面向机器学习的计算机课程认知分析与教学改革研究  

Cognitive Analysis and Teaching Reform of Computer Courses for Machine Learning

作　　者：何小利 张博 宋钰 吴亚东 He Xiaoli;Zhang Bo;Song Yu;Wu Yadong(School of Computer Science and Engineering,Sichuan University of Science and Engineering,Zigong 643000,China;Network Information Management Center,Sichuan University of Science and Engineering,Zigong 643000,China)

机构地区：[1]四川轻化工大学计算机科学与工程学院,四川自贡643000 [2]四川轻化工大学网络中心,四川自贡643000

出　　处：《洛阳师范学院学报》2024年第5期85-90,共6页Journal of Luoyang Normal University

基　　金：教育部高等教育司第二批产学合作协同育人项目(202102101);四川轻化工大学教学改革研究项目(JG-2402);四川省高校重点实验室基金项目(2022WYY02)。

摘　　要：为了深入了解和掌握学生对计算机课程的认知和学习情况,针对我校学生进行了有关计算机课程认知的问卷调查.随后利用机器学习中的KNN、随机森林和神经网络等技术建立了预测模型,对收集到的数据进行了仔细的清理和可视化处理,并进行分析.基于分析结果,提出以下建议:其一,采取个性化的教学策略;其二,加强与专业相关的课程内容;其三,建议采用多样化的教学方法;其四,提供多样化的编程语言课程,并不断优化教材的质量和内容.This research aims to investigating students’cognition of computer courses in Sichuan University of Science and Engineering with questionnaires so as to understand students’cognition and learning of computer courses.The researchers cleaned and visualized the collected data,and established a prediction model for in-depth analysis with K-Nearest Neighbors(KNN),random forest and neural network in machine learning.Based on the analysis results,this research puts forward four suggestions.Firstly,it is necessary to adopt personalized teaching strategies.Secondly,it is necessary to optimize the contents of specialized courses.Thirdly,it is necessary to adopt diversified teaching methods.Finally,it is necessary to provide diversified programming language courses,and continuously optimize the quality and contents of textbooks.

关 键 词：机器学习 大学生 计算机课程 C语言 PYTHON 预测模型 教学改革 

分 类 号：C97[经济管理—劳动经济] F272.92