The primary objectives of this course include (1) exposure to important
articles in data visualization research and (2) rethinking students’ research
problems as visualization problems. Through this course, students will learn
popular research methods and system development approaches, including
empirical study, system evaluation, technical evaluation, design space/survey,
user interface, and abstraction (grammar/formalization). Students will also
practice visualization research methods and techniques in their final projects.
Overview
Instructor: Hyeok Kim (Email: hyeok.kim.work [at] gmail [dot] com; please use this email for course-related inquiries.)
Time: Tuesday/Thursday 13:00–14:30
Location: N1-#102
Office hours: TBD/By appointment (N1-#607)
Topics
Week 1–3: introduction to visualization research and perception & cognition
Week 4-6: visualization for communication
Week 7-15: application, programming, and user contexts
In-class presentation (on 1 paper, once in the course).
Detailed reading report (2 papers, 2 pages) and online discussion for Week 11 in replacement of the physical classes.
Final project (including mid-term progress report): Students can choose one of the four options (detailed project proposal, interactive visualization system, data story, and survey paper).
Students are highly encouraged to skim optional readings.
Weekly Plans
All time and dates are based on the Korea Standard Time (KST). Plans can be
adjusted. PDF files will be uploaded for the readings.
Week 1: Introduction to Visualization Research
Day 1 (Sep. 1): Introduction to data visualization research (Lecture)
Students are expected to prepare a short self-introduction like an elevator pitch.
While reading reports are not assigned this week, students are strongly encouraged to read the required readings.
Day 2 (Sep. 3): Data visualization models (Lecture)
Requred readings
Card, S. K., Mackinlay, J., and Shneiderman, B.. (1999). Chapter 1. Information Visualization. Readings in information visualization: using vision to think. PDF.
Amar, R., Eagan, J., and Stasko, J.. (1999). Low-level components of analytic activity in information visualization. Readings in information visualization: using vision to think. PDF.
Optional readings
Brehmer, M. and Munzner, T. (2013). A multi-level typology of abstract visualization tasks. IEEE TVCG. PDF.
Munzner, T. (2009). A nested model for visualization design and validation. IEEE TVCG. PDF.
Battle, L. and Ottley, A.. (2023). What do we mean when we say “insight”? A formal synthesis of existing theory. IEEE TVCG. PDF.
Day 1 (Sep. 8): Empirical studies on graphical perception
Day 2 (Sep. 10): Applications of graphical perception studies
Requred readings
Cleveland, W. S. and McGill, R.. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American statistical association.
Szafir, D. A.. (2017). Modeling color difference for visualization design. IEEE TVCG.
Optional readings
Mackinlay, J.. (1986). Automating the design of graphical presentations of relational information. ACM TOG.
Demiralp, Ç., Bernstein, M. S., and Heer, J.. (2014). Learning perceptual kernels for visualization design. IEEE TVCG.
Saket, B., Endert, A., and Demiralp, Ç.. (2018). Task-based effectiveness of basic visualizations. IEEE TVCG.
Kim, Y. and Heer, J.. (2018). Assessing effects of task and data distribution on the effectiveness of visual encodings. CGF.
Xiong, C., Stokes, C., Kim, Y. S., and Franconeri, S.. (2022). Seeing what you believe or believing what you see? belief biases correlation estimation. IEEE TVCG.
Heer, J., Kong, N., and Agrawala, M.. (2009). Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations. ACM CHI.
Wongsuphasawat, K., Moritz, D., Anand, A., Mackinlay, J., Howe, B., and Heer, J.. (2015). Voyager: Exploratory analysis via faceted browsing of visualization recommendations. IEEE TVCG.
Hullman, J., Qiao, X., Correll, M., Kale, A., and Kay, M.. (2018). In pursuit of error: A survey of uncertainty visualization evaluation. IEEE TVCG.
Kale, A., Nguyen, F., Kay, M., and Hullman, J.. (2018). Hypothetical outcome plots help untrained observers judge trends in ambiguous data. IEEE TVCG.
Yang, F., Cai, M., Mortenson, C., Fakhari, H., Lokmanoglu, A. D., Hullman, J., Franconeri, S., Diakopoulos, N., Nisbet, E. C., and Kay, M.. (2023). Swaying the public? impacts of election forecast visualizations on emotion, trust, and intention in the 2022 US midterms. IEEE TVCG.
Kim, Y. S., Reinecke, K., and Hullman, J.. (2017). Data through others' eyes: The impact of visualizing others' expectations on visualization interpretation. IEEE TVCG.
Kale, A., Kay, M., and Hullman, J.. (2020). Visual reasoning strategies for effect size judgments and decisions. IEEE TVCG.
Kale, A., Guo, Z., Qiao, X. L., Heer, J., and Hullman, J.. (2023). Evm: Incorporating model checking into exploratory visual analysis. IEEE TVCG.
Liu, Y., Kale, A., Althoff, T., and Heer, J.. (2020). Boba: Authoring and visualizing multiverse analyses. IEEE TVCG.
Week 4: Communication 1—Narrative Visualization
Day 1 (Sep. 22): Narrative visualization and systems
Day 2 (Sep. 24): No class due to the Chuseok (추석)
Requred readings
Segel, E. and Heer, J.. (2010). Narrative visualization: Telling stories with data. IEEE TVCG.
Hullman, J. and Diakopoulos, N.. (2011). Visualization rhetoric: Framing effects in narrative visualization. IEEE TVCG.
Optional readings
Stokes, C., Bearfield, C. X., and Hearst, M. A.. (2023). The role of text in visualizations: How annotations shape perceptions of bias and influence predictions. IEEE TVCG.
Rahman, M. D., Rahat-Uz-Zaman, M., McNutt, A., and Rosen, P.. (2025). AnnoGram: An Annotative Grammar of Graphics Extension. IEEE VIS.
Kim, Y., Wongsuphasawat, K., Hullman, J., and Heer, J.. (2017). Graphscape: A model for automated reasoning about visualization similarity and sequencing. ACM CHI.
Bigelow, A., Drucker, S., Fisher, D., and Meyer, M.. (2016). Iterating between tools to create and edit visualizations. IEEE TVCG.
Conlen, M. and Heer, J.. (2018). Idyll: A markup language for authoring and publishing interactive articles on the web. ACM UIST.
Week 5: Communication 2—Visualization Literacy and Deceptive Visualizations
Day 1 (Sep. 29): Measuring visualization literacy
Day 2 (Oct. 1): Deceptive visualizations
Requred readings
Lee, S., Kim, S. H., and Kwon, B. C.. (2016). Vlat: Development of a visualization literacy assessment test. IEEE TVCG.
Pandey, A. V., Rall, K., Satterthwaite, M. L., Nov, O., and Bertini, E.. (2015). How deceptive are deceptive visualizations? An empirical analysis of common distortion techniques. ACM CHI.
Optional readings
Pandey, S. and Ottley, A.. (2023). Mini‐VLAT: A short and effective measure of visualization literacy. CGF.
Nobre, C., Zhu, K., Mörth, E., Pfister, H., and Beyer, J.. (2024). Reading between the pixels: Investigating the barriers to visualization literacy. ACM CHI.
Kindlmann, G. and Scheidegger, C.. (2014). An algebraic process for visualization design. IEEE TVCG.
McNutt, A., Kindlmann, G., and Correll, M.. (2020). Surfacing visualization mirages. ACM CHI.
Ge, L. W., Cui, Y., and Kay, M.. (2023). Calvi: Critical thinking assessment for literacy in visualizations. ACM CHI.
Chen, Q., Sun, F., Xu, X., Chen, Z., Wang, J., and Cao, N.. (2021). Vizlinter: A linter and fixer framework for data visualization. IEEE TVCG.
McNutt, A., Stone, M. C., and Heer, J.. (2024). Mixing linters with GUIs: a color palette design probe. IEEE TVCG.
Lee, C., Yang, T., Inchoco, G. D., Jones, G. M., and Satyanarayan, A.. (2021). Viral visualizations: How coronavirus skeptics use orthodox data practices to promote unorthodox science online. ACM CHI.
Nadib, K. A., Kogan, M., Lex, A., and Lisnic, M.. (2026). Guardrail Selection in Line Charts to Contextualize Persuasive Visualizations. CGF.
Week 6: Communication 3—Interactive Visualization
Day 1 (Oct. 6): Interaction in visualization
Day 2 (Oct. 8): Interactive visualization systems
Requred readings
Yi, J. S., Kang, Y. A., Stasko, J., and Jacko, J. A.. (2007). Toward a deeper understanding of the role of interaction in information visualization. IEEE TVCG.
Heer, J. and Bostock, M.. (2010). Declarative language design for interactive visualization. IEEE TVCG.
Optional readings
Feng, M., Deng, C., Peck, E. M., and Harrison, L.. (2018). The effects of adding search functionality to interactive visualizations on the web. ACM CHI.
Chuah, M. C. and Roth, S. F.. (1996). On the semantics of interactive visualizations. IEEE InfoVis.
Satyanarayan, A. and Heer, J.. (2014). Lyra: An interactive visualization design environment. CGF.
Battle, L. and Scheidegger, C.. (2020). A structured review of data management technology for interactive visualization and analysis. IEEE TVCG.
Snyder, L. S. and Heer, J.. (2023). Divi: Dynamically interactive visualization. IEEE TVCG.
Zong, J., Barnwal, D., Neogy, R., and Satyanarayan, A.. (2020). Lyra 2: Designing interactive visualizations by demonstration. IEEE TVCG.
Brehmer, M. and Kosara, R.. (2021). From jam session to recital: Synchronous communication and collaboration around data in organizations. IEEE TVCG.
Heer, J., Conlen, M., Devireddy, V., Nguyen, T., and Horowitz, J.. (2023). Living papers: A language toolkit for augmented scholarly communication. ACM UIST.
Week 7: Application 1—ML/AI/Quantum Computing
Day 1 (Oct. 13): AI and ML for VIS
Day 2 (Oct. 15): VIS for AI/ML/Quantum computing
Requred readings
Wu, A., Wang, Y., Shu, X., Moritz, D., Cui, W., Zhang, H., Zhang, D., and Qu, H.. (2021). Ai4vis: Survey on artificial intelligence approaches for data visualization. IEEE TVCG.
Kim, H., Jeng, M. J., and Smith, K. N.. (2025). Toward human-quantum computer interaction: Interface techniques for usable quantum computing. ACM CHI.
Optional readings
Narechania, A., Karduni, A., Wesslen, R., and Wall, E.. (2021). VITALITY: Promoting serendipitous discovery of academic literature with transformers & visual analytics. IEEE TVCG.
Görtler, J., Hohman, F., Moritz, D., Wongsuphasawat, K., Ren, D., Nair, R., and Patel, K.. (2022). Neo: Generalizing confusion matrix visualization to hierarchical and multi-output labels. ACM CHI.
Hu, K., Bakker, M. A., Li, S., Kraska, T., and Hidalgo, C.. (2019). Vizml: A machine learning approach to visualization recommendation. ACM CHI.
Moritz, D., Wang, C., Nelson, G. L., Lin, H., Smith, A. M., Howe, B., and Heer, J.. (2018). Formalizing visualization design knowledge as constraints: Actionable and extensible models in Draco. IEEE TVCG.
Wang, H. W., Gordon, M., Battle, L., and Heer, J.. (2024). DracoGPT: Extracting visualization design preferences from large language models. IEEE TVCG.
Li, H., Wang, Y., Zhang, S., Song, Y., and Qu, H.. (2021). KG4VIS: A knowledge graph-based approach for visualization recommendation. IEEE TVCG.
Kim, H. and Heer, J.. (2025). Data Augmentation for Visualization Design Knowledge Bases. IEEE TVCG.
Kim, H., L'Yi, S., Gehlenborg, N., and Heer, J.. (2026). Automatic Synthesis of Visualization Design Knowledge Bases. ACM CHI.
Ruan, S., Guan, Q., Griffin, P., Mao, Y., and Wang, Y.. (2023). Quantumeyes: Towards better interpretability of quantum circuits. IEEE TVCG.
Gyarmati, P. F., Moritz, D., Möller, T., and Koesten, L.. (2025). Structured Visualization Design Knowledge for Grounding Generative Reasoning and Situated Feedback. arXiv.
Zhang, D., Chatzimparmpas, A., Kamali, N., and Hullman, J.. (2024). Evaluating the utility of conformal prediction sets for ai-advised image labeling. ACM CHI.
Kim, H. and Battle, L.. (2026). A design space for quantum circuit visualizations. IEEE TVCG.
Day 1 ( (Oct. 20, 13:00–15:45)): In-class project progress presentation
No required readings this week
Week 9: System Development and User Testing
Day 1 (Oct. 27): Visualization systems
Day 2 (Oct. 29): User testing in visualization research
Requred readings
Satyanarayan, A., Lee, B., Ren, D., Heer, J., Stasko, J., Thompson, J., Brehmer, M., and Liu, Z.. (2019). Critical reflections on visualization authoring systems. IEEE TVCG.
Isenberg, T., Isenberg, P., Chen, J., Sedlmair, M., and Möller, T.. (2013). A systematic review on the practice of evaluating visualization. IEEE TVCG.
Optional readings
Cutler, Z., Wilburn, J., Shrestha, H., Ding, Y., Bollen, B., Nadib, K. A., He, T., McNutt, A., Harrison, L., and Lex, A.. (2026). ReVISit 2: A Full Experiment Life Cycle User Study Framework. IEEE TVCG.
Liu, Z., Thompson, J., Wilson, A., Dontcheva, M., Delorey, J., Grigg, S., and Stasko, J.. (2018). Data illustrator: Augmenting vector design tools with lazy data binding for expressive visualization authoring. ACM CHI.
Gathani, S., Monadjemi, S., Ottley, A., and Battle, L.. (2022). A grammar‐based approach for applying visualization taxonomies to interaction logs. CGF.
Ren, D., Lee, B., and Brehmer, M.. (2018). Charticulator: Interactive construction of bespoke chart layouts. IEEE TVCG.
Nobre, C., Wootton, D., Cutler, Z., Harrison, L., Pfister, H., and Lex, A.. (2021). reVISit: Looking under the hood of interactive visualization studies. ACM CHI.
Wu, Y., Guo, Z., Mamakos, M., Hartline, J., and Hullman, J.. (2023). The rational agent benchmark for data visualization. IEEE TVCG.
Kim, H. and Heer, J.. (2026). How do visualization researchers manage stimuli?. IEEE TVCG.
Week 10: Visualization Programming 1—D3.js
Day 1 (Nov. 3): Programming in visualization (lecture) and D3.js (lab)
Day 2 (Nov. 5): D3.js (lab)
Requred readings
Bostock, M., Ogievetsky, V., and Heer, J.. (2011). D³ data-driven documents. IEEE TVCG.
Liu, Z., Chen, C., and Hooker, J.. (2024). Manipulable semantic components: a computational representation of data visualization scenes. IEEE TVCG.
Optional readings
Bostock, M. and Heer, J.. (2009). Protovis: A graphical toolkit for visualization. IEEE TVCG.
Battle, L., Feng, D., and Webber, K.. (2022). Exploring d3 implementation challenges on stack overflow. IEEE VIS.
Yang, J., McNutt, A. M., and Battle, L.. (2024). Considering visualization example galleries. IEEE VL/HCC.
Lin, M., Patel, H., Lamkin, M., Bako, H., and Battle, L.. (2025). How Do Observable Users Decompose D3 Code? A Qualitative Study. IEEE VIS.
Engage in online discussion by making comments to at least three other reports.
Requred readings
Herman, I., Melançon, G., and Marshall, M. S.. (2000). Graph visualization and navigation in information visualization: A survey. IEEE TVCG.
Peña-Araya, V., Bezerianos, A., and Pietriga, E.. (2020). A comparison of geographical propagation visualizations. ACM CHI.
Optional readings
Schöttler, S., Yang, Y., Pfister, H., and Bach, B.. (2021). Visualizing and interacting with geospatial networks: A survey and design space. CGF.
Filipov, V., Arleo, A., and Miksch, S.. (2023). Are we there yet? a roadmap of network visualization from surveys to task taxonomies. CGF.
Nusrat, S., Alam, M. J., and Kobourov, S.. (2016). Evaluating cartogram effectiveness. IEEE TVCG.
Mota, R., Ferreira, N., Silva, J. D., Horga, M., Lage, M., Ceferino, L., Alim, U., Sharlin, E., and Miranda, F.. (2022). A comparison of spatiotemporal visualizations for 3D urban analytics. IEEE TVCG.
Von Landesberger, T., Kuijper, A., Schreck, T., Kohlhammer, J., van Wijk, J. J., Fekete, J. D., and Fellner, D. W.. (2011). Visual analysis of large graphs: state‐of‐the‐art and future research challenges. CGF.
Nobre, C., Meyer, M., Streit, M., and Lex, A.. (2019). The state of the art in visualizing multivariate networks. CGF.
Setlur, V., Battersby, S. E., Tory, M., Gossweiler, R., and Chang, A. X.. (2016). Eviza: A natural language interface for visual analysis. ACM UIST.
Zhang, D., Adar, E., and Hullman, J.. (2021). Visualizing uncertainty in probabilistic graphs with network hypothetical outcome plots (NetHOPs). IEEE TVCG.
Wilkinson, L.. (2011). The grammar of graphics. Handbook of Computational Statistics.
Satyanarayan, A., Moritz, D., Wongsuphasawat, K., and Heer, J.. (2016). Vega-lite: A grammar of interactive graphics. IEEE TVCG.
Optional readings
Wickham, H.. (2010). A layered grammar of graphics. JCGS.
McNutt, A. M.. (2022). No grammar to rule them all: A survey of json-style dsls for visualization. IEEE TVCG.
VanderPlas, J., Granger, B., Heer, J., Moritz, D., Wongsuphasawat, K., Satyanarayan, A., Lees, E., Timofeev, I., Welsh, B., and Sievert, S.. (2018). Altair: Interactive statistical visualizations for python. JOSS.
Zong, J., Pollock, J., Wootton, D., and Satyanarayan, A.. (2022). Animated Vega-Lite: Unifying animation with a grammar of interactive graphics. IEEE TVCG.
Heer, J. and Moritz, D.. (2023). Mosaic: An architecture for scalable & interoperable data views. IEEE TVCG.
Pollock, J. and Satyanarayan, A.. (2025). GoFish: a Grammar of More Graphics!. IEEE TVCG.
Pu, X. and Kay, M.. (2020). A probabilistic grammar of graphics. ACM CHI.
L'Yi, S., Wang, Q., Lekschas, F., and Gehlenborg, N.. (2021). Gosling: A grammar-based toolkit for scalable and interactive genomics data visualization. IEEE TVCG.
Week 13: User 1—Accessibility and Multimodality
Day 1 (Nov. 24): Accessibility and Multimodality
Day 2 (Nov. 26): No class due to undergraduate admission interviews
Requred readings
Seo, J., Xia, Y., Lee, B., Mccurry, S., and Yam, Y. J.. (2024). Maidr: Making statistical visualizations accessible with multimodal data representation. ACM CHI.
Bae, S. S., Zheng, C., West, M. E., Do, E. Y. L., Huron, S., and Szafir, D. A.. (2022). Making data tangible: A cross-disciplinary design space for data physicalization. ACM CHI.
Optional readings
Zhang, Z., Thompson, J. R., Shah, A., Agrawal, M., Sarikaya, A., Wobbrock, J. O., Cutrell, E., and Lee, B.. (2024). ChartA11y: Designing accessible touch experiences of visualizations with blind smartphone users. ACM ASSETS.
Kim, N. W., Joyner, S. C., Riegelhuth, A., and Kim, Y.. (2021). Accessible visualization: Design space, opportunities, and challenges. CGF.
Wang, Y., Wang, R., Jung, C., and Kim, Y. S.. (2022). What makes web data tables accessible? Insights and a tool for rendering accessible tables for people with visual impairments. ACM CHI.
Kim, H., Kim, Y. S., and Hullman, J.. (2024). Erie: A declarative grammar for data sonification. ACM CHI.
Zong, J., Pedraza Pineros, I., Chen, M., Hajas, D., and Satyanarayan, A.. (2024). Umwelt: Accessible structured editing of multi-modal data representations. ACM CHI.
Chen, M., Pedraza Pineros, I., Satyanarayan, A., and Zong, J.. (2025). Tactile vega-lite: Rapidly prototyping tactile charts with smart defaults. ACM CHI.
Thompson, J. R., Martinez, J. J., Sarikaya, A., Cutrell, E., and Lee, B.. (2023). Chart reader: Accessible visualization experiences designed with screen reader users. ACM CHI.
Week 14: Application 3—Data Analytics and Dashboard
Day 1 (Dec. 1): Visualization and data analysis
Day 2 (Dec. 3): Dashboard + Tableau (lab)
Requred readings
Crisan, A., Fiore-Gartland, B., and Tory, M.. (2020). Passing the data baton: A retrospective analysis on data science work and workers. IEEE TVCG.
Becker, R. A., Cleveland, W. S., and Shyu, M. J.. (1996). The visual design and control of trellis display. JCGS.
Optional readings
Bach, B., Freeman, E., Abdul-Rahman, A., Turkay, C., Khan, S., Fan, Y., and Chen, M.. (2022). Dashboard design patterns. IEEE TVCG.
Mackinlay, J., Hanrahan, P., and Stolte, C.. (2007). Show me: Automatic presentation for visual analysis. IEEE TVCG.
Srinivasan, A., Purich, J., Correll, M., Battle, L., Setlur, V., and Crisan, A.. (2024). From dashboard zoo to census: A case study with tableau public. IEEE TVCG.
Isenberg, P., Elmqvist, N., Scholtz, J., Cernea, D., Ma, K. L., and Hagen, H.. (2011). Collaborative visualization: Definition, challenges, and research agenda. Information Visualization.
Tory, M., Bartram, L., Fiore-Gartland, B., and Crisan, A.. (2021). Finding their data voice: Practices and challenges of dashboard users. IEEE CG&A.
Setlur, V. and Tory, M.. (2022). How do you converse with an analytical chatbot? revisiting gricean maxims for designing analytical conversational behavior. ACM CHI.
Mahyar, N. and Tory, M.. (2014). Supporting communication and coordination in collaborative sensemaking. IEEE TVCG.
Week 15: Responsive Design and Non-desktop Visualizations
Day 1 (Dec. 8): Responsive design
Day 2 (Dec. 10): Non-desktop visualizations
Requred readings
Kim, H., Moritz, D., and Hullman, J.. (2021). Design patterns and trade‐offs in responsive visualization for communication. CGF.
Ens, B., Goodwin, S., Prouzeau, A., Anderson, F., Wang, F. Y., Gratzl, S., Lucarelli, Z, Moyle, B., Smiley, J, and Dwyer, T.. (2020). Uplift: A tangible and immersive tabletop system for casual collaborative visual analytics. IEEE TVCG.
Optional readings
Schöttler, S., Dykes, J., Wood, J., Hinrichs, U., and Bach, B.. (2024). Constraint-based breakpoints for responsive visualization design and development. IEEE TVCG.
Blascheck, T., Besançon, L., Bezerianos, A., Lee, B., and Isenberg, P.. (2018). Glanceable visualization: Studies of data comparison performance on smartwatches. IEEE TVCG.
Bezerianos, A. and Isenberg, P.. (2012). Perception of visual variables on tiled wall-sized displays for information visualization applications. IEEE TVCG.
Spindler, M., Tominski, C., Schumann, H., and Dachselt, R.. (2010). Tangible views for information visualization. ACM ITS.
Kim, H., Rossi, R., Du, F., Koh, E., Guo, S., Hullman, J., and Hoffswell, J.. (2022). Cicero: A declarative grammar for responsive visualization. ACM CHI.
Kim, H., Rossi, R., Sarma, A., Moritz, D., and Hullman, J.. (2021). An automated approach to reasoning about task-oriented insights in responsive visualization. IEEE TVCG.
Kim, H., Rossi, R., Hullman, J., and Hoffswell, J.. (2023). Dupo: A mixed-initiative authoring tool for responsive visualization. IEEE TVCG.
Badam, S. K. and Elmqvist, N.. (2021). Effects of screen-responsive visualization on data comprehension. Information Visualization.
Horak, T., Aigner, W., Brehmer, M., Joshi, A., and Tominski, C.. (2021). Responsive visualization design for mobile devices. Mobile Data Visualization.
Brehmer, M., Lee, B., Isenberg, P., and Choe, E. K.. (2018). Visualizing ranges over time on mobile phones: a task-based crowdsourced evaluation. IEEE TVCG.
Wu, A., Tong, W., Dwyer, T., Lee, B., Isenberg, P., and Qu, H.. (2020). Mobilevisfixer: Tailoring web visualizations for mobile phones leveraging an explainable reinforcement learning framework. IEEE TVCG.
Wu, A., Xie, L., Lee, B., Wang, Y., Cui, W., and Qu, H.. (2021). Learning to automate chart layout configurations using crowdsourced paired comparison. ACM CHI.
Week 16: Finals
Day 1 (Dec. 15, 13:00–15:45): Final project presentation
No required readings this week
Assignments
All assignments are due by the EOD (the end of the day) of the specified dates. Students can request extensions up to two (2) days.
Formatting
For reflection reports, there is no specific formatting requirement, but avoid using bullet points.
For written parts of the project, use any kinds of recognized conference paper formats.
1. Weekly reading
Due: Before the first class of each week unless specified otherwise
Students are expected to do the required reading (2-4 papers) by the first class of each week unless specified otherwise. This course involves lots of in-class discussion. Students need to submit reflection reports (at least 4 paragraphs) before the first class of each week. Reflection reports must not be a summary, but should describe their reactions to the reading materials. Example approaches include a future study idea (extensions or applications), potential counter examples of the papers’ arguments, how AI would impact the findings, criticism, and so on. This assignment is designed to be a practice for critical reading of research papers. There are no format requirements.
2. A paper presentation
Due: In class
Each student will pick a paper or two in the below reading list and present them during the class. Each presentation must be shorter than 15 minutes and must not summarize the chosen papers but include their reflections and discussion topics for the class. This presentation will be initiating thoughts for that week’s discussion during the class. This assignment is also designed to be a presentation practice.
3. Extended reading report & online discussion
Due: Nov. 10 (report), Nov. 12 (discussion)
For the week of Nov. 10/12, the instructor is traveling for the VIS conference, so there will be no physical classes. Instead, students are required to submit a 2-page reading report by November 10 (by the end of the day) and leave discussion comments to at least three other reports by November 12. There are no format requirements.
4. Project
Due: See below
Students will choose one of the following options as their final assignment. In doing so, students are welcome to incorporate their own research projects.
Options
A detailed research project proposal. Choosing this option, students will need to submit a 5- to 6-page detailed research proposal. Proposals must discuss their relevance as visualization research. Proposals can be considered as a full paper without the result sections.
An interactive visualization system. Choosing this option, students will need to design and implement an interactive visualization system. Students will also need to submit a short essay that describes the objectives of the project and how the system (up to 2 pages).
An interactive data story. Choosing this option, students will need to design and implement an interactive data story (like those from news outlets). The final outcome must be accessible and responsive.
A survey paper on a topic. Choosing this option, students will need to submit a 4-page survey paper on a specific topic in relation to visualization. For example, students can survey papers that cover the use of visualization in their own field.
Steps
Mid-term report In-class date: Oct. 20, Tuesday, 13:00–15:45 Report due date: Oct. 25 Each team will give a 5-minute presentation about their final project along with 3-minute Q&A. Students are expected to present any progress they have been making by then. If needed, students can make a separate appointment for feedback before or after the mid-term report. By the end of that week (Oct. 25, Sunday, EOD), students must submit a 2-page proposal for their final project and progress report (formats and rubrics will be announced later).
Final-term report In-class date: Dec. 15, Tuesday, 13:00-15:45 Report due date: Dec. 20 Each team will give a 7-minute presentation with 3-minute Q&A. Based on the feedback and Q&A, students are required to submit the final, updated materials as specified above by the end of the finals week (Sunday). Extensions for the final project may not be possible depending on the grading schedule.
Policies
Failure to meet the following policies may result in deduction in grading
and/or disciplinary actions.
No hate, no discrimination, and no violence allowed
Including assignment, discussion, and final project, there is no room for hate, discrimination, and violence against classmates and certain demographics.
Use of AI
Students are not restrained from using AI but the following exceptions. 1. Students must do readings on their own. Do NOT use AI for summarizing the reading materials. 2. Students can get minor assistance from AI for writing (e.g., English, checking flows), but the ideas and writing must be original. 3. If AI is used in any part of assignments, students must submit a detailed report on how they have used AI.
No plagiarism
In any kind of activity in this course, plagiarism is strictly forbidden. Students are responsible for plagiarism due to the use of AI.
Citation
Students are required to check citations/references thoroughly. Students will be responsible for fake/wrong citations due to AI hallucination.
Accessibility Support
When needing accessibility support, please contact the Student Affairs Team and the instructor via e-mail as well.