
NSF Org: |
IIS Division of Information & Intelligent Systems |
Recipient: |
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Initial Amendment Date: | July 23, 2013 |
Latest Amendment Date: | August 1, 2016 |
Award Number: | 1302522 |
Award Instrument: | Continuing Grant |
Program Manager: |
William Bainbridge
IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
Start Date: | August 1, 2013 |
End Date: | July 31, 2018 (Estimated) |
Total Intended Award Amount: | $1,200,000.00 |
Total Awarded Amount to Date: | $1,200,000.00 |
Funds Obligated to Date: |
FY 2016 = $78,000.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
5000 FORBES AVE PITTSBURGH PA US 15213-3890 (412)268-8746 |
Sponsor Congressional District: |
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Primary Place of Performance: |
5000 Forbes Avenue Pittsburgh PA US 15213-3890 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | HCC-Human-Centered Computing |
Primary Program Source: |
01001617DB NSF RESEARCH & RELATED ACTIVIT |
Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
Online deliberation seeks to improve group decision making by accessing diverse expertise and experience, informing and marshaling evidence in a fruitful exchange of ideas. Successful deliberation environments can bring great benefits, such as broadening participation, tapping a greater range of knowledge, testing ideas against each other, and fostering appreciation of other views. However, for large and complex problem spaces that generate extensive discussion, it is difficult for would-be participants to find where they could best make a contribution, to understand how various contributions fit together, or to grasp the contingencies between needs and contributions.
To address this problem, this project will develop and test a system that provides a personalized view of a large information space that reveals the shape and foci of contributions in a way that reflects the goals, expertise, and interests of each user. The system will allow participants to see how their goals and interests match current themes and to find groups of people and related sets of contributions that would be of interest. To do this, the research will integrate insights from sociolinguistics with state-of-the-art latent variable modeling techniques from the field of language technologies to extend prior work in the areas of perspective and stance modeling in order to identify the necessary structure in textual data to enable personalized information extraction, summarization, and presentation.
The project includes archival data analysis to develop algorithms and data representations, experiments to test the value to users of various ways of representing topics and social networks, a staged series of deployments for formative and summative evaluation, and the development of tool architecture and user interfaces to support experimentation and deployment. Through these activities, the investigators will systematically explore the effects of design decisions on participation, navigability and the nature of the deliberation.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
PI Herbsleb and Co-PI Rose worked with a number of PhD students to make fundamental contributions to our scientific understanding and ability to support personalized environments for online deliberation. We accomplished this through several complementary strands of work:
Navigability and similarity. One approach to generating personalized views of a deliberation space is to allow a user to select one or more proposals of interest, and then organize the remaining proposals based on their similarity to the selection. In order to do this effectively, it is necessary to be able to compute document similarity automatically. Latent Dirichlet Allocation (LDA) is a popular, state-of-the-art technique for identifying the topic content of a document, contents which can then be compared among documents to compute similarity. Yet there has been very little work to see if LDA-based similarity measures actually reflect similarity as judged by humans. We conducted a series of studies that found LDA generally corresponds well to human similarity judgments, but there are specific types of cases where does not, for reasons we were able to identify. These studies provide a firm basis for decisions about when and how LDA-based similarity is actually useful for automated structuring of document spaces. We used these results to develop a prototype ?find similar proposals? functionality for the MIT Climate Co-Lab as an example of how this work can be applied in online spaces where deliberation occurs.
Exposing deliberation. Most online deliberation system have two types of content: the subject matter content (e.g., Wikipedia article pages) and the record of deliberations about that content (e.g., Wikipedia talk pages). These pages can be separate, so that a user seeking content is very unlikely to encounter deliberations, or they can be tightly bound together (e.g., Reddit) so that deliberation and content are encountered together. Using a study paradigm that builds on the computational work described above, we explored the consequences of this design decision by examining the effect of exposure to deliberation on perceptions of content quality. We found that exposure to deliberation generally lowered perception of quality, particularly when the deliberation revealed conflict. Yet users felt they learned more when encountering some kinds of conflict. Our work provides guidance for designing the relation of deliberation and content to achieve particular aims.
Online Interactions. Massive Open Online Courses (MOOCs) have great potential for enhancing educational opportunities, particularly when learning happens in teams, with ongoing discussion about the material. Yet it is very hard to form good online teams, especially given the relatively high dropout rates typical of MOOCs. We experimented with an automated predictive measure of collaboration potential using an analysis of deliberative interactions in an online discussion space as a basis for construction of teams of students. Compared to teams selected randomly, teams based on predicted collaboration potential as measured during deliberation had better team performance and a higher level of communication satisfaction. This work has formed the foundation for a series of follow up studies in other online courses.
Last Modified: 11/30/2018
Modified by: James D Herbsleb
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