摘要
当前,各高校都非常重视程序设计类课程的教学改革。在实现高质量教育发展的进程中,程序设计类课程混合式教学实践面临一系列新问题。文章基于理论依据、教学目标、学习者分析、操作程序、实现条件、教学评价和支持系统等七个方面,构建了以知识图谱、认知地图和算法协同能力为基础的混合式教学模式。研究发现:基于知识图谱的混合式教学模式能帮助构建课程知识点的相关关系,认知地图能实现学生个性化自主学习;设定以德育与思政、知识、能力、情感态度与价值观为教学目标,通过协同推荐算法,破除学生在项目中出现的“搭便车”困境;从课前、课中和课后三个阶段全过程监测学生学习过程,同时搭建线上线下融合式教学平台,从而全面掌握学生的学习情况;从教学内容、教学方法、学生参与、成效与教学反馈等方面形成完备的课程评价体系。
Currently,all universities prioritize the pedagogical reform of programming courses.During the pursuit of high-quality education,blended teaching practices of programming courses encounter new challenges.This paper constructs a blended teaching model based on knowledge graphs,cognitive mapping and algorithm-supported collaboration ability,around seven aspects:theoretical framework,teaching objectives,learner analysis,operating procedures,implementation conditions,teaching evaluation,and support systems.The findings are as follows:first,the blended teaching model based on knowledge graph can help build the correlation of knowledge points in the course,and cognitive map can realize students'personalized independent learning;secondly,this study sets moral education,ideology and politics,knowledge,ability,emotional attitude and values as the teaching objectives,and breaks the"free rider"dilemma of students in the project through collaborative recommendation algorithm;thirdly,the process of students'learning is comprehensively monitored throughout the pre-class,in-class,and post-class stages,and an integrated online and offline teaching platform is built to fully grasp students'learning situation;finally,a complete course evaluation system is formed from the aspects of teaching content,teaching methods,student participation,effectiveness and teaching feedback.
作者
赖新峰
肖斌
Lai Xinfeng;Xiao Bin
出处
《教育学术月刊》
北大核心
2025年第4期57-63,70,共8页
Education Research Monthly
基金
国家自然科学基金项目“碳减排政策下离岸外包生产运营的复杂性及演化研究”(编号:72261013)
江西省高校教学改革研究课题“基于知识图谱的程序设计类课程混合式教学改革研究”(编号:JXJG-22-4-17)
江西财经大学教育教学改革研究项目“基于认知地图的程序设计语言类课程改革研究”(编号:JG2022036)。
关键词
知识图谱
混合式教学模式
协同学习
相似度推荐
knowledge graph
blended teaching model
collaborative learning
similarity recommendation
