Ess Visualization Syllabus2

ESS 33634-01: Data Visualization, Society, and Student Learning (3 cr.) Syllabus: Spring 2019 Instructor: Christine Trinter Phone: (574) 631-5763 Email: [email protected]

Meeting Dates, Time, and Location: January 15th – April 30th 11:00am – 12:15pm Flanner Hall, Room 824

Teaching Assistant: Catherine Floyd Phone (574) 631-1712 Email: [email protected]

Course Website: www.vizsocietystudentlearning.weebly.com

Course Description as listed in the Graduate Bulletin Students in today’s information age are consuming greater amounts of quantitative information on a daily basis than ever before. This information comes in many forms and typically involves large data sets that tell “number stories” such as social media activity, politics, global health concerns, and educational achievement. How those number stories are displayed, numerically, text narrative, or graphically holds both power and peril. In this course, we will consider the evolution of visual displays of quantitative information, analyze the characteristics of visualizations, and explore the ways in which the interaction between data and design influence the communication of a number story. These ideas will be investigated through the lens of societal issues and student learning. Course Goals and Instructor Philosophy The intent of this course is to encourage you to consider the many ways of envisioning quantitative information. In particular, I hope to create situations that will encourage you to reflect upon and analyze (1) the evolution of visual displays of quantitative information, (2) how people make sense of data, (3) what it means to articulate a data story, and (4) the interaction between data and design. You will be introduced to some of the current thinking and literature concerning data visualization. We will explore foundational principles behind data graphics and ask ourselves why we should be interested in visualizing data. The study of semiotics and socio-cultural theory will inform our thinking about how people learn and perceive data stories. During this class, you will learn how design influences messaging with particular attention to color, pattern, space, and image. We will decipher the differences between various types of quantitative information and how representation plays a role in meaning making. These ideas will be applied to authentic contexts with particular emphasis on formal and informal learning environments such as school-based disciplines and societal issues. The perspective of this course takes an inquiry-based approach to learning. This viewpoint, based in Piagetian developmental theory, assumes that the learner constructs knowledge based on experiences and his or her reflective thought about those experiences. Learning is an active process and reflecting on those experiences is an important part of constructing knowledge. This view of learning is especially appropriate to the study of visualization because the complexity of these representations calls upon relationships that must be constructed in the learner’s mind and not just “looked at” or memorized. Considering this viewpoint, you will be expected to contribute significantly to class discussions. I will present artifacts, assign readings and facilitate class

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discussions. Together we will create a class atmosphere conducive to analysis, reflection, and the sharing of ideas. It is my hope that through this course, you will become comfortable with challenging your own thinking around the meaning of data and how to contribute to data storytelling in effective ways. It is my hope that this course will not only provide answers to questions but leave you with more questions to answer. Attendance Policy and Class Participation It is important to attend class regularly and to come to class having completed the day’s assignment in order to benefit from the course and to obtain the information the course provides. In order to allow for sickness, representations of the university, religious holidays, travel problems due to inclement weather, etc. one absence will be allowed without penalty to your final course grade. Each absence after this one will reduce your final course grade by 5 points. Excessive tardiness (or leaving class early) will be counted as absences. If class is canceled due to inclement weather, we will keep on schedule and I will answer any questions you may have on the material that was not discussed. If you have any extenuating circumstances that prohibit you from attending class, please contact the instructor at your earliest convenience so we can make other arrangements to keep you up to date with the material. Class participation entails the following: × Regular attendance (see policy above) × Active daily participation through asking questions, engaging in group activities, presenting to your peers, answering questions and staying on task. × Come to class prepared to share, question, and be actively involved Honor Code All individuals are expected to follow the principles outlined in the Notre Dame Honor Code. Special Needs Individuals with any exceptionalities or special learning needs are encouraged to contact the instructor within the first few days of class so that appropriate accommodations and/or modifications may be put into place. Assignments and Evaluation The grading scale (listed below) for this course is taken from the Undergraduate Academic Code. All assignments are due at the beginning of class on the date indicated. LATE assignments may be turned in up to 24 hours after the due date with a loss of one-half of a letter grade. After 24 hours, no assignment will be accepted (unless there are extenuating circumstances that you discussed with the instructor prior to the due date). Please type your assignments, as appropriate. No assignment that contains multiple spelling, punctuation, and/or sentence or paragraph structure errors will receive a grade of A. Letter Grade A AB+

Numeric Grade 94+ 90-93 87-89

Explanatory Comments Work meets or exceeds the highest expectations for the course Superior work in all areas of the course Superior work in most areas of the course

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B BC+ C CD F

83-86 80-82 77-79 73-76 70-72 60-69 59-

Solid work across the board More than acceptable, but falls short of solid work Work meets all the basic requirements and standards in several areas Work meets most of the basic requirements and standards in several areas While acceptable, work falls short of meeting basic standards in several areas Work just over the threshold of acceptability Unacceptable performance

Assignment Weighting Readings and Discussion Participation

17%

Reading Reflections

13%

Individual Explorations

20%

Visualization Analysis

15%

Telling a Data Story

15%

Final Project and Presentation

20%

Assignment Descriptions Data takes many forms and hence, visualizations can be generated on a wide range of topics. As part of this course, you will be asked to both find and react to visualizations. You will also be asked to generate a graphic using data of your choosing. Indeed, it would benefit you to identify a topic that you are interested in to focus your thinking when it comes time to make these choices. While it is not required to center all of your choice assignments on one topic, doing so may help you work efficiently. While this course will include a wide range of issues in the form of visualizations, you are encouraged to choose a topic that pertains to society and/or schooling. All assignments should be submitted through Sakai by 11am (class time) on the due date. This includes reading reflections and individual explorations. One of the most important course requirements is to take pride in your work! Readings and Discussion Participation. I believe that a student learns by doing, by participating and by reflecting on assignments and discussions. Discussion topics are listed by date. Readings related to each topic should be completed prior to the date identified. Throughout the course, readings will be assigned from various journals, websites, and books chosen by the instructor. All readings will be made available to you on the course website. Readings will either take .pdf format, website link or a chapter from a book that is available through the Hesburgh library. You will be expected to have the reading completed and be prepared to discuss and apply the content in class. Furthermore, you will be required to lead one class discussion based on the reading. Discussion participation will be worth 13% and discussion lead is worth 4%. The list of discussion leaders will be distributed at the end of our first week of classes. Reading Reflections. You will be assigned approximately two readings for each of the first 13 weeks of class. Choose one of the two pieces and write a 1-2 page reflection. Your reflection

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should be typed, double spaced and use 12-point Times New Roman font. Reading reflections are due each Thursday by 11am. Please include the following information: × Your name and due date × Article citation in APA format × A brief paragraph providing an overview of the reading Choose at least two of the following questions to answer in your reflection: × What did you learn from the reading? × Did the reading change your thinking? If so, in what ways? × How did this reading reflect principles of data visualization? × How did this reading influence your thinking about your current line of study (major), if at all? × As you consider your final project, what takeaways from this reading might inform your work? × Consider this author’s perspective and compare it against another author’s perspective from an earlier reading. × Do the design principles described in this reading speak to a specific data type and if so, how? × How does this reading make use of exemplary visualizations and in what way did these examples influence your thinking? Individual Explorations. There are four workshop days spread out over the course of the semester for individual exploration. We will not meet as a class on these days. These workshop days provide you the opportunity to use the ideas you learned from our class and apply them to a topic of your choosing. The assignments are due by 11am on the dates listed below. Detailed descriptions and evaluation criteria for each assignment will be handed out separately. Workshop Day 1 (Jan. 17th): Visualization Exploration. Due January 22nd Workshop Day 2 (Feb. 7th): Design Part I. Due February 12th Workshop Day 3: (Feb. 21st): Design Part II. Due February 26th Workshop Day 4: (March 7th): Number Stories. Due March 21th Visualization Analysis. Over the course of the semester, you will learn principles for effectively communicating information using graphic representations. For this assignment, you will choose one visualization (from a suite of choices) to evaluate using a given analysis protocol. Please see assignment handout for additional details and evaluation criteria. Due by 11am on March 5th. Telling a Data Story. This assignment gives you the opportunity to try your hand at creating a visualization of your own! You will be given a choice of data sets from which to develop your own data story. Your graphic must include a justification for your choices. You will be paired with a peer who will provide feedback before final submission. Please see assignment handout for additional details and evaluation criteria. Due by 11am on April 2nd, Final product due on April 16th. Final Project and Presentation. Your final project and presentation are a culmination of the ideas explored throughout the semester. This project includes a brief literature review of the selected issue, data supporting the issue, and a visualization with justification for design rationale.

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You will be asked to present your project to the class and field questions about your project. You may be interested in designing your own visualization or you may prefer to modify a visualization designed by someone else. Therefore, you have a choice between each of these options, as described below. You are welcome to work with a partner or individually. All projects will be initiated by a project proposal that must be approved by the instructor. Please choose one of the following two projects (additional details will be provided on a separate assignment handout): Option 1: Choose a topic that interests you, pertaining to society or schooling, and audience you believe would benefit from a data graphic on this topic. Research the context and existing work about this topic. Gather relevant data to be used in generating a visual for your project. Create a visual for your audience and a justification for the choices you made when generating your graphic. Prepare a presentation for the class containing brief information about the topic, context, audience and the graphic. Be prepared to answer questions about your visual. Option 2: Choose a topic that interests you, pertaining to society or schooling, and an existing data graphic pertaining to this topic. Research the context and existing work about this topic. Examine the data used to create this graphic (as appropriate given accessibility limitations). Decide whether you would like to maintain the same audience this graphic was created for or choose a different audience. Re-create the visual for your audience and a justification for the choices you made when generating your graphic. Prepare a presentation for the class containing brief information about the topic, context, audience and the graphic. Be prepared to answer questions about your visual. Option 3: Re-design the Conceptual Framework image on the University of Notre Dame STEM Center website. Research the context and existing work about this topic (Prof. Trinter will connect you with STEM Center faculty and staff). During your data collection, determine the audience this graphic was created for and determine if there is a wider audience or if the Center should maintain the same goal audience. Re-create the visual for the audience and a justification for the choices you made when generating your graphic. Prepare a presentation for the class containing brief information about the topic, context, audience and the graphic. Be prepared to answer questions about your visual. Project Proposal is Due by 11am on March 28th Final Projects and Presentations are Due by 11am on April 23rd Project Proposal should include the following (using provided template): 1. Option number and topic chosen. 2. Plan for researching the context and existing work. 3. Plan for accessing the data.

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Class Schedule Topic

Assignments Due

Readings Due

January

Foundation

Learning and Sensemaking

15

What is data visualization?

17

Workshop Day 1: Visualization Exploration

22

Why should we be interested in visualization?

24

Semiotics

Ware, C. (2013) Arnheim, R. (1969)

29

Visualization and Cognition

Ware, C. (2013)

31

Visualization and Cognition

Mayer, R. & Moreno, R. (2003)

5

Color

Silva, S., Santos, B.S. & Madeira, J. (2011).

7

Workshop Day 2: Design and Perception

12

Static and Moving Patterns

14

Space

Ware, C. (2013).

19

Objects

Ware, C. (2013).

21

Workshop Day 3: Design and Perception

26

Data Ink

28

Perception and Design

Tufte, E.R. (2001) Individual Exploration Workshop 1

Tufte, E.R. (2001)

February

Perception and Storytelling

Individual Exploration Workshop 2

Individual Exploration Workshop 3

Ware, C. (2013).

Tufte, E.R. (2001)

Cleveland, W. & McGill, R. (1984).

6

March

Quantitative Information and Representation

5

Narrative Visualization

Visualization Analysis

Segel, E. and Heer, J. (2010)

7

Workshop Day 4: Number Stories

12

SPRING BREAK

14

SPRING BREAK

19

Types of quantities and representations (when)

Individual Exploration Workshop 4

Borner, K. & Polley, D. E. (2014).

21

Types of quantities and representations (where)

Borner, K. & Polley, D. E. (2014).

26

Types of quantities and representations (what)

Borner, K. & Polley, D. E. (2014).

28

Types of quantities and representations (with whom)

Borner, K. & Polley, D. E. (2014).

2

More than just bars, lines and pies

4

Chartjunk

Tufte, E.R. (2001).

9

Visualization and Learning in Formal Schooling

Rieber, L. (1995) Arcavi, A. (2003)

11

Visualization and Learning in Formal Schooling

Chiu, J. & Linn, M.C. (2014)

16

Societal Implications

Tufte, E.R. (2001)

18

Societal Implications

New York Times Upshot and Washington Post

23

Presentations

25

NO CLASS

30

Presentations

April

Applications

Telling A Data Story

Lile, S. Elliott, K. (2017).

Final Projects and Presentations

Final Projects and Presentations

7

Readings Organized by Due Date Topic

Readings Due

15

What is data visualization?

17

Workshop Day 1: Visualization Exploration

22

Why should we be Tufte, E.R. (2001). Graphical integrity. In Edward Tufte (Ed.) The Visual interested in visualization? Display of Quantitative Information (pp. 53-77). Cheshire, CT: Graphics Press.

24

Semiotics

Tufte, E.R. (2001). Graphical excellence. In Edward Tufte (Ed.) The Visual Display of Quantitative Information (pp.13-52). Cheshire, CT: Graphics Press.

Ware, C. (2013). Semiotics of graphics. In Meg Dunkerley (Ed.) Information Visualization: Perception for Design (pp. 6-8). Boston, MA: Elsevier. Arnheim, R. (1969). Pictures, symbols, and signs. In Rudolf Arnheim (Ed.) Visual Thinking (pp. 135-152). Berkeley, CA: University of California Press.

29

Visualization and Cognition

Ware, C. (2013). Visual thinking processes. In Meg Dunkerley (Ed.) Information Visualization: Perception for Design (pp. 375-393). Boston, MA: Elsevier.

31

Visualization and Cognition

Mayer, R. & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43-52.

5

Color

Silva, S., Santos, B.S. & Madeira, J. (2011). Using color in visualization: A Survey. Computers and Graphics, 35, 320-333.

7

Workshop Day 2: Design and Perception

Tufte, E.R. (1990). Color and information. In Edward R. Tufte Envisioning Information. Cheshire, CT: Graphics Press.

12

Static Patterns

Ware, C. (2013). Static and moving patterns. In Meg Dunkerley (Ed.) Information Visualization: Perception for Design (pp. 179-227). Boston, MA: Elsevier.

14

Space

Ware, C. (2013). Space Perception. In Meg Dunkerley (Ed.) Information Visualization: Perception for Design (pp. 239-290). Boston, MA: Elsevier.

19

Objects

Ware, C. (2013). Visual Objects and Data Objects. In Meg Dunkerley (Ed.) Information Visualization: Perception for Design (pp. 293-323). Boston, MA: Elsevier.

21

Workshop Day 3: Design and Perception

26

Data Ink

Tufte, E.R. (2001). Data-Ink and Graphical Redesign. In Edward Tufte (Ed.) The Visual Display of Quantitative Information (pp.91-106). Cheshire, CT: Graphics Press.

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28

Perception and Design

Cleveland, W. & McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79(387), pp. 531-554.

5

Narrative Visualization

Segel, E. and Heer, J. Narrative visualization: telling stories with data. IEEE Transactions on Visualization and Computer Graphics 16, 6 (2010), 1139–1148.

7

Workshop Day 4: Number Stories

12

SPRING BREAK

14

SPRING BREAK

19

Types of quantities and representations (When)

Borner, K. & Polley, D. E. (2014). “When”: Temporal data (pp. 37-74). In Katy Borner & David E. Polley, Visual Insights: A Practical Guide to Making Sense of Data. Cambridge, MA: The MIT Press.

21

Types of quantities and representations (Where)

Borner, K. & Polley, D. E. (2014). “Where”: Geospatial data (pp. 75-112). In Katy Borner & David E. Polley, Visual Insights: A Practical Guide to Making Sense of Data. Cambridge, MA: The MIT Press.

26

Types of quantities and representations (What)

Borner, K. & Polley, D. E. (2014). “What”: Topical data (pp. 113-142). In Katy Borner & David E. Polley, Visual Insights: A Practical Guide to Making Sense of Data. Cambridge, MA: The MIT Press.

28

Types of quantities and representations (With Whom)

Borner, K. & Polley, D. E. (2014). “With Whom”: Tree and Network data (pp. 143-214). In Katy Borner & David E. Polley, Visual Insights: A Practical Guide to Making Sense of Data. Cambridge, MA: The MIT Press.

2

More than just bars, lines and pies

Lile, S. 44 Types of Graphs Perfect for Every Top Industry: https://visme.co/blog/types-of-graphs/ Elliott, K. (2017). 39 Studies About Human Perception in 30 Minutes.

4

Chartjunk

Tufte, E.R. (2001). Chartjunk: Vibrations, Grids, and Ducks. In Edward Tufte (Ed.) The Visual Display of Quantitative Information (pp. 106-122). Cheshire, CT: Graphics Press.

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Visualization and Learning Rieber, L. (1995). A historical review of visualization in human cognition. in Formal Schooling Educational Technology Research and Development, 43(1), pp. 45-56. Arcavi, A. (2003). The role of visual representations in the learning of mathematics. Educational Studies in Mathematics, 52(3), p. 215-241.

11

Visualization and Learning Chiu, J. & Linn, M.C. (2014). Supporting knowledge integration in in Formal Schooling chemistry with a visualization-enhanced inquiry unit. Journal of Science Education and Technology, 23, pp 37-58.

16

Societal Implications

Tufte, E.R. (2001). Visual and Statistical Thinking: Displays of Evidence for Making Decisions. Cheshire, CT: Graphics Press.

9

18

Societal Implications

New York Times Upshot and Washington Post

23

Presentations

NO READINGS

25

NO CLASS

NO READINGS

30

Presentations

NO READINGS

10