A category-based framework of a self-improving instructional planner

• Main Authors: Legaspi, Roberto S., Sison, Raymund, Numao, Masayuki
• Format: text
• Published: Animo Repository 2006
• Subjects: Intelligent tutoring systems; Machine learning; Learning ability; Computer Sciences; Software Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/3531; https://animorepository.dlsu.edu.ph/context/faculty_research/article/4533/type/native/viewcontent/tjsai.21.94
• Institution: De La Salle University

To have an instructional plan guide the learning process is significant to various teaching styles and an important task in an ITS. Though various approaches have been used to tackle this task, the compelling need is for an ITS to improve on its own the plans established in a dynamic way. We hypothesize that the use of knowledge derived from student categories can significantly support the improvement of plans on the part of the ITS. This means that category knowledge can become effectors of effective plans. We have conceived a Category-based Self-improving Planning Module (CSPM) for an ITS tutor agent that utilizes the knowledge learned from learner categories to support self-improvement. The learning framework of CSPM employs unsupervised machine learning and knowledge acquisition heuristics for learning from experience. We have experimented on the feasibility of CSPM using recorded teaching scenarios.