Learning path planning methods based on learning path variability and ant colony optimization
With the advancement of education information, the scale of online education has been expanding, which brings challenges to students' learning path planning, i.e., course and learning method planning. To address the limitations of learning path planning such as insufficient personalization, the...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Systems and Soft Computing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772941924000206 |
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| _version_ | 1846115615048204288 |
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| author | Jing Zhao Haitao Mao Panpan Mao Junyong Hao |
| author_facet | Jing Zhao Haitao Mao Panpan Mao Junyong Hao |
| author_sort | Jing Zhao |
| collection | DOAJ |
| description | With the advancement of education information, the scale of online education has been expanding, which brings challenges to students' learning path planning, i.e., course and learning method planning. To address the limitations of learning path planning such as insufficient personalization, the study proposes a learning path planning method based on learning path variability and ant colony optimization. First, dynamic time regularization is used to obtain learning path variability, and the K-means algorithm is used to classify students' learning types. Subsequently, an ant colony optimization algorithm is used to generate learning paths. Finally, the effectiveness of the method is tested. The results show that the loss value of the ant colony optimization algorithm converges to a minimum value of 0.1, which has the best stability of the loss function curve and the fastest convergence speed compared to other algorithms. Under the same experimental environment, the accuracy of the algorithm is as high as 0.9, which is conducive to the search for the optimal solution. The path planning method designed by the research can effectively grasp the learning characteristics and habits of students, and the accurate classification degree can reach 96.6%. With this learning path planning method, the average video learning time of students reaches a maximum of 80 min, while the average completion rate of students' course objectives is stable at 90%, which is about 20% higher than that of the GA-based learning path planning method. The method can significantly improve academic performance and educational outcomes. The method thus grasps the type of student learning, stimulates students' interest in learning, improves the effect of online learning, helps to promote education informatization and provides a boost to the deep reform of education. |
| format | Article |
| id | doaj-art-a6ea2c8e1b214a6aab20e5e92d039a34 |
| institution | Kabale University |
| issn | 2772-9419 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Systems and Soft Computing |
| spelling | doaj-art-a6ea2c8e1b214a6aab20e5e92d039a342024-12-19T11:02:59ZengElsevierSystems and Soft Computing2772-94192024-12-016200091Learning path planning methods based on learning path variability and ant colony optimizationJing Zhao0Haitao Mao1Panpan Mao2Junyong Hao3College of Liberal Arts, Xinyang Normal University, Xinyang, 464000, Henan, PR China; Corresponding author.College of Asset Management Division, Xinyang Normal University, Xinyang, 464000, Henan, PR ChinaSchool of Mathematics and Statistics, Xinyang Normal University, Xinyang, 464000, Henan, PR ChinaSchool of Education, Central China Normal University, Wuhan, 430079, Hubei, PR ChinaWith the advancement of education information, the scale of online education has been expanding, which brings challenges to students' learning path planning, i.e., course and learning method planning. To address the limitations of learning path planning such as insufficient personalization, the study proposes a learning path planning method based on learning path variability and ant colony optimization. First, dynamic time regularization is used to obtain learning path variability, and the K-means algorithm is used to classify students' learning types. Subsequently, an ant colony optimization algorithm is used to generate learning paths. Finally, the effectiveness of the method is tested. The results show that the loss value of the ant colony optimization algorithm converges to a minimum value of 0.1, which has the best stability of the loss function curve and the fastest convergence speed compared to other algorithms. Under the same experimental environment, the accuracy of the algorithm is as high as 0.9, which is conducive to the search for the optimal solution. The path planning method designed by the research can effectively grasp the learning characteristics and habits of students, and the accurate classification degree can reach 96.6%. With this learning path planning method, the average video learning time of students reaches a maximum of 80 min, while the average completion rate of students' course objectives is stable at 90%, which is about 20% higher than that of the GA-based learning path planning method. The method can significantly improve academic performance and educational outcomes. The method thus grasps the type of student learning, stimulates students' interest in learning, improves the effect of online learning, helps to promote education informatization and provides a boost to the deep reform of education.http://www.sciencedirect.com/science/article/pii/S2772941924000206Dynamic time regularizationK-means algorithmAnt colony optimization algorithmLearning pathsOnline education |
| spellingShingle | Jing Zhao Haitao Mao Panpan Mao Junyong Hao Learning path planning methods based on learning path variability and ant colony optimization Systems and Soft Computing Dynamic time regularization K-means algorithm Ant colony optimization algorithm Learning paths Online education |
| title | Learning path planning methods based on learning path variability and ant colony optimization |
| title_full | Learning path planning methods based on learning path variability and ant colony optimization |
| title_fullStr | Learning path planning methods based on learning path variability and ant colony optimization |
| title_full_unstemmed | Learning path planning methods based on learning path variability and ant colony optimization |
| title_short | Learning path planning methods based on learning path variability and ant colony optimization |
| title_sort | learning path planning methods based on learning path variability and ant colony optimization |
| topic | Dynamic time regularization K-means algorithm Ant colony optimization algorithm Learning paths Online education |
| url | http://www.sciencedirect.com/science/article/pii/S2772941924000206 |
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