Exploration and Practice of Case-Based Teaching Integrated with Project-Driven Teaching Model: A Case Study of the Course "Principles of Machine Vision Detection"

Abstract

Learning and mastering machine vision technology is one of the core competencies for university graduates' employment and entrepreneurship. China is the largest market for machine vision, and there is a talent gap of 9.5 million in the industrial AI field, represented by machine vision. Machine vision is a multidisciplinary, integrated technology that involves knowledge from multiple fields. Traditional teaching methods have significant limitations when faced with the rapid updates and high demands of this technology. This paper explores the reform of the teaching model by combining case-based teaching with project-driven methods in the course "Principles of Machine Vision Detection." Through the organic integration of case-based teaching and project-driven learning, the model aims to enhance students' practical skills, innovation abilities, and problem-solving capabilities. The paper focuses on analyzing the theoretical foundations, implementation paths, practical effects, and challenges of this teaching model, and proposes corresponding improvements based on the course's practical outcomes. The research results show that the integration of case-based teaching with project-driven learning has a significant impact on the teaching of "Principles of Machine Vision Detection." It enhances students' practical and innovative capabilities and their ability to collaborate in teams, offering valuable insights into cultivating innovative talents that meet national strategic needs.