Collaborative supervision of machine learning as a tool for Web-based education: A teaching and learning triangle

Machine-learning applications often suffer bottlenecks due to inefficiency in the human-machine interface. A novel architecture design has been developed to allow expert supervisors to collaborate and cooperate in real-time to alleviate the effects of the bottleneck. Replacing supervisors with students, this architecture also allows for supervised training and collaborative learning of students as well as machine learners. Our attempts to provide Web-based courses to distance learners have highlighted the need for more effective use of the medium for education and appropriate tools to provide the necessary richness of experience. We present our design and an example application to demonstrate how we address some of the shortfalls present in Web-based, distance education.