RAA1: Visual Difficulties to Enhance Engagement and Learning

Hullman, J., Adar, E., & Shah, P. (2011). Benefitting InfoVis with Visual Difficulties. Visualization and Computer Graphics, IEEE Transactions on, 17(12), 2213–2222.
Purpose of the Research: 
To provide a counterpoint to efficiency-based design theory with guidelines that describe how visual difficulties can be introduced to benefit comprehension and recall.
This is an essay-style paper. The method uses is to synthesize empirical results from cross-disciplinary research on visual information representations.
Main Findings:
The dominant visual design and evaluation guidelines are based on the cognitive efficiency model, which refraining from using distracting visual elements, irrelevant information, leveraging labeling, and graphical formats that reduce cognitive processing by the users. However, empirical studies from various sub-fields of psychology and education support that desirable visual difficulties may induce active processing and engagement of the users thus enhance deep reflection and long term recall. Visualization effectiveness is better characterized as a trade-off between efficient processing and desirable visual difficulties to stimulate learning. 
One analogy to the authors’ argument I can think of is watching movies vs. reading books. Just like one folk mentioned in class about the Harry Potter movies vs. books, watching movie is effortless, the users do not need to actively construct the details in their minds. However, according to the logic of the authors’ argument, reading books make the readers work, think, and thus more engaged in learning. Leaving space for the users to think and reflect is one thing, another thing is that it maybe okay to add some distracting or seemingly irrelevant information to just attract the users’ interests so they could be more engaged. A large part of this paper is actually talking about engagement, rather than visual difficulties, but the authors use the contradictory and eye-catching phrase  “visual difficulties” in the title. This paper reminds us that we have almost passed the stage of designing only for efficiency, supporting for reflection and learning need to be taken into consideration.

RAA#3: CommentSpace, Collaborative Visual Analytics

  1. APA Citation:
    Willett, W., Heer, J., Hellerstein, J., & Agrawala, M. (2011). CommentSpace: structured support for collaborative visual analysis. Proceedings of the 2011 annual conference on Human factors in computing systems (pp. 3131–3140). PDF

    CommentSpace website

  2. Purpose:  (1) Present details of a web-based collaborative visual analysis system CommentSpace that aims to help users better make sense of the visualizations through synthesizing others’ comments. CommentSpace “enables analysts to annotate visualizations and apply two additional kinds of structure: 1) tags that consist of descriptive text attached to comments or views; and 2) links that denote relationships between two comments or between a comment and a specific visualization state or view. The resulting structure can help analysts navigate, organize, and synthesize the comments, and move beyond exploration to more complex analytical tasks. (2) Evaluate this system: “how a small, fixed vocabulary of tags (question, hypothesis, to-do) and links (evidence-for, evidence-against) can help analysts collect and organize new evidence, identify important findings made by others, and synthesize their findings” and “establish common ground”.
  3. Methods: (1) present technical details of the design of this system, and usage scenario (2) evaluate by two controlled user studies and a live deployment comparing CommentSpace with a similar system that doesn’t support tags and links.
  4. Main findings: (1) A small, fixed vocabulary of tags and links helps analysts more consistently and accurately classify evidence and establish comment ground. (2) Managing and incentivizing participation is important for analysts to progress from exploratory analysis to deeper analytical tasks. (3) Tags and links can help teams complete evidence gathering and synthesis tasks and that organizing comments using tags and links improves analytics results.
  5. Analysis: (1) This paper is from the “garden” of information visualization and visual analytics. This line of work (collaborative visual analytics) is drawn from and expanding into CSCW and social media research. Because computing systems are eventually serving people within their social contexts, also because of the popularity of the web, many technical systems are implemented on the web and thus seek to support people, their communication and collaboration. I see this emerging converging point between social media and visualization techniques, but there are still huge discrepancies in the way of thinking and doing among researchers in different disciplines (esp. computer science and social science). Traditionally, the way of conducting user studies in technical world usually lack of rigor or depth. “It was almost a joke in some technical domains that reviewers of papers just need to check the mental box of the existence of user studies without considering the quality”. Large part of those papers are dedicated to “fancy algorithms”. The future of social computing calls for close collaboration between computer scientists and social scientists, further more engineers, artists and designers. (2) This paper is related to my project of integrating user participation in rating, tagging and commenting academia papers. CommentSpace is designed as a modular softare that can run  in conjunction with any interactive visualization system or website that treats each view of the data as a discrete state, so maybe I am looking forward to adopt it or some elements of it to my project in the future.