In the Developmental Intelligence Laboratory, we are interested in understanding fundamental cognitive mechanisms of human intelligence, human learning, and human interaction and communication in everyday activities. To do so, we collect and analyze micro-level multimodal behavioral data using state-of-the-art sensing and computational techniques. One of our primary research aims is to understand human learning and early development. How do young children acquire fundamental knowledge of the world? How do they select and process the information around them and learn from scratch? How do they learn to move their bodies and to communicate and interact with others? Learning this kind of knowledge and skills is the core of human intelligence. To understand how human learners achieve the learning goal, the primary approach in our research is to attach GoPro-like cameras on the head of young children to record egocentric video from their point of view. Using this innovative approach, we've been collecting video data of children’s everyday activities, such as playing with their parents and their peers, reading books with parents and caregivers, and playing outside. We've been using state-of-the-art machine learning and data mining approaches to analyze high-density behavioral data. This research line will ultimately solve the mystery on why human children are such efficient learners. Moreover, the findings from our research will be used to help improve learning of children with developmental deficits. A complimentary research line is to explore how human learning can teach us about how machines can learn. Can we model and simulate how a human child learns and develops? To this end, our research aims at bridging and connecting developmental science in psychology and machine learning and computer vision in computer science.
The Social Networks Adapting Pedagogical Practice (SNAPP) tool performs real-time social network analysis and visualization of discussion forum activity within popular commercial and open source Learning Management Systems (LMS). SNAPP essentially serves as a diagnostic instrument, allowing teaching staff to evaluate student behavioral patterns against learning activity design objectives and intervene as required a timely manner.
J. Verma, S. Agrawal, B. Patel, and A. Patel. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 5 (1):
41 - 51(February 2016)
J. Verma, S. Agrawal, B. Patel, and A. Patel. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 5 (1):
11(February 2016)
S. Chua, C. Tagg, M. Sharples, and B. Rienties. Workshop at the 7th International Learning Analytics and Knowledge Conference. Simon Fraser University, Vancouver, Canada, 13-17 March 2017, page 36-62. (2017)
M. Atzmueller, L. Thiele, G. Stumme, and S. Kauffeld. Proc. ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, New York, NY, USA, ACM Press, (2016)
M. Atzmueller. Solving Large Scale Learning Tasks: Challenges and Algorithms. Festschrift in Honour of Prof. Dr. Katharina Morik, volume 9580 of LNCS, Springer Verlag, (2016)
M. Atzmueller, L. Thiele, G. Stumme, and S. Kauffeld. Proc. ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, New York, NY, USA, ACM Press, (2016)
C. Scholz, M. Atzmueller, and G. Stumme. Proc. 21st Intl. Symposium on Methodologies for Intelligent Systems, Heidelberg, Germany, Springer Verlag, (2014)
M. Atzmueller, L. Thiele, G. Stumme, and S. Kauffeld. Proc. ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, New York, NY, USA, ACM Press, (2016)