Recommending a personalized lesson plan based on constraint satisfaction and negotiation

Abstract

An appropriate lesson plan is important in teaching and learning. A traditional lesson plan is constructed manually by a teacher with great efforts and difficult to be generated automatically. In this research, we proposed an intelligent agent system able to recommend a personalized lesson plan by using methods in constraint satisfaction problems and negotiation. The constraints consist of teaching goals, teaching time, student abilities, classroom resources and teacher's preference, whereas the recommendation by negotiation system can assist a teacher user to find the coordinated solution under minimum constraint relaxation when no exact solution can be found.