. I. Jusufi, Y. Dingjie, and A. Kerren, “The Network Lens: Interactive Exploration of Multivariate Networks Using Visual Filtering,” Proc. 14th Int’l Conf. Information Visualization (IV 10), IEEE CS, 2010, pp. 35–42. Andreas Kerren is a professor of computer science at Linnaeus University’s School of Computer Science, Physics and Mathematics. He also heads the university’s Information and Software Visualization group. Contact him at kerren@ acm.org. Contact department editors Gitta Domik at domik@ uni-paderborn.de and Scott Owen at [email protected]. IEEE Computer Graphics and Applications
15
