面向Python的自适应学习系统知识空间模型构建研究

Research on Knowledge Space Model Construction of Adaptive Learning System for Python

首先,采用基于知识空间模型的构建方法对领域知识进行结构化描述,定义不同知识单元的顺序性和层次性的关系。其次,利用项目反应理论分析学生能力。最后,根据布鲁姆教育目标分类学设定能力维度,有助于综合评价学生知识和能力。文章针对Python程序设计课程进行领域知识的结构化描述,学习者可以利用推荐的课程学习资源学习和测试,并根据学生的测试情况得到相应的能力分析结果。

Firstly,this paper describes domain knowledge structurally based on the construction method of knowledge space model,and defines the relationship between the order and hierarchy of different knowledge units.Secondly,the ability of students is analyzed by item response theory.Finally,the ability dimension is set according to Bloom’s taxonomy of educational goals,which is helpful to realize the comprehensive evaluation of students’knowledge and ability.This paper provides a structured description of the domain knowledge for Python Programming Course.Learners can use the recommended course learning resources for learning and testing,and get the ability analysis results according to the test situation of students.