This dissertation describes the development of a state-of-the-art immersive media environment and its potential to motivate high school youth with autism to vocally express themselves. Due to the limited availability of media environments in public education settings, studies on the use of such systems in special education contexts are rare. A study called Sea of Signs utilized the Situated Multimodal Art Learning Lab (SMALLab), to present a custom-designed conversational scenario for pairs of youth with autism. Heuristics for building the scenario were developed following a 4-year design-based research approach that fosters social interaction, communication, and self-expression through embodied design. Sea of Signs implemented these heuristics through an immersive experience, supported by spatial and audio-visual feedback that helped clarify and reinforce students' vocal expressions within a partner-based conversational framework. A multiple-baseline design across participants was used to determine the extent to which individuals exhibited observable change as a result of the activity in SMALLab. Teacher interviews were conducted prior to the experimental phase to identify each student's pattern of social interaction, communication, and problem-solving strategies in the classroom. Ethnographic methods and video coding were used throughout the experimental phase to assess whether there were changes in (a) speech duration per session and per turn, (b) turn-taking patterns, and (c) teacher prompting per session. In addition, teacher interviews were conducted daily after every SMALLab session to further triangulate the nature of behaviors observed in each session. Final teacher interviews were conducted after the experimental phase to collect data on possible transfer of behavioral improvements into students' classroom lives beyond SMALLab. Results from this study suggest that the activity successfully increased independently generated speech in some students, while increasing a focus on seeking out social partners in others. Furthermore, the activity indicated a number of future directions in research on the nature of voice and discourse, rooted in the use of aesthetics and phenomenology, to augment, extend, and encourage developments in directed communication skills for youth with autism.

Online social networks, including Twitter, have expanded in both scale and diversity of content, which has created significant challenges to the average user. These challenges include finding relevant information on a topic and building social ties with like-minded individuals. The fundamental question addressed by this thesis is if an individual can leverage social network to search for information that is relevant to him or her. We propose to answer this question by developing computational algorithms that analyze a user's social network. The features of the social network we analyze include the network topology and member communications of a specific user's social network. Determining the "social value" of one's contacts is a valuable outcome of this research. The algorithms we developed were tested on Twitter, which is an extremely popular social network. Twitter was chosen due to its popularity and a majority of the communications artifacts on Twitter is publically available. In this work, the social network of a user refers to the "following relationship" social network. Our algorithm is not specific to Twitter, and is applicable to other social networks, where the network topology and communications are accessible. My approaches are as follows. For a user interested in using the system, I first determine the immediate social network of the user as well as the social contacts for each person in this network. Afterwards, I establish and extend the social network for each user. For each member of the social network, their tweet data are analyzed and represented by using a word distribution. To accomplish this, I use WordNet, a popular lexical database, to determine semantic similarity between two words. My mechanism of search combines both communication distance between two users and social relationships to determine the search results. Additionally, I developed a search interface, where a user can interactively query the system. I conducted preliminary user study to evaluate the quality and utility of my method and system against several baseline methods, including the default Twitter search. The experimental results from the user study indicate that my method is able to find relevant people and identify valuable contacts in one's social circle based on the query. The proposed system outperforms baseline methods in terms of standard information retrieval metrics.

This thesis investigates the role of activity visualization tools in increasing group awareness at the workspace. Today, electronic calendaring tools are widely used in the workplace. The primary function is to enable each person maintain a work schedule. They also are used to schedule meetings and share work details when appropriate. However, a key limitation of current tools is that they do not enable people in the workplace to understand the activity of the group as a whole. A tool that increases group awareness would promote reflection; it would enable thoughtful engagement with one's co-workers. I have developed two tools: the first tool enables the worker to examine detailed task information of one's own tasks, within the context of his/her peers' anonymized task data. The second tool is a public display to promote group reflection. I have used an iterative design methodology to refine the tools. I developed ActivityStream desktop tool that enables users to examine the detailed information of their own activities and the aggregate information of other peers' activities. ActivityStream uses a client-server architecture. The server collected activity data from each user by parsing RSS feeds associated with their preferred online calendaring and task management tool, on a daily basis. The client software displays personalized aggregate data and user specific tasks, including task types. The client display visualizes the activity data at multiple time scales. The activity data for each user is represented though discrete blocks; interacting with the block will reveal task details. The activity of the rest of the group is anonymized and aggregated. ActivityStream visualizes the aggregated data via Bezier curves. I developed ActivityStream public display that shows a group people's activity levels change over time to promote group reflection. In particular, the public display shows the anonymized task activity data, over the course of one year. The public display visualizes data for each user using a Bezier curve. The display shows data from all users simultaneously. This representation enables users to reflect on the relationships across the group members, over the course of one year. The survey results revealed that users are more aware of their peers' activities in the workspace.