Talk Prof Sharon Oviatt, Monash University
Event details
| Date | 06.11.2019 |
| Hour | 14:15 › 15:00 |
| Speaker |
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 input tools, and behavioral analytics. She received her PhD at the University of Toronto. Her research is known for its pioneering and multidisciplinary style at the intersection of Computer Science, Psychology, Linguistics, 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 multimodal systems, and to human-centered computer interfaces,” awarded to the top 1% of the international computing community. She has published a large volume of high-impact papers (Google Scholar citations >12,100; h-index 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 Press), The Paradigm Shift to Multimodality in Contemporary Computer Interfaces (2015, Morgan-Claypool), and the multi-volume Handbook of Multimodal-Multisensor Interfaces (co-edited with Bjoern Schuller and others, 2017-2019, ACM Books). |
| Location | |
| Category | Conferences - Seminars |
I Know What You Know: What Hand Movements Reveal about Domain Expertise
Abstract:
In this talk, I’ll introduce the 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 lab during the last 6 months as an example of multimodal behavioral analytics. In this research, we investigated whether students’ level of domain expertise can be detected during authentic learning activities by analyzing their physical activity patterns. Using new multimodal behavioral analytic techniques, both signal- and representation-level analyses revealed that students varying in mathematics expertise were distinguishable based on their hand movements. More expert students reduced manual activity by a substantial 50%, which was evident in fine-grained signal analyses and total 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 hand movements, and how more expert students adapt their hand movements to solve harder problems. Limited Resource Theory provides an account for why physical activity is reduced as domain expertise is acquired.
Abstract:
In this talk, I’ll introduce the 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 lab during the last 6 months as an example of multimodal behavioral analytics. In this research, we investigated whether students’ level of domain expertise can be detected during authentic learning activities by analyzing their physical activity patterns. Using new multimodal behavioral analytic techniques, both signal- and representation-level analyses revealed that students varying in mathematics expertise were distinguishable based on their hand movements. More expert students reduced manual activity by a substantial 50%, which was evident in fine-grained signal analyses and total 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 hand movements, and how more expert students adapt their hand movements to solve harder problems. Limited Resource Theory provides an account for why physical activity is reduced as domain expertise is acquired.
Practical information
- Informed public
- Free
- This event is internal