{"id":7398,"date":"2016-03-04T14:03:00","date_gmt":"2018-05-30T14:35:00","guid":{"rendered":"https:\/\/mediax.stanford.edu\/kwp-bailenson\/"},"modified":"2018-11-15T10:28:22","modified_gmt":"2018-11-15T18:28:22","slug":"kwp-bailenson","status":"publish","type":"page","link":"https:\/\/mediax.stanford.edu\/research-projects\/kwp-bailenson\/","title":{"rendered":"Using Video Game Platforms to Understand Thinking Styles of People Engaged in Collaboration"},"content":{"rendered":"

From The Theme<\/em>
\nKNOWLEDGE WORKER PRODUCTIVITY<\/a><\/strong><\/p>\n

WHAT IF<\/strong>
\nWhat if we could automatically detect how well members of a group are \u201csynching\u201d on a collaborative project?<\/p>\n

\"VHIL<\/p>\n

WHAT WE SET OUT TO DO<\/strong>
\nWe set out to better understand correlations between degrees of group \u201csync\u201d in collaborative projects on one hand, and team creativity and productivity on the other. Our research involved leveraging machine learning and video game technology to analyze the behavior and interaction of teams of two people (dyads) in a creative task.<\/p>\n

We sought to assess – and then predict – collaborative innovation with bottom up, statistical methods for pattern identification. We used commercial video game technology and machine learning to track participants\u2019 physical movements and nonverbal behavior, identify patterns, and associate them with creativity. We also examined the measures of synchrony in the movement data collected from participants, and explored the effectiveness of top-down hypothesis based predictions.<\/p>\n

WHAT WE FOUND<\/strong>
\nBased on automatically detected measures of nonverbal behavior, our models were able to predict creativity, with a success rate as high as 96%. Using the movement data from both participants was more predictive than using data from just one participant. In addition, we found evidence that synchrony declined as the time lag between nonverbal behavior of the two dyad members increased.<\/p>\n

LEARN MORE<\/strong>
\nmediaX Research Project Update, Fall 2013<\/a><\/p>\n

mediaX Research Theme Update, Spring 2013<\/a><\/p>\n

Watch Videos About This Project<\/a><\/p>\n

VHIL Research: Automatic Detection of Nonverbal Behavior Predicts Learning in Dyadic Interactions<\/a><\/p>\n

PEOPLE BEHIND THE PROJECT<\/strong>
\n\"\"Jeremy Bailenson<\/a><\/strong> is founding director of Stanford University\u2019s Virtual Human Interaction Lab, the Thomas More Storke Professor in the Department of Communication at Stanford, and a Senior Fellow at the Woods Institute for the Environment. He designs and studies virtual reality systems that allow physically remote individuals to meet in virtual space, and explores the manner in which these systems change the nature of verbal and nonverbal interaction. In particular, he explores how virtual reality can change the way people think about education, environmental behavior, and health.<\/p>\n

\"\"Andrea Stevenson Won<\/a><\/strong> is Assistant Professor of Communication at Cornell University, where she directs the Virtual Embodiment Lab. At the time of the project, she was a PhD Candidate in the Department of Communication at Stanford University, researching the capture and expression of nonverbal behavior and the physical and psychological effects of mediated embodiment.<\/p>\n

Wenqing Dai<\/a><\/strong>; Graduate Student, Computer Science<\/p>\n

Le Yu<\/a><\/strong>; Graduate Student, Computer Science<\/p>\n","protected":false},"excerpt":{"rendered":"

From The Theme KNOWLEDGE WORKER PRODUCTIVITY WHAT IF What if we could automatically detect how well members of a group are \u201csynching\u201d on a collaborative project? WHAT WE SET OUT TO DO We set out to better understand correlations between degrees of group \u201csync\u201d in collaborative projects on one hand, and team creativity and productivity […]<\/p>\n","protected":false},"author":3,"featured_media":0,"parent":363,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"tags":[29,47,22,62,24,11,34,51,20,21],"class_list":["post-7398","page","type-page","status-publish","hentry","tag-collaboration","tag-creativity","tag-feedback","tag-games","tag-interaction","tag-machine-learning","tag-presence","tag-productivity","tag-sensors","tag-virtual-reality"],"acf":[],"yoast_head":"\nUsing Video Game Platforms to Understand Thinking Styles of People Engaged in Collaboration - mediaX at Stanford University<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mediax.stanford.edu\/research-projects\/kwp-bailenson\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Using Video Game Platforms to Understand Thinking Styles of People Engaged in Collaboration - mediaX at Stanford University\" \/>\n<meta property=\"og:description\" content=\"From The Theme KNOWLEDGE WORKER PRODUCTIVITY WHAT IF What if we could automatically detect how well members of a group are \u201csynching\u201d on a collaborative project? 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