基于隐马尔科夫算法的课程教学质量评价的研究  

Research on Teaching Quality Evaluation Based on Hidden Markov Algorithm

作　　者：肖丽平[1] 田紫君[1] 田景文[1] XIAO Li-ping;TIAN Zi-jun;TIAN Jing-wen(Beijing Union University,Beijing 100101,China)

机构地区：[1]北京联合大学前沿智能技术研究院,北京100101

出　　处：《黑龙江高教研究》2022年第1期151-155,共5页Heilongjiang Researches on Higher Education

基　　金：北京联合大学教育科学研究课题“基于隐马尔可夫模型(HMM)的课程教学质量评价研究”(编号:JK2202017)成果之一;北京市科技重点研发计划(编号:Z19110900910000)成果之一。

摘　　要：以某大学学习某专业课的MOOC+SPOC的学习数据为样本,利用隐马尔科夫算法、数据挖掘技术,建立学生学习行为状态的评估模型,实时评估学生学习行为;教师根据评估结果对教学内容进行调整,对学生学习行为进行干扰,以提高教学质量。实证分析表明,基于隐马尔科夫算法的课程教学质量评价模型具有一定的有效性,分析结果有一定的参考性,方便教师在教学中有的放矢。Taking as samples the learning data of MOOC+SPOC of a certain professional course in a university, this paper uses hidden Markov algorithm and data mining technology to establish the evaluation model of students’ learning behavior state and evaluate students’ learning behavior in real time. According to the evaluation results, teachers adjust the teaching content and interfere with students’ learning behavior to improve the teaching quality. The empirical analysis shows that the evaluation model of curriculum teaching quality based on hidden Markov algorithm has certain effectiveness, and the analysis results have certain reference value, which is convenient for teachers to set the target in teaching.

关 键 词：数据分析 学习行为 教学质量 隐马尔科夫模型 

分 类 号：G642[文化科学—高等教育学]