Several SoCS Faculty Awarded PSEER Funding To Investigate Inclusivity In The Classroom
Three School of Computer Science faculty members have been awarded funding from PSEER to investigate inclusion barriers for underrepresented groups in computer science. Drs Stacey Scott, Andrew Hamilton-Wright, and Ritu Chaturvedi have been awarded $10,000 for their project.
Investigating inclusive curriculum and student support services in computer science
This research project aims to investigate inclusion barriers for female and other underrepresented populations in the computing degree programs offered by the School of Computer Science (SoCS) at the University of Guelph. It also aims to investigate existing best practices that have been used to successfully increase the participation of female and visible minority students in programs, and create a set of recommendations for SoCS tailored to our local context and student population to increase the inclusiveness of our programs.
Interactions with digital information and media are part of everyday life in today’s modern digital information society. Current technologies largely focus on supporting personal interactions with digital information, and geographically distributed collaborative interactions and communications. Yet, face-to-face human interaction is still an important part of many modern work and social places. Existing technologies are awkward and often frustrating to use in this context. My research focuses on the design of novel interface and interaction technologies, and whole interactive systems, which support common everyday face-to-face collaborative and social activities that involve digital information. Past projects have involved the design of interactive large displays, such as digital tabletops and walls, and multi-display environment, but I’m interested in a broad range of technology development that can better support face-to-face group activities.
I use machine learning techniques in decision exploration systems. I am particularly interested in using rule-based association mining techniques to allow visual exploration of risk and certainty in decision-making systems. Application areas are varied, but much of my work has been biophysical: electromyographic (EMG) based muscular disease characterization, EMG and postural fatigue and pain prediction, and EMG and force based characterization of sleep apnea. By combining rule-based systems with statistical certainty, models of decision confidence can be created to allow contingent decision planning and exploration. I am primarily interested in exploring and visualizing these sorts of data domains. In addition, I have an interest in physiological models, such as my EMG simulator, for use in providing gold standard data for validation of EMG based techniques.
My research interests are in data mining techniques with a focus on educational data mining. In particular, my research focuses on the domain and student model components of a web-based tutoring system.