Intelligent Computer-Aided Instruction Modeling and a Method to Optimize Study Strategies for Parallel Robot Instruction

Abstract

Parallel robots are known for their strong bearing capability and high kinematic accuracy, but they are relatively difficult to design and to teach. This paper addresses this difficulty by presenting an intelligent computer-aided instruction (ICAI) modeling method for parallel robot instruction. The paper analyzes, with reference to their incoming educational profile, Mechatronics students' cognitive processes while acquiring knowledge of parallel robots; it also compares the educational benefits of various methods of teaching this topic. The ICAI model for teaching parallel robots is rooted in machine learning, using information fusion methods based on an artificial neural network (ANN). Two terms of using the ICAI model have validated the method's effectiveness in teaching parallel robots, providing a rational study strategy and improving the students' learning process.