Student knowledge tracking based multi-indicator exercise recommendation algorithm
Personalized exercise recommendation was an important topic in the era of education informatization, the forgetting laws of students in the learning process were ignored by the traditional problem recommendation algorithm, which failed to fully tap the students’ knowledge mastery level and the commo...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2022-09-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022234/ |
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| _version_ | 1841530715028586496 |
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| author | Bin ZHUGE Zhenghu YIN Wenxue SI Lei YAN Ligang DONG Xian JIANG |
| author_facet | Bin ZHUGE Zhenghu YIN Wenxue SI Lei YAN Ligang DONG Xian JIANG |
| author_sort | Bin ZHUGE |
| collection | DOAJ |
| description | Personalized exercise recommendation was an important topic in the era of education informatization, the forgetting laws of students in the learning process were ignored by the traditional problem recommendation algorithm, which failed to fully tap the students’ knowledge mastery level and the common characteristics of similar students, insufficient, could not reasonably promote students’ learning of new knowledge or help students find and fill omissions.In view of the above defects, a multi-index exercise recommendation method based on student knowledge tracking was proposed, which was divided into two modules: preliminary screening and re-filtering of exercises, focusing on the novelty, difficulty and diversity of exercise recommendation.Firstly, a knowledge probability prediction (SF-KCCP) model combined with students’ forgetting law was constructed to ensure the novelty of the recommended exercises.Then, students’ knowledge and concept mastery level was accurately excavated based on the dynamic key-value knowledge tracking (DKVMN) model to ensure that exercises of appropriate difficulty were recommended.Finally, the user-based collaborative filtering (UserCF) algorithm was integrated into the re-filtering module, and the similarity between student groups was used to achieve the diversity of recommendation results.The proposed method is demonstrated by extensive experiments to achieve better performance than some existing baseline models. |
| format | Article |
| id | doaj-art-fd4f8942e28f479eb0680d4854ee4f8c |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2022-09-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-fd4f8942e28f479eb0680d4854ee4f8c2025-01-15T03:00:10ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012022-09-013812914359577348Student knowledge tracking based multi-indicator exercise recommendation algorithmBin ZHUGEZhenghu YINWenxue SILei YANLigang DONGXian JIANGPersonalized exercise recommendation was an important topic in the era of education informatization, the forgetting laws of students in the learning process were ignored by the traditional problem recommendation algorithm, which failed to fully tap the students’ knowledge mastery level and the common characteristics of similar students, insufficient, could not reasonably promote students’ learning of new knowledge or help students find and fill omissions.In view of the above defects, a multi-index exercise recommendation method based on student knowledge tracking was proposed, which was divided into two modules: preliminary screening and re-filtering of exercises, focusing on the novelty, difficulty and diversity of exercise recommendation.Firstly, a knowledge probability prediction (SF-KCCP) model combined with students’ forgetting law was constructed to ensure the novelty of the recommended exercises.Then, students’ knowledge and concept mastery level was accurately excavated based on the dynamic key-value knowledge tracking (DKVMN) model to ensure that exercises of appropriate difficulty were recommended.Finally, the user-based collaborative filtering (UserCF) algorithm was integrated into the re-filtering module, and the similarity between student groups was used to achieve the diversity of recommendation results.The proposed method is demonstrated by extensive experiments to achieve better performance than some existing baseline models.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022234/deep learningexercise recommendationknowledge trackingcollaborative filtering |
| spellingShingle | Bin ZHUGE Zhenghu YIN Wenxue SI Lei YAN Ligang DONG Xian JIANG Student knowledge tracking based multi-indicator exercise recommendation algorithm Dianxin kexue deep learning exercise recommendation knowledge tracking collaborative filtering |
| title | Student knowledge tracking based multi-indicator exercise recommendation algorithm |
| title_full | Student knowledge tracking based multi-indicator exercise recommendation algorithm |
| title_fullStr | Student knowledge tracking based multi-indicator exercise recommendation algorithm |
| title_full_unstemmed | Student knowledge tracking based multi-indicator exercise recommendation algorithm |
| title_short | Student knowledge tracking based multi-indicator exercise recommendation algorithm |
| title_sort | student knowledge tracking based multi indicator exercise recommendation algorithm |
| topic | deep learning exercise recommendation knowledge tracking collaborative filtering |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022234/ |
| work_keys_str_mv | AT binzhuge studentknowledgetrackingbasedmultiindicatorexerciserecommendationalgorithm AT zhenghuyin studentknowledgetrackingbasedmultiindicatorexerciserecommendationalgorithm AT wenxuesi studentknowledgetrackingbasedmultiindicatorexerciserecommendationalgorithm AT leiyan studentknowledgetrackingbasedmultiindicatorexerciserecommendationalgorithm AT ligangdong studentknowledgetrackingbasedmultiindicatorexerciserecommendationalgorithm AT xianjiang studentknowledgetrackingbasedmultiindicatorexerciserecommendationalgorithm |