Project 4 : Design Interfaces to Enhance Social Interaction in Online Educational Videos
Project | 01
Ph.D. Thesis Research | Designing Conversational Agents to Promote Self-disclosure and Behavior Change - Present
Conversational agents (CAs), as an AI technology, are regarded as one of the most promising technologies and are increasingly applied in many domains. Because CAs provide a fast, convenient, and low-cost communication channel, both scholars and practitioners are keen to develop effective CAs to address the challenges of providing healthcare services and improving people's well-being.
However, effective communication with people through CAs requires people's trust and people's willingness to self-disclose their personal information or experiences with the CAs. To achieve long-term benefits (e.g., addressing chronological health issues), being able to sustain communication between people and the CAs also remains a challenge.
In this dissertation research, we design, implement and evaluate CAs to address the fundamental challenges of effectively eliciting and sustaining people's self-disclosure with the CAs in different social contexts. More specifically, our research will contribute to: 1) effective CA designs that succeed in eliciting people's deep self-disclosure to a CA over time, 2) empirical evidence and deeper understandings of sustaining people's trust of CAs throughout different interaction periods, e.g., with and without the involvement of real mental health professionals, and 3) new design insights of integrating human support in human-CA interaction to promote behavior change. The global pandemic (COVID-19) heightens the challenges of providing healthcare services at all levels, our research will make a timely impact on society and a lasting impact on human-AI research at large.
Project | 02
Independent Project | Investigating Social Interaction and Tipping Behaviors in Live Streaming
Live streaming (e.g., Twitch, Youtube Live) is a proliferating social media generating special social interactions on the platforms. For example, tipping in live streaming, in which viewers buy virtual gifts to reward the streamers. This project explores people's tipping behaviors and how it impacts interactions between viewers and streamers. We analyzed videos of live streams by labeling the viewer-streamer interactions and found that viewers were motivated by the reciprocal acts of streamers. Furthermore, we interviewed many streamers to understand their motivations behind the viewer-streamer interactions. This research contributes to the understanding of tipping behaviors from both streamers and viewers' perspectives. We have published some research papers, please refer them for more detail.
Publications: CHI2018, CSCW2019, MobileHCI2019
Project | A Smart Crowd Donation Platform for Supporting Education and Classroom
Donation-based crowdfunding has the potential to democratize capital raising by soliciting donations directly from the public through the Web and social media. These crowdfunding platforms, however, often function as unregulated open markets, in which there is a minimal intervention to influence donation distribution across projects. In fact, research on crowdfunding hints that donation distribution in most crowdfunding platforms is suboptimal: while the overall success rates of crowdfunding projects are often low, a significant proportion of projects receive donations way over their targets. In this paper, we propose a new donation distributing system that aims to (a) distribute donations more effectively among the projects, and (b) align the allocation of donations with the preferences of donors. An agent-based model was developed to test the proposed system. Results showed that the proposed system not only increased the overall success rates of projects but also led to more successes for projects preferred by donors. Implications to future crowdfunding platforms are discussed.
Project | 03
Publications: IUI 2018, SBP2017, CHI2015, SBP2016
Project | 04
Project | Using Time-Anchored Peer Comments to Enhance Social Interaction in Online Educational Videos
Online learning is increasingly prevalent as an option for self-learning and as a resource for instructional design. Prerecorded video is currently the main medium of online education content delivery and instruction; this affords asynchronicity and flexibility and enables the dissemination of lecture content in a distributed and scalable manner. However, the same properties may impede learners' engagement due to the lack of social interaction and peer support. In this paper, we propose a time-anchored commenting interface to allow online learners who watch the same video clips to exchange comments on them. Comments left by previous learners at specific time points of a video are displayed to new learners when they watch the same video and reach those time points. We investigated how the display of time-anchored comments (dynamic or static) and type of comments (content-related or social-oriented) influenced users' perceived engagement, perceived social interactivity, and learning outcomes. Our results show that dynamically displaying time-anchored comments can indeed enhance learners' perceived social interactivity. Moreover, the content of comments would further affect learners' intention of commenting. Based on our findings, we make various recommendations for the improvement of social interaction and learning experience in online education.
Publications: CHI 2015
Project | 05
Publications: Sensors 2020, TNSRE 2016, CHI 2014, TIM 2015
Master Degree Thesis Research | Applying EEG Analysis to Human-Computer Interaction Design
Human cognitive limitations are becoming a crucial issue in human-computer interaction nowadays and thus an important determinant of overall human performance. This thesis purpose to introduce two methods that applying EEG technology helps us to improve and evaluate human-computer interaction.
We suggested a new approach to detect users’ attention state. Attention monitoring is particularly important for many HCI applications. How to automatically determine a user's visual attention state is challenging since attention involves many complex and internal human cognitive functions. Behavioral observations, such as eye gaze or response to external stimuli, can provide some clues for users' visual attention state; however, the users' cognitive state cannot be easily known. We explored the feasibility of designing an attention monitoring system that can detect if our brain sees a visual stimulus consciously.
Moreover, we used an EEG-based approach to assist usability tests with audio notifications. Audio notifications have become an important way to prompt users. Several studies have been proposed to evaluate audio notifications, but they rarely considered user workload and environmental impact at the same time. We developed an EEG-based approach to evaluate audio notifications by measuring subjects’ auditory perceptual response (mismatch negativity) and attention status (P3a). We demonstrated this approach by two experiments, in which auditory icons were evaluated under different workloads and environments. According to the experiment results, the perceptual effects of audio notifications could be measured objectively.
Based on these technologies, we could directly detect users’ cognitive changes when they interact with our design. Therefore, this thesis used an EEG device to detect and analyze the variation of human cognitive state from visual and auditory perception respectively. We hope to provide new feasible approaches and views to the HCI field.