人工智能基础算法实训课程教学案例研究

Case Study on the Teaching of Artificial Intelligence Basic Algorithm Training Course

为解决现有人工智能课程中多个核心算法实验内容相互脱节、连贯性差等问题,研究者设计了融合多种基础算法的英语知识点画像系统实验教学案例。该案例以英语学科为切入点,基于教育领域所存在的若干有价值数据(培养过程数据、学习成绩数据等),从“基础知识水平—学习投入程度—知识掌握水平”三个角度出发,分别利用决策树分类、K-means聚类等方法对多属性标签进行生成,并对算法进行性能对比。实训框架综合多种机器学习算法对画像系统进行生成,形成了涵盖“系统设计—编码—分析—展示”的综合性实训教学案例。几轮教学实践表明,引入实际项目贯穿实验环节,能够在一定程度上提升学生的创新实践能力和算法的综合运用能力。

In order to solve the problems of disjointed and poorly coherent experimental content of multiple core algorithms in existing artificial intelligence courses,this paper designs an experimental teaching case of an English knowledge profiling system that integrates multiple basic algorithms.The case,based on several valuable data existing in the education field(such as training process data,learning performance data,etc.),takes the English subject as the breakthrough point,starting from the three perspectives of“basic knowledge level-learning engagement level-knowledge mastery level”,using decision tree classification,K-means clustering and other methods to generate multi-properties labels,and comparing the performance of the algorithms.The training framework integrates multiple machine learning algorithms to generate profiling systems,forming a comprehensive training teaching case covering“system design-coding-analysis-display”.Several rounds of teaching practice have shown that introducing projects throughout the experimental process can,to some extent,enhance students’innovative practical ability and comprehensive application ability.