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.
SNAPP is a software tool that allows users to visualize the network of interactions resulting from discussion forum posts and replies. The network visualisations of forum interactions provide an opportunity for teachers to rapidly identify patterns of user behaviour – at any stage of course progression. SNAPP has been developed to extract all user interactions from various commercial and open source learning management systems (LMS) such as BlackBoard (including the former WebCT), and Moodle. SNAPP is compatible for both Mac and PC users and operates in Internet Explorer, Firefox and Safari.
Most of the student data generated from Learning Management Systems (LMS) include reports on the number of sessions (log-ins), dwell time (how long the log-in lasted) and number of downloads. This tells us a lot about content retrieval in a transmission model of learning and teaching, but not about how students are interacting with each other in more socio-constructivist practice. Discussion forum activity is a good indicator of student interactions and is systemically captured by most LMS. SNAPP uses information on who posted and replied to whom, and what major discussions were about, and how expansive they were, to analyse the interactions of a forum and display it in a Social Network Diagram. The following figures illustrate how SNAPP re-interprets discussion forum postings into a network diagram.
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)