College of Information Studies
Permanent URI for this communityhttp://hdl.handle.net/1903/1631
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item The Role of 3D Spatiotemporal Telemetry Analysis in Combat Flight Simulation(2024) Mane, Sourabh Vijaykumar; Elmqvist, Niklas Dr; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Analyzing 3D telemetry data collected from competitive video games on the internet can support players in improving performance as well as spectators in viewing data-driven narratives of the gameplay. In this thesis, we conduct an in-depth qualitative study on the use of telemetry analysis by embedding over several weeks in a virtual F-14A Tomcat squadron in the multiplayer combat flight simulator DCS World (DCS) (2008). Based on formative interviews with DCS pilots, we design a web-based game analytics framework for rendering 3D telemetry from the flight simulator in a live 3D player, incorporating many of the data displays and visualizations requested by the participants. We then evaluate the framework with real mission data from several air-to-air engagements involving the virtual squadron. Our findings highlight the key role of 3D telemetry playback in competitive multiplayer gaming.Item Promoting Rich and Low-Burden Self-Tracking With Multimodal Data Input(2022) Luo, Yuhan; Choe, Eun Kyoung; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Manual tracking of personal data offers many benefits such as increased engagement and situated awareness. However, existing self-tracking tools often employ touch-based input to support manual tracking, imposing a heavy input burden and limiting the richness of the collected data. Inspired by speech's fast and flexible nature, this dissertation examines how speech input works with traditional touch input to manually capture personal data in different contexts: food practice, productivity, and exercise. As a first step, I conducted co-design workshops with registered dietitians to explore opportunities for customizing food trackers composed of multimodal input. The workshops generated diverse tracker designs to meet dietitians' information needs, with a wide range of tracking items, timing, data format, and input modalities. In the second study, I specifically examined how speech input supports capturing everyday food practice. I created FoodScrap, a speech-based food journaling app, and conducted a data collection study, in which FoodScrap not only collected rich details of meals and food decisions, but was also recognized for encouraging self-reflection. To further integrate touch and speech on mobile phones, I developed NoteWordy, a multimodal system integrating touch and speech input to capture multiple types of data. Through deploying NoteWordy in the context of productivity tracking, I found several input patterns varying by the data type as well as participants' input habits, error tolerance, and social surroundings. Additionally, speech input helped faster entry completion and enhanced the richness of the free-form text. Furthermore, I expanded the research scope by exploring speech input on smart speakers by developing TandemTrack, a multimodal exercise assistant coupling a mobile app and an Alexa skill. In a four-week deployment study, TandemTrack demonstrated the convenience of the hands-free speech input to capture exercise data and acknowledged the importance of visual feedback on the mobile app to help with data exploration. Across these studies, I describe the strengths and limitations of speech as an input modality to capture personal data in various contexts, and discuss opportunities for improving the data capture experience with natural language input. Lastly, I conclude the dissertation with design recommendations toward a low-burden, rich, and reflective self-tracking experience.