A multi-feature fusion exercise recommendation model based on knowledge tracing machines
The subject of personalized exercise recommendation holds significant relevance within the domain of personalized services in smart education. Nevertheless, traditional algorithms have often lacked a deep understanding of student characteristics and failed to adequately explore the relationship betw...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2024-09-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024210/ |
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| _version_ | 1841528865824964608 |
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| author | ZHUGE Bin WANG Ying XIAO Mengfan YAN Lei WANG Bingyan DONG Ligang JIANG Xian |
| author_facet | ZHUGE Bin WANG Ying XIAO Mengfan YAN Lei WANG Bingyan DONG Ligang JIANG Xian |
| author_sort | ZHUGE Bin |
| collection | DOAJ |
| description | The subject of personalized exercise recommendation holds significant relevance within the domain of personalized services in smart education. Nevertheless, traditional algorithms have often lacked a deep understanding of student characteristics and failed to adequately explore the relationship between knowledge mastery and question-answering behaviors, leading to low recommendation accuracy. To address these issues, combining the knowledge tracing machine and the user-based collaborative filtering algorithm, as a KTM-based multi-feature fusion exercise recommendation model, SKT-MFER was proposed. Firstly, as a knowledge tracking model, KTM-LC, incorporating student learning behaviors and learning abilities, was constructed to accurately assess the student’s knowledge mastery level. Subsequently, two filters were implemented to ensure the exercise recommendation’s accuracy: the first was an initial screening utilizing the knowledge point mastery matrix to eliminate students who were similar to the target student, and the second was a filtering process considering the combined similarity of cognitive state similarity and exercise difficulty similarity. Through extensive experiments, it proves that the proposed method yields better results than some existing baseline models. |
| format | Article |
| id | doaj-art-8225688bc1c44d5dba5643ccd58643e0 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2024-09-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-8225688bc1c44d5dba5643ccd58643e02025-01-15T03:34:00ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012024-09-0140758773366208A multi-feature fusion exercise recommendation model based on knowledge tracing machinesZHUGE BinWANG YingXIAO MengfanYAN LeiWANG BingyanDONG LigangJIANG XianThe subject of personalized exercise recommendation holds significant relevance within the domain of personalized services in smart education. Nevertheless, traditional algorithms have often lacked a deep understanding of student characteristics and failed to adequately explore the relationship between knowledge mastery and question-answering behaviors, leading to low recommendation accuracy. To address these issues, combining the knowledge tracing machine and the user-based collaborative filtering algorithm, as a KTM-based multi-feature fusion exercise recommendation model, SKT-MFER was proposed. Firstly, as a knowledge tracking model, KTM-LC, incorporating student learning behaviors and learning abilities, was constructed to accurately assess the student’s knowledge mastery level. Subsequently, two filters were implemented to ensure the exercise recommendation’s accuracy: the first was an initial screening utilizing the knowledge point mastery matrix to eliminate students who were similar to the target student, and the second was a filtering process considering the combined similarity of cognitive state similarity and exercise difficulty similarity. Through extensive experiments, it proves that the proposed method yields better results than some existing baseline models.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024210/smart educationexercise recommendationknowledge trackingcollaborative filteringfactorization machine |
| spellingShingle | ZHUGE Bin WANG Ying XIAO Mengfan YAN Lei WANG Bingyan DONG Ligang JIANG Xian A multi-feature fusion exercise recommendation model based on knowledge tracing machines Dianxin kexue smart education exercise recommendation knowledge tracking collaborative filtering factorization machine |
| title | A multi-feature fusion exercise recommendation model based on knowledge tracing machines |
| title_full | A multi-feature fusion exercise recommendation model based on knowledge tracing machines |
| title_fullStr | A multi-feature fusion exercise recommendation model based on knowledge tracing machines |
| title_full_unstemmed | A multi-feature fusion exercise recommendation model based on knowledge tracing machines |
| title_short | A multi-feature fusion exercise recommendation model based on knowledge tracing machines |
| title_sort | multi feature fusion exercise recommendation model based on knowledge tracing machines |
| topic | smart education exercise recommendation knowledge tracking collaborative filtering factorization machine |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2024210/ |
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