An intelligent smart tutor system based on emotion analysis and recommendation engine
K. Meenakshi, R. Sunder, Aditya Kumar, Nitesh Kumar Sharma · 2017
One of the core model of the Tutor System is considered to be Student model, which pays special attention to student's cognitive and affective states and their evolution as the learning process advances. As the student works step-by-step through their problem solving process the system engages in a process called model tracing. Anytime the student model deviates from the domain model the system identifies, or flags, that an error has occurred. This helps in the shorter feedback cycle for the student and help them in overcoming these shortcomings through the recommendation engine. The tutor model accepts information from the domain and student models and makes choices about tutoring strategies and actions. At any point in the problem-solving process the learner may request guidance on what to do next, relative to their current location in the model. Knowledge tracing tracks the learner's progress from problem to problem and builds a profile of strengths and weaknesses relative to the production rules. The tutor system gives a visual graph of the learner's success in each of the monitored skills related to solving problems. When a learner requests a hint, or an error is flagged, the know ledge tracing data and the skill meter are updated in real-time. The Another core feature of the tutor system is its recommendation engine which on the basis of the skill meter that is updated at the real time will find what all are the things that the student may be interested on and suggest him the path that could be more fruitful. And last the tutor system is able to builds student profiles while observing student performance throughout the interaction of the system.