Collaborative Learning Groupings Incorporating Deep Knowledge Tracing Optimization Strategies
Effective grouping in collaborative learning is crucial for enhancing the efficiency of collaborative learning. A well-structured collaborative learning group can significantly enhance the learning effectiveness of both individuals and group members. However, the current approaches to collaborative...
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| Format: | Article |
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/5/2692 |
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| author | Haojun Li Yaohan Chen Weixia Liao Xuhui Wang |
| author_facet | Haojun Li Yaohan Chen Weixia Liao Xuhui Wang |
| author_sort | Haojun Li |
| collection | DOAJ |
| description | Effective grouping in collaborative learning is crucial for enhancing the efficiency of collaborative learning. A well-structured collaborative learning group can significantly enhance the learning effectiveness of both individuals and group members. However, the current approaches to collaborative learning grouping often lack a thorough examination of students’ knowledge-level characteristics, thereby failing to ensure that the knowledge structures of group members complement each other. Therefore, a collaborative learning grouping method incorporating the optimization strategy of deep knowledge tracking is proposed. Firstly, the optimized deep knowledge tracking (DKVMN-EKC) model is used to model the knowledge state of learners to obtain the degree of knowledge mastery of learners, and then the <i>K</i>-means method is used to similarly cluster all learners, and finally, the learners of different clusters are assigned to suitable learning groups according to the principle of heterogeneity of grouping. Extensive experiments have demonstrated that DKVMN-EKC can precisely model students’ knowledge mastery levels and that the proposed approach facilitates effective grouping at the level of students’ knowledge structures, thereby ensuring fairer and more heterogeneous grouping results. This approach fosters positive interactions among students, enabling them to learn from one another and effectively improve their understanding of various knowledge points. |
| format | Article |
| id | doaj-art-26d182ad01ed4aeba43d81228a96bb5c |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-26d182ad01ed4aeba43d81228a96bb5c2025-08-20T02:04:35ZengMDPI AGApplied Sciences2076-34172025-03-01155269210.3390/app15052692Collaborative Learning Groupings Incorporating Deep Knowledge Tracing Optimization StrategiesHaojun Li0Yaohan Chen1Weixia Liao2Xuhui Wang3School of Education, Zhejiang University of Technology, 288 Liuhe Road, Xihu District, Hangzhou 310023, ChinaSchool of Education, Zhejiang University of Technology, 288 Liuhe Road, Xihu District, Hangzhou 310023, ChinaSchool of Education, Zhejiang University of Technology, 288 Liuhe Road, Xihu District, Hangzhou 310023, ChinaSchool of Education, Zhejiang University of Technology, 288 Liuhe Road, Xihu District, Hangzhou 310023, ChinaEffective grouping in collaborative learning is crucial for enhancing the efficiency of collaborative learning. A well-structured collaborative learning group can significantly enhance the learning effectiveness of both individuals and group members. However, the current approaches to collaborative learning grouping often lack a thorough examination of students’ knowledge-level characteristics, thereby failing to ensure that the knowledge structures of group members complement each other. Therefore, a collaborative learning grouping method incorporating the optimization strategy of deep knowledge tracking is proposed. Firstly, the optimized deep knowledge tracking (DKVMN-EKC) model is used to model the knowledge state of learners to obtain the degree of knowledge mastery of learners, and then the <i>K</i>-means method is used to similarly cluster all learners, and finally, the learners of different clusters are assigned to suitable learning groups according to the principle of heterogeneity of grouping. Extensive experiments have demonstrated that DKVMN-EKC can precisely model students’ knowledge mastery levels and that the proposed approach facilitates effective grouping at the level of students’ knowledge structures, thereby ensuring fairer and more heterogeneous grouping results. This approach fosters positive interactions among students, enabling them to learn from one another and effectively improve their understanding of various knowledge points.https://www.mdpi.com/2076-3417/15/5/2692collaborative learningknowledge mastery state diagnosisknowledge tracingQ matrix |
| spellingShingle | Haojun Li Yaohan Chen Weixia Liao Xuhui Wang Collaborative Learning Groupings Incorporating Deep Knowledge Tracing Optimization Strategies Applied Sciences collaborative learning knowledge mastery state diagnosis knowledge tracing Q matrix |
| title | Collaborative Learning Groupings Incorporating Deep Knowledge Tracing Optimization Strategies |
| title_full | Collaborative Learning Groupings Incorporating Deep Knowledge Tracing Optimization Strategies |
| title_fullStr | Collaborative Learning Groupings Incorporating Deep Knowledge Tracing Optimization Strategies |
| title_full_unstemmed | Collaborative Learning Groupings Incorporating Deep Knowledge Tracing Optimization Strategies |
| title_short | Collaborative Learning Groupings Incorporating Deep Knowledge Tracing Optimization Strategies |
| title_sort | collaborative learning groupings incorporating deep knowledge tracing optimization strategies |
| topic | collaborative learning knowledge mastery state diagnosis knowledge tracing Q matrix |
| url | https://www.mdpi.com/2076-3417/15/5/2692 |
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