Huanhuan Zhang, Lei Wang, Yuxian Qu, Wei Li, Qiaoyong Jiang
{"title":"用于个性化学生建模和学习能力分类的增强型动态键值记忆网络","authors":"Huanhuan Zhang, Lei Wang, Yuxian Qu, Wei Li, Qiaoyong Jiang","doi":"10.1007/s12559-024-10341-w","DOIUrl":null,"url":null,"abstract":"<p>Knowledge tracing (KT) is a technique that can be applied to predict students’ current skill mastery levels and future academic performance based on previous question-answering data. A good KT model can more accurately reflect a student’s cognitive processes and provide a more realistic assessment of skill mastery level. Currently, most KT models regard all students as a whole, while ignoring their personal differences; a few KT models attempt to personalize the modeling of students from the perspective of their learning abilities, among which a typical example is Deep Knowledge Tracing with Dynamic Student Classification (DKT-DSC). However, these models have a relatively coarse-grained approach to modeling students’ learning abilities and cannot accurately capture the nonlinear relationship between students’ learning abilities and the questions they answer. To solve these problems, we propose a novel KT model named the Enhanced Dynamic Key-Value Memory Networks for Dynamic Student Classification (EnDKVMN-DSC). This model is specifically designed for personalized student modeling and learning ability classification. Specifically, first, we propose a novel Enhanced Dynamic Key-Value Memory Network (EnDKVMN) and use it to model each student’s learning ability. Second, students are classified according to their learning abilities based on the <i>K</i>-means algorithm. Finally, the enriched input features are constructed and passed through Gated Recurrent Unit (GRU) networks to obtain prediction results. All experiments are conducted on four real-world datasets to evaluate our proposed model, and the results show that EnDKVMN-DSC outperforms the other four state-of-the-art KT models based on DKT or DKVMN in predicting student performance.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhanced Dynamic Key-Value Memory Networks for Personalized Student Modeling and Learning Ability Classification\",\"authors\":\"Huanhuan Zhang, Lei Wang, Yuxian Qu, Wei Li, Qiaoyong Jiang\",\"doi\":\"10.1007/s12559-024-10341-w\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Knowledge tracing (KT) is a technique that can be applied to predict students’ current skill mastery levels and future academic performance based on previous question-answering data. A good KT model can more accurately reflect a student’s cognitive processes and provide a more realistic assessment of skill mastery level. Currently, most KT models regard all students as a whole, while ignoring their personal differences; a few KT models attempt to personalize the modeling of students from the perspective of their learning abilities, among which a typical example is Deep Knowledge Tracing with Dynamic Student Classification (DKT-DSC). However, these models have a relatively coarse-grained approach to modeling students’ learning abilities and cannot accurately capture the nonlinear relationship between students’ learning abilities and the questions they answer. To solve these problems, we propose a novel KT model named the Enhanced Dynamic Key-Value Memory Networks for Dynamic Student Classification (EnDKVMN-DSC). This model is specifically designed for personalized student modeling and learning ability classification. Specifically, first, we propose a novel Enhanced Dynamic Key-Value Memory Network (EnDKVMN) and use it to model each student’s learning ability. Second, students are classified according to their learning abilities based on the <i>K</i>-means algorithm. Finally, the enriched input features are constructed and passed through Gated Recurrent Unit (GRU) networks to obtain prediction results. All experiments are conducted on four real-world datasets to evaluate our proposed model, and the results show that EnDKVMN-DSC outperforms the other four state-of-the-art KT models based on DKT or DKVMN in predicting student performance.</p>\",\"PeriodicalId\":51243,\"journal\":{\"name\":\"Cognitive Computation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-08-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cognitive Computation\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s12559-024-10341-w\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cognitive Computation","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s12559-024-10341-w","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Enhanced Dynamic Key-Value Memory Networks for Personalized Student Modeling and Learning Ability Classification
Knowledge tracing (KT) is a technique that can be applied to predict students’ current skill mastery levels and future academic performance based on previous question-answering data. A good KT model can more accurately reflect a student’s cognitive processes and provide a more realistic assessment of skill mastery level. Currently, most KT models regard all students as a whole, while ignoring their personal differences; a few KT models attempt to personalize the modeling of students from the perspective of their learning abilities, among which a typical example is Deep Knowledge Tracing with Dynamic Student Classification (DKT-DSC). However, these models have a relatively coarse-grained approach to modeling students’ learning abilities and cannot accurately capture the nonlinear relationship between students’ learning abilities and the questions they answer. To solve these problems, we propose a novel KT model named the Enhanced Dynamic Key-Value Memory Networks for Dynamic Student Classification (EnDKVMN-DSC). This model is specifically designed for personalized student modeling and learning ability classification. Specifically, first, we propose a novel Enhanced Dynamic Key-Value Memory Network (EnDKVMN) and use it to model each student’s learning ability. Second, students are classified according to their learning abilities based on the K-means algorithm. Finally, the enriched input features are constructed and passed through Gated Recurrent Unit (GRU) networks to obtain prediction results. All experiments are conducted on four real-world datasets to evaluate our proposed model, and the results show that EnDKVMN-DSC outperforms the other four state-of-the-art KT models based on DKT or DKVMN in predicting student performance.
期刊介绍:
Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.