Developing Computational Intelligence Curriculum Materials to Advance Student Learning for Robot Control and Optimization

摘要

The integration of nature-inspired intelligence in computational intelligence curricula, particularly for robot path planning optimization, represents a significant advancement in both research and education realms.This study introduces a unique pedagogical approach that combines sparrow-dissection and scaffolding with flipped learning (SDS-FL) and ongoing project-based methods.This approach is implemented in a graduate-level course where students explore various nature-inspired algorithms such as particle swarm optimization, genetic algorithms, and bat algorithms, provided with source codes for practical application and adaptation.The flipped classroom model ensures pre-class preparation, enabling students to build foundational knowledge independently.In-class sessions are then focused on collaborative project work and discussions, fostering problem-solving, critical thinking, and analytical skills.This hybrid teaching method, promoting active engagement and practical application, is evaluated for its effectiveness through various assessments, including homework, exams, and feedback surveys.The positive outcomes from this innovative pedagogical approach affirm its success in enhancing students' understanding and application of nature-inspired intelligence in computational intelligence, making the learning process more dynamic and impactful.