基于认知状态的互补均衡聚类分组模型研究
A Balanced Clustering Grouping Model Based on Cognitive States
王彬彬 1李强1
作者信息
- 1. 淮南师范学院(安徽 淮南 232038)
- 折叠
摘要
分组是协作学习的关键步骤,针对学生分组中的学生特征不易量化、小组大小和学习能力不均衡等问题,提出一种基于认知状态的互补均衡聚类分组模型.首先使用认知诊断模型获取学生的认知状态,对认知状态进行量化和特征表示;然后对分组目标进行函数定义,使用改进后的均衡聚类算法将学生进行聚簇;最后依据分组目标,根据学生的知识特征获得最终的分组方案.实验中对比了现有的3种分组方法,分别在9个数据集上进行对比实验,验证了基于认知状态的均衡聚类模型在组内知识互补率和组间知识均衡率两项指标上的有效性.
Abstract
Grouping is a key step in collaborative learning.A balanced clustering model based on cogni-tive diagnosis is proposed to address the problems of quantization of student characteristics and dispro-portion in group size and learning ability in student grouping.Firstly,the cognitive diagnostic model is used to obtain the cognitive states of students,and the cognitive states are quantified;then the grouping goals are defined as functions,and students are clustered using an improved balanced clustering algo-rithm;finally,the final grouping scheme is obtained based on the grouping goals and the students'knowl-edge characteristics.Three existing grouping methods were compared and contrasted within nine datasets.The experiments verified the effectiveness of the balanced clustering based on cognitive states of two indi-cators:the rate of intra-group knowledge complementarity and the rate of knowledge balance between groups.
关键词
协作学习/分组策略/知识互补/聚类分析Key words
collaborative learning/grouping strategy/complementary knowledge/clustering引用本文复制引用
基金项目
安徽省高校自然科学研究重点项目(2022AH051576)
淮南师范学院科研项目(2021XJYB032)
淮南师范学院科研项目(2021XJYB033)
出版年
2024
NETL
NSTL
维普
万方数据