{"id":719014,"date":"2021-01-25T10:03:08","date_gmt":"2021-01-25T18:03:08","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?post_type=msr-research-item&#038;p=719014"},"modified":"2023-02-21T02:56:46","modified_gmt":"2023-02-21T10:56:46","slug":"meetingcoach-an-intelligent-dashboard-for-supporting-effective-inclusive-meetings","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/meetingcoach-an-intelligent-dashboard-for-supporting-effective-inclusive-meetings\/","title":{"rendered":"MeetingCoach: An Intelligent Dashboard for Supporting Effective and Inclusive Meetings"},"content":{"rendered":"<p>Video-conferencing is essential for many companies, but its limitations in conveying social cues can lead to ineffective meetings. We present MeetingCoach, an intelligent post-meeting feedback dashboard that summarizes contextual and behavioral meeting information. Through an exploratory survey (N=120), we identified important signals (e.g., turn taking, sentiment) and used these insights to create a wireframe dashboard. The design was evaluated with in situ participants (N=16) who helped identify the components they would prefer in a post-meeting dashboard. After recording video-conferencing meetings of eight teams over four weeks, we developed an AI system to quantify the meeting features and created personalized dashboards for each participant. Through interviews and surveys (N=23), we found that reviewing the dash-board helped improve attendees\u2019 awareness of meeting dynamics, with implications for improved effectiveness and inclusivity. Based on our findings, we provide suggestions for future feedback system designs of video-conferencing meetings.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Video-conferencing is essential for many companies, but its limitations in conveying social cues can lead to ineffective meetings. We present MeetingCoach, an intelligent post-meeting feedback dashboard that summarizes contextual and behavioral meeting information. Through an exploratory survey (N=120), we identified important signals (e.g., turn taking, sentiment) and used these insights to create a wireframe dashboard. 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