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Jiang Hu

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Jiang Hu

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Publications by Jiang Hu (bibliography)

» 2007 «

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Hu, Jiang, Winterboer, Andi, Nass, Clifford, Moore, Johanna D. and Illowsky, Rebecca (2007): Context & usability testing: user-modeled information presentation in easy and difficult driving conditions. In: Proceedings of ACM CHI 2007 Conference on Human Factors in Computing Systems 2007. pp. 1343-1346. Available online

“A 2x2 enhanced Wizard-of-Oz experiment (N = 32) was conducted to compare two different approaches to presenting information to drivers in easy and difficult driving conditions. Data of driving safety, evaluation of the spoken dialogue system, and perception of self were analyzed. Results show that the user-modeled summarize-and-refine (UMSR) approach led to more efficient information retrieval than did the summarize-and-refine (SR) approach. However, depending on driving condition, higher efficiency did not always translate into pleasant subjective experience. Implications for usability testing and interface design were presented, followed by discussions of future research directions.”

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» 2006 «

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Brzozowski, Mike, Carattini, Kendra, Klemmer, Scott R., Mihelich, Patrick, Hu, Jiang and Ng, Andrew Y. (2006): groupTime: preference based group scheduling. In: Proceedings of ACM CHI 2006 Conference on Human Factors in Computing Systems 2006. pp. 1047-1056. Available online

“As our business, academic, and personal lives continue to move at an ever-faster pace, finding times for busy people to meet has become an art. One of the most perplexing challenges facing groupware is effective asynchronous group scheduling (GS). This paper presents a lightweight interaction model for GS that can extend its reach beyond users of current group calendaring solutions. By expressing availability in terms of preferences, we create a flexible framework for GS that preserves plausible deniability while exerting social pressure to encourage honesty among users. We also propose an ontology that enables us to model user preferences with machine learning, predicting user responses to further lower cognitive load. The combination of visualization/direct manipulation with machine learning allows users to easily and efficiently optimize meeting times. We also suggest resulting design implications for this class of intelligent user interfaces.”

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Pearson, Jamie, Hu, Jiang, Branigan, Holly P., Pickering, Martin J. and Nass, Clifford (2006): Adaptive language behavior in HCI: how expectations and beliefs about a system affect users' word choice. In: Proceedings of ACM CHI 2006 Conference on Human Factors in Computing Systems 2006. pp. 1177-1180. Available online

“People display adaptive language behaviors in face-to-face conversations, but will computer users do the same during HCI? We report an experiment (N=20) demonstrating that users' use of language (in terms of lexical choice) is influenced by their beliefs and expectations about a system: When users believe that the system is unsophisticated and restricted in capability, they adapt their language to match the system's language more than when they believe the system is relatively sophisticated and capable. Moreover, this tendency is based entirely on users' expectations about the system; it is unaffected by the actual behavior that the system exhibits. Our results demonstrate that interface design engenders particular beliefs in users about a system's capabilities, and that these beliefs can determine the extent to which users adapt to the system. We argue that such effects can be leveraged to improve the quality and effectiveness of human-computer interactions.”

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