人工智能背景下的机器学习课程递进式实验教学探索与设计

Exploration and Design of Progressive Experimental Teaching for Machine Learning Courses in the Context of Artificial Intelligence

摘要: 在大模型技术快速发展的背景下，传统机器学习实验教学存在内容滞后、与前沿技术脱节、评价体系单一等问题。研究提出一种“知识贯通–能力递进–评价多维”的递进式实验教学方案，以布鲁姆认知模型为框架构建递进式知识体系、设计认知导向与领域驱动的实验案例库、创新三阶闭环实验教学模式，并建立多维阶梯式评估机制。该方案通过调整认知层级与新技术的要求，帮助学生从理解算法到进行系统创新的能力全方位提升，有效提升学生解决复杂工程问题的能力和教学质量，为培养适应人工智能时代变化需求的高质量人才提供有效方法。

Abstract: In the context of the rapid development of large model technology, traditional machine learning experimental education faces challenges, such as outdated content, disconnection from cutting-edge technologies, and a singular evaluation system. This study proposes a progressive experimental teaching plan characterized by “knowledge integration - capability progression - multidimensional evaluation”. It constructs a progressive knowledge system based on Bloom’s cognitive model, designs an experimental case library that combines cognitive orientation and domain-driven approaches, innovates a three-stage closed-loop experimental teaching model, and establishes a multidimensional ladder-like assessment mechanism. This plan adjusts the cognitive levels to align with the requirements of new technologies, helping students enhance their abilities across the entire process from understanding algorithms to system innovation. It effectively improves students’ ability to solve complex engineering problems and the quality of teaching, providing an effective method for cultivating high-quality talent that can adapt to the changing demands of the artificial intelligence era.