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Maria Tomprou

dblp:194/9677 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2019
0000-0003-3173-1677ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 2 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
2 papers
Collaborative and social computing · 92% Wearable and physiological sensing · 8%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Collaborative and social computing
online communities
0.412019
Career Mentoring in Online Communities: Seeking and Receiving Advice from an Online Community · CHI 2019
Collaborative and social computing › online communities
question answering communities
0.412019
Career Mentoring in Online Communities: Seeking and Receiving Advice from an Online Community · CHI 2019
Collaborative and social computing
collective intelligence
0.312017
Deep Structures of Collaboration: Physiological Correlates of Collective Intelligence and Group Satisfaction · CSCW 2017

Methods — techniques the papers use, named apart from their topics

quantitative analysis · 0.4qualitative analysis · 0.4physiological sensing · 0.3laboratory experiment · 0.3
YearPublicationVenuePosition
2019 Career Mentoring in Online Communities: Seeking and Receiving Advice from an Online Community
abstract
Although people frequently seek mentoring or advice for their career, most mentoring is performed in person. Little research has examined the nature and quality of career mentoring online. To address this gap, we study how people use online Q&A forums for career advice. We develop a taxonomy of career advice requests based on a qualitative analysis of posts in a career-related online forum, identifying three key types: best practices, career threats, and time-sensitive requests. Our quantitative analysis of responses shows that both requesters and external viewers value general information, encouragement, and guidance, but not role modeling. We found no relation between the type of requests and features of responses, nor differences in responses valued by requesters versus external viewers. We present design recommendations for supporting online career advice exchange.
Maria Tomprou, Laura A. Dabbish, Robert E. Kraut, Fannie Liu
CHI1
2017 Deep Structures of Collaboration: Physiological Correlates of Collective Intelligence and Group Satisfaction
abstract
Collective intelligence (CI), a group's capacity to perform a wide variety of tasks, is a key factor in successful collaboration. Group composition, particularly diversity and member social perceptiveness, are consistent predictors of CI, but we have limited knowledge about the mechanisms underlying their effects. To address this gap, we examine how physiological synchrony, as an indicator of coordination and rapport, relates to CI in computer-mediated teams, and if synchrony might serve as a mechanism explaining the effect of group composition on CI. We present results from a laboratory experiment where 60 dyads completed the Test of Collective Intelligence (TCI) together online and rated their group satisfaction, while wearing physiological sensors. We find that synchrony in facial expressions (indicative of shared experience) was associated with CI and synchrony in electrodermal activity (indicative of shared arousal) with group satisfaction. Furthermore, various forms of synchrony mediated the effect of member diversity and social perceptiveness on CI and group satisfaction. Our results have important implications for online collaborations and distributed teams.
Prerna Chikersal, Maria Tomprou, Young Ji Kim, Anita Williams Woolley, Laura A. Dabbish
CSCW2