[1]祁天龙,任美睿,赵建宇,等.基于聚类的协作学习分组方法[J].计算机技术与发展,2023,33(06):189-193.[doi:10. 3969 / j. issn. 1673-629X. 2023. 06. 028]
　QI Tian-long,REN Mei-rui,ZHAO Jian-yu,et al.Collaborative Learning Grouping Method Based on Clustering[J].,2023,33(06):189-193.[doi:10. 3969 / j. issn. 1673-629X. 2023. 06. 028]

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基于聚类的协作学习分组方法()

分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:

33

期数:

2023年06期

页码:

189-193

栏目:

新型计算应用系统

出版日期:

2023-06-10

文章信息/Info

Title:

Collaborative Learning Grouping Method Based on Clustering

文章编号:

1673-629X(2023)06--0189-05

作者:

祁天龙¹ ; 任美睿¹; 2 ; 赵建宇¹ ; 郭龙江¹; 2

1. 陕西师范大学 计算机科学学院,陕西 西安 710062;

2. 陕西师范大学 现代教学技术教育部重点实验室,陕西 西安 710062

Author(s):

QI Tian-long1 ; REN Mei-rui1; 2 ; ZHAO Jian-yu1 ; GUO Long-jiang1; 2

1. School of Computer Science,Shaanxi Normal University,Xi’ an 710062,China;

2. Key Laboratory of Modern Teaching Technology of Ministry of Education,Shaanxi Normal University,Xi’ an 710062,China

关键词:

协作学习; 在线学习; 分组方法; 聚类; 满意度; 时间重合度

Keywords:

collaborative learning; online learning; grouping method; clustering; satisfaction; time coincidence

分类号:

TP391. 7

DOI:

10. 3969 / j. issn. 1673-629X. 2023. 06. 028

摘要:

协作学习能够促进在线学习平台中学习者之间的沟通交流。 同一小组内学习者共同协作完成既定任务过程中,不仅可以巩固已有知识,也能通过互相学习,获得新知识和新技能,在提高个人表现的同时,增加学习兴趣,从而有效地降低辍学率。 近年来已提出了很多协作学习分组方法。 然而,现有分组方法没有兼顾主题意愿、学习时间规律和小组规模这三个对小组沟通效率有重要影响的因素。 该文依据学习者的主题意愿预分组,然后依据学习时间规律迭代地调用聚类算法将学习者划分到满足上下限的小组中,结果表明,上述方法形成的协作学习小组在满意度和时间重合度上均优于IFST 和随机分组方法。 最后,以 XuetangX 平台上的 1 754 名学习者为实验对象进行协作学习分组,实验结果表明,形成的小组有充分的协作学习时间,指派的主题能够很好地满足学习者的意愿,且各个小组之间成员数均衡。

Abstract:

Collaborative learning can promote communication between learners on the online learning platform. When learners in the samegroup collaborate to complete the established task,they?

can not only consolidate the existing knowledge,but also acquire new knowledgeand new skills through mutual learning. While improving individual performance, they can increase their interest in learning, thuseffectively reducing the dropout rate. In recent years,many collaborative learning grouping methods have been proposed. However,theexisting grouping methods do not take into account the three factors which have important influence on the efficiency of group communication:topic willingness,learning time rule and group size. According to the learners‘ topic willingness,they are pre-grouped,and thenthe clustering algorithm is iteratively called according to the learning time rule to divide the learners into groups that meet the upper andlower limits. It is showed that the formed collaborative learning groups are better than IFST and random grouping methods in terms ofsatisfaction and time coincidence. Finally,1 754 learners on the XuetangX platform are taken as the experimental objects for collaborativelearning grouping. The experimental results show that the formed groups have sufficient collaborative learning time,the assigned topicscan well meet the wishes of learners,and the size of each group is balanced.

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