Takeaways on Immersion Analytics Research

Created by: Amanda Levy (May 2022)

General Performance Takeaways

  • Despite conducting collaboration testing with Bob Levy from Immersion Analytics, the collaboration feature did not work smoothly during the class activity. I had everyone enter the room created by Immersion Analytics' provided Lobby Address. I was the host and had opened a .viz file that was displayed on my Oculus. When the class entered the collaboration space, however, they couldn’t see any graphs. They could only see the galaxy background.

=> Thus, I switched to an alternative collaboration activity that relied on more external communication than inherent Immersion Analytics features. Class members exited the virtual room, and, instead, they opened the previously downloaded .viz files in their individual Immersion Analytics environment. I split the class into partner groups, and both partners went to Main Menu --> Experiences --> and opened the .viz files. As a group, they communicated to simultaneously look at the same graphs, and because they were in the same physical room, they discussed out loud their findings. Additionally, they filled out this Google Form to share their opinions on how Immersion Analytics supports collaboration efforts as defined in this Seven Scenarios paper.

  • People complained about glitchy performance. When I conducted a test run with Bob Levy from Immersion Analytics, the performance was optimal with no lag. Thus, the class proposed that it might be the room’s wifi.

  • An issue we resolved is that for the user to see the .viz files under “Experiences” → “Open”, you must transfer the .viz files themselves, not the depository.

For future research, it would be beneficial to figure out which applications each of the 18 dimensions would be best used with.

Evaluating Immersion Analytics Using Collaborative Data Analysis (CDA) from the Seven Scenarios Paper

Summary and Suggestions for Collaboration Features

( all of the Google Form questions and responses are below)

  1. The software is built on the foundational benefit that you can leverage visual cues to characterize data points by up to 18 dimensions. This fundamental visual nature supports collaboration because it is easy to identify and verbally share a specific data point with a partner who is looking at the same graph. As a class member commented, you can say “the small cone in the corner.” Users found that this features encouraged communication.

      1. Some users found this difficult, especially when there was a lot of data that crowded the graphs. It was suggested that the below-described pointer approach might be a resolution.

      2. A user found that the ability to use up to 18 different visual characteristics to characterize a point made the “VR visualization … interesting [so] it was fun to talk about so that facilitated conversation.” Other users corroborated this point as well.

  2. Another set of features that a user mentioned was that “being able to select a datapoint was an amazing feature that made it easier to conduct the discussions. [They] also liked the side panel that loaded when you selected the data point.”

      1. Building on the above analysis

        1. The visual characteristics of the data point makes it easier for multiple users to identify the same point, and subsequently clicking on the data point and viewing the side panel helps users continue the conversation by having all the data attributes written out in a clear table that they can sequentially go through to draw conclusions about each characteristic.

      2. Additionally, sometimes it is difficult to read the axis clearly, so users found the feature of selecting the data point and viewing the side panel helpful “to check which country it lined up” with.

  3. For those who wanted to know what the differing visual characteristics represented, users found it helpful to start by looking at the legend, which a user quoted “helped us talk about various scenarios by providing a point of reference.”

  4. A repeated suggestion for a feature that would aid collaboration is the ability “to see someone else’s pointer pointing to data points on the graph.” This may be a feature of the buggy room that we were unable to get working for the in-class activity.

  5. A complementary collaboration feature was “the way you could slice the visualization to talk about them.”

  6. When the room did not allow for collaborative viewing, the class pursued an alternative collaboration activity where partners independently but simultaneously viewed the .viz files. This forced groups to discuss out-loud, and a classmate noted that they had to “coordinate and view data points together. However, ... it could be easier in the collaboration feature the app offers.”

      1. Another classmate noted that independent alternative activity “could be difficult to collaborate over if people were not in the same physical location.”

        1. They offered a product suggestion of adding an in-app chat to support communication more directly. An in-app chat would allow this alternative in-class activity and communication in general to be feasible and facilitated with participants in different physical locations.

          1. Even if the room feature was successful and allowed for audio (future research using Immersion Analytics should confirm this feature), adding an in-app chat would promote accessibility for users who experience hearing issues.

        2. An additional feature suggestion that could be helpful for supporting communication and collaboration when users are in different physical locations is the use of avatars in the collaboration rooms. Other VR apps let you upload a picture or select personalization accessories that allow you to make a virtual representation of you. Often, users are more excited and passionate about the task at hand when they are working with “humans” in the VR environment.

  7. A classmate noted a very interesting point about how features can impact collaboration. They commented that “collaborative visualization involves people clicking on the same set of buttons to see the same visualizations, so in that sense I think the ease of navigation with the graphs really helped.”

      1. Although research has validated the benefits of collaboration, it can seem inefficient if the setup and flow requires more steps and more active-involvement. Focusing on the simplicity of the setup and action steps can be very impactful on whether teams opt for collaboration or independent reflections.

        1. A separate but related example of this is how sometimes students prefer independent projects over group projects.

"Does the tool support effective and efficient collaborative data analysis?" How so?

"Does the tool support group insight?" How so?

"Is social exchange around and communication about the data facilitated?" How so?

"How is the collaborative visualization system used?" "What is the process of collaborative analysis?"

"How are certain system features used during collaborative work? What are patterns of system use?"