Saleema Amershi
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Publications by Saleema Amershi (bibliography)
» 2009 «
Hoffmann, Raphael, Amershi, Saleema, Patel, Kayur, Wu, Fei, Fogarty, James and Weld, Daniel S. (2009): Amplifying community content creation with mixed initiative information extraction. In: Proceedings of ACM CHI 2009 Conference on Human Factors in Computing Systems 2009. pp. 1849-1858. Available online
“Although existing work has explored both information extraction and community content creation, most research has focused on them in isolation. In contrast, we see the greatest leverage in the synergistic pairing of these methods as two interlocking feedback cycles. This paper explores the potential synergy promised if these cycles can be made to accelerate each other by exploiting the same edits to advance both community content creation and learning-based information extraction. We examine our proposed synergy in the context of Wikipedia infoboxes and the Kylin information extraction system. After developing and refining a set of interfaces to present the verification of Kylin extractions as a non primary task in the context of Wikipedia articles, we develop an innovative use of Web search advertising services to study people engaged in some other primary task. We demonstrate our proposed synergy by analyzing our deployment from two complementary perspectives: (1) we show we accelerate community content creation by using Kylin's information extraction to significantly increase the likelihood that a person visiting a Wikipedia article as a part of some other primary task will spontaneously choose to help improve the article's infobox, and (2) we show we accelerate information extraction by using contributions collected from people interacting with our designs to significantly improve Kylin's extraction performance.”
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Amershi, Saleema and Morris, Meredith Ringel (2009): Co-located collaborative web search: understanding status quo practices. In: Proceedings of ACM CHI 2009 Conference on Human Factors in Computing Systems 2009. pp. 3637-3642. Available online
“Co-located collaborative Web search is a surprisingly common activity, despite the fact that Web browsers and search engines are not designed to support collaboration. We report the findings of two studies (a diary study and an observational study) that provide insights regarding the frequency of co-located collaborative searching, the strategies participants use, and the pros and cons of these strategies. We then articulate design implications for next-generation tools that could enhance the experience of co-located collaborative search.”
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» 2008 «
Amershi, Saleema and Morris, Meredith Ringel (2008): CoSearch: a system for co-located collaborative web search. In: Proceedings of ACM CHI 2008 Conference on Human Factors in Computing Systems April 5-10, 2008. pp. 1647-1656. Available online
“Web search is often viewed as a solitary task; however, there are many situations in which groups of people gather around a single computer to jointly search for information online. We present the findings of interviews with teachers, librarians, and developing world researchers that provide details about users' collaborative search habits in shared-computer settings, revealing several limitations of this practice. We then introduce CoSearch, a system we developed to improve the experience of co-located collaborative Web search by leveraging readily available devices such as mobile phones and extra mice. Finally, we present an evaluation comparing CoSearch to status quo collaboration approaches, and show that CoSearch enabled distributed control and division of labor, thus reducing the frustrations associated with shared-computer searches, while still preserving the positive aspects of communication and collaboration associated with joint computer use.”
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Amershi, Saleema, Carenini, Giuseppe, Conati, Cristina, Mackworth, Alan K. and Poole, David (2008): Pedagogy and usability in interactive algorithm visualizations: Designing and evaluating CIspace. In Interacting with Computers, 20 (1) pp. 64-96
“Interactive algorithm visualizations (AVs) are powerful tools for teaching and learning concepts that are difficult to describe with static media alone. However, while countless AVs exist, their widespread adoption by the academic community has not occurred due to usability problems and mixed results of pedagogical effectiveness reported in the AV and education literature. This paper presents our experiences designing and evaluating CIspace, a set of interactive AVs for demonstrating fundamental Artificial Intelligence algorithms. In particular, we first review related work on AVs and theories of learning. Then, from this literature, we extract and compile a taxonomy of goals for designing interactive AVs that address key pedagogical and usability limitations of existing AVs. We advocate that differentiating between goals and design features that implement these goals will help designers of AVs make more informed choices, especially considering the abundance of often conflicting and inconsistent design recommendations in the AV literature. We also describe and present the results of a range of evaluations that we have conducted on CIspace that include semi-formal usability studies, usability surveys from actual students using CIspace as a course resource, and formal user studies designed to assess the pedagogical effectiveness of CIspace in terms of both knowledge gain and user preference. Our main results show that (i) studying with our interactive AVs is at least as effective at increasing student knowledge as studying with carefully designed paper-based materials; (ii) students like using our interactive AVs more than studying with the paper-based materials; (iii) students use both our interactive AVs and paper-based materials in practice although they are divided when forced to choose between them; (iv) students find our interactive AVs generally easy to use and useful. From these results, we conclude that while interactive AVs may not be universally preferred by students, it is beneficial to offer a variety of learning media to students to accommodate individual learning preferences. We hope that our experiences will be informative for other developers of interactive AVs, and encourage educators to exploit these potentially powerful resources in classrooms and other learning environments.”
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» 2007 «
Amershi, Saleema and Conati, Cristina (2007): Unsupervised and supervised machine learning in user modeling for intelligent learning environments. In: Proceedings of the 2007 International Conference on Intelligent User Interfaces 2007. pp. 72-81. Available online
“In this research, we outline a user modeling framework that uses both unsupervised and supervised machine learning in order to reduce development costs of building user models, and facilitate transferability. We apply the framework to model student learning during interaction with the Adaptive Coach for Exploration (ACE) learning environment (using both interface and eye-tracking data). In addition to demonstrating framework effectiveness, we also compare results from previous research on applying the framework to a different learning environment and data type. Our results also confirm previous research on the value of using eye-tracking data to assess student learning.”
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Mar 20th, 2010
Changes to this page (author)
20 Feb 2010: Enabled abstracts to be shown on Saleema Amershi's author page.05 Jun 2009: Author was edited 09 May 2009: Author was edited
09 May 2009: Author was edited
12 May 2008: Author was edited
24 Jul 2007: Author was added to the bibliography
