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SUMMARY:Talk Prof Sharon Oviatt\, Monash University
DTSTART:20191106T141500
DTEND:20191106T150000
DTSTAMP:20260924T071642Z
UID:3968f7ad237f2fcc0de816f97e59442b4a4c12957d7df39ac20cfe66
CATEGORIES:Conferences - Seminars
DESCRIPTION:Professor Sharon Oviatt is internationally known for her work 
 on human-centered interfaces\, multimodal-multisensor interfaces\, mobile 
 interfaces\, educational interfaces\, the cognitive impact of computer inp
 ut tools\, and behavioral analytics. She received her PhD at the Universit
 y of Toronto. Her research is known for its pioneering and multidisciplina
 ry style at the intersection of Computer Science\, Psychology\, Linguistic
 s\, and Learning Sciences. Sharon has been recipient of the inaugural ACM-
 ICMI Sustained Accomplishment Award\, National Science Foundation Special 
 Creativity Award\, ACM-SIGCHI CHI Academy Award\, and an ACM Fellow Award 
 for “contributions to the empirical and theoretical foundations of multi
 modal systems\, and to human-centered computer interfaces\,” awarded to 
 the top 1% of the international computing community. She has published a l
 arge volume of high-impact papers (Google Scholar citations >12\,100\; h-i
 ndex 51)\, and is an Associate Editor of the main journals and edited book
  collections in the field of human-centered interfaces. Her recent books 
 include The Design of Future Educational Interfaces (2013\, Routledge Pre
 ss)\, The Paradigm Shift to Multimodality in Contemporary Computer Interfa
 ces (2015\, Morgan-Claypool)\, and the multi-volume Handbook of Multimodal
 -Multisensor Interfaces (co-edited with Bjoern Schuller and others\, 2017-
 2019\, ACM Books).\n \nI Know What You Know: What Hand Movements Reveal a
 bout Domain Expertise \n \nAbstract: \nIn this talk\, I’ll introduce th
 e Human-Centred AI group at Monash\, its main research areas and plans for
  growth\, and topics of special interest related to multimodal behavioral 
 analytics and digital health. Then I will present new research from our la
 b during the last 6 months as an example of multimodal behavioral analytic
 s. In this research\, we investigated whether students’ level of domain 
 expertise can be detected during authentic learning activities by analyzin
 g their physical activity patterns. Using new multimodal behavioral analyt
 ic techniques\, both signal- and representation-level analyses revealed th
 at students varying in mathematics expertise were distinguishable based on
  their hand movements. More expert students reduced manual activity by a s
 ubstantial 50%\, which was evident in fine-grained signal analyses and tot
 al rate of gesturing. This reduction was most apparent on easy-to-moderate
  problems. Interestingly\, further analysis on type of gesturing revealed 
 that more expert students nonetheless selectively produced 62% more iconic
  gestures\, which are known to facilitate inferences about spatial content
 . These findings highlight the close relation between mental state and han
 d movements\, and how more expert students adapt their hand movements to s
 olve harder problems. Limited Resource Theory provides an account for why 
 physical activity is reduced as domain expertise is acquired.\n \n 
LOCATION:RLC D1 661 https://plan.epfl.ch/?room==RLC%20D1%20661
STATUS:CONFIRMED
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