Online Personalized Learning Path Recommendation Based on Saltatory Evolution Ant Colony Optimization Algorithm
To solve the problems of slow convergence and low accuracy when the traditional ant colony optimization (ACO) algorithm is applied to online learning path recommendation problems, this study proposes an online personalized learning path recommendation model (OPLPRM) based on the saltatory evolution...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/13/2792 |
| _version_ | 1797591265089421312 |
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| author | Shugang Li Hui Chen Xin Liu Jiayi Li Kexin Peng Ziming Wang |
| author_facet | Shugang Li Hui Chen Xin Liu Jiayi Li Kexin Peng Ziming Wang |
| author_sort | Shugang Li |
| collection | DOAJ |
| description | To solve the problems of slow convergence and low accuracy when the traditional ant colony optimization (ACO) algorithm is applied to online learning path recommendation problems, this study proposes an online personalized learning path recommendation model (OPLPRM) based on the saltatory evolution ant colony optimization (SEACO) algorithm to achieve fast, accurate, real-time interactive and high-quality learning path recommendations. Consequently, an online personalized learning path optimization model with a time window was constructed first. This model not only considers the learning order of the recommended learning resources, but also further takes the review behavior pattern of learners into consideration, which improves the quality of the learning path recommendation. Then, this study constructed a SEACO algorithm suitable for online personalized learning path recommendation, from the perspective of optimal learning path prediction, which predicts path pheromone evolution by mining historical data, injecting the domain knowledge of learning path prediction that can achieve best learning effects extracted from domain experts and reducing invalid search, thus improving the speed and accuracy of learning path optimization. A simulation experiment was carried out on the proposed online personalized learning path recommendation model by using the real leaner learning behavior data set from the British “Open University” platform. The results illustrate that the performance of the proposed online personalized learning path recommendation model, based on the SEACO algorithm for improving the optimization speed and accuracy of the learning path, is better than traditional ACO algorithm, and it can quickly and accurately recommend the most suitable learning path according to the changing needs of learners in a limited time. |
| first_indexed | 2024-03-11T01:35:02Z |
| format | Article |
| id | doaj.art-3de3e61639c2419cba223ccf4bf09c7b |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T01:35:02Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-3de3e61639c2419cba223ccf4bf09c7b2023-11-18T17:01:26ZengMDPI AGMathematics2227-73902023-06-011113279210.3390/math11132792Online Personalized Learning Path Recommendation Based on Saltatory Evolution Ant Colony Optimization AlgorithmShugang Li0Hui Chen1Xin Liu2Jiayi Li3Kexin Peng4Ziming Wang5School of Management, Shanghai University, Shanghai 200444, ChinaSchool of Management, Shanghai University, Shanghai 200444, ChinaSchool of Management, Shanghai University, Shanghai 200444, ChinaSongjiang No. 2 Middle School, Shanghai 201600, ChinaSchool of Management, Shanghai University, Shanghai 200444, ChinaSchool of Management, Shanghai University, Shanghai 200444, ChinaTo solve the problems of slow convergence and low accuracy when the traditional ant colony optimization (ACO) algorithm is applied to online learning path recommendation problems, this study proposes an online personalized learning path recommendation model (OPLPRM) based on the saltatory evolution ant colony optimization (SEACO) algorithm to achieve fast, accurate, real-time interactive and high-quality learning path recommendations. Consequently, an online personalized learning path optimization model with a time window was constructed first. This model not only considers the learning order of the recommended learning resources, but also further takes the review behavior pattern of learners into consideration, which improves the quality of the learning path recommendation. Then, this study constructed a SEACO algorithm suitable for online personalized learning path recommendation, from the perspective of optimal learning path prediction, which predicts path pheromone evolution by mining historical data, injecting the domain knowledge of learning path prediction that can achieve best learning effects extracted from domain experts and reducing invalid search, thus improving the speed and accuracy of learning path optimization. A simulation experiment was carried out on the proposed online personalized learning path recommendation model by using the real leaner learning behavior data set from the British “Open University” platform. The results illustrate that the performance of the proposed online personalized learning path recommendation model, based on the SEACO algorithm for improving the optimization speed and accuracy of the learning path, is better than traditional ACO algorithm, and it can quickly and accurately recommend the most suitable learning path according to the changing needs of learners in a limited time.https://www.mdpi.com/2227-7390/11/13/2792saltatory evolution ant colony optimization algorithmpersonalized learninglearning path recommendationdomain knowledge mining |
| spellingShingle | Shugang Li Hui Chen Xin Liu Jiayi Li Kexin Peng Ziming Wang Online Personalized Learning Path Recommendation Based on Saltatory Evolution Ant Colony Optimization Algorithm Mathematics saltatory evolution ant colony optimization algorithm personalized learning learning path recommendation domain knowledge mining |
| title | Online Personalized Learning Path Recommendation Based on Saltatory Evolution Ant Colony Optimization Algorithm |
| title_full | Online Personalized Learning Path Recommendation Based on Saltatory Evolution Ant Colony Optimization Algorithm |
| title_fullStr | Online Personalized Learning Path Recommendation Based on Saltatory Evolution Ant Colony Optimization Algorithm |
| title_full_unstemmed | Online Personalized Learning Path Recommendation Based on Saltatory Evolution Ant Colony Optimization Algorithm |
| title_short | Online Personalized Learning Path Recommendation Based on Saltatory Evolution Ant Colony Optimization Algorithm |
| title_sort | online personalized learning path recommendation based on saltatory evolution ant colony optimization algorithm |
| topic | saltatory evolution ant colony optimization algorithm personalized learning learning path recommendation domain knowledge mining |
| url | https://www.mdpi.com/2227-7390/11/13/2792 |
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