Adaptive Algorithm Recommendation and Application of Learning Resources in English Fragmented Reading
This paper firstly designs a five-dimensional model of learners’ characteristics (learners’ English reading ability, cognitive style, learning goal, learning situation, and learning effect) and a three-dimensional model of English reading resources’ characteristics (question types, topics, and diffi...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5592534 |
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| _version_ | 1849404204650594304 |
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| author | Jinyu Cheng Hong Wang |
| author_facet | Jinyu Cheng Hong Wang |
| author_sort | Jinyu Cheng |
| collection | DOAJ |
| description | This paper firstly designs a five-dimensional model of learners’ characteristics (learners’ English reading ability, cognitive style, learning goal, learning situation, and learning effect) and a three-dimensional model of English reading resources’ characteristics (question types, topics, and difficulty of resources) in a fragmented learning environment through literature research. At the same time, to make the learning resources meet the characteristics of fragmented learning time and space, the English Level 4 reading resources are reasonably designed and segmented to adapt to the needs of learners’ mobile fragmented learning. Then, combined with machine learning algorithms, an adaptive recommendation model of learning resources in English fragmented reading is constructed. The algorithm-based adaptive recommendation algorithm for English fragmented reading resources is designed. Based on the generated decision trees, the expression rules are parsed to achieve adaptive pushing of resources. The results of this study show that adaptive recommendation of learning resources in English fragmented reading can help teachers to develop future resource recommendation strategies through effective data collection to adaptively push resources that are close to learners’ individual needs. The use of mobile by English learners to learn to read in a fragmented learning context enables targeted training in weak areas of English reading, thus enhancing different aspects of learners’ reading skills. |
| format | Article |
| id | doaj-art-913ab8c81fdf43a7bbb6d3abf6f8bc0c |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-913ab8c81fdf43a7bbb6d3abf6f8bc0c2025-08-20T03:37:05ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55925345592534Adaptive Algorithm Recommendation and Application of Learning Resources in English Fragmented ReadingJinyu Cheng0Hong Wang1International Education College, Wuhan Sports University, Wuhan 430079, ChinaWushu College, Wuhan Sports University, Wuhan 430079, ChinaThis paper firstly designs a five-dimensional model of learners’ characteristics (learners’ English reading ability, cognitive style, learning goal, learning situation, and learning effect) and a three-dimensional model of English reading resources’ characteristics (question types, topics, and difficulty of resources) in a fragmented learning environment through literature research. At the same time, to make the learning resources meet the characteristics of fragmented learning time and space, the English Level 4 reading resources are reasonably designed and segmented to adapt to the needs of learners’ mobile fragmented learning. Then, combined with machine learning algorithms, an adaptive recommendation model of learning resources in English fragmented reading is constructed. The algorithm-based adaptive recommendation algorithm for English fragmented reading resources is designed. Based on the generated decision trees, the expression rules are parsed to achieve adaptive pushing of resources. The results of this study show that adaptive recommendation of learning resources in English fragmented reading can help teachers to develop future resource recommendation strategies through effective data collection to adaptively push resources that are close to learners’ individual needs. The use of mobile by English learners to learn to read in a fragmented learning context enables targeted training in weak areas of English reading, thus enhancing different aspects of learners’ reading skills.http://dx.doi.org/10.1155/2021/5592534 |
| spellingShingle | Jinyu Cheng Hong Wang Adaptive Algorithm Recommendation and Application of Learning Resources in English Fragmented Reading Complexity |
| title | Adaptive Algorithm Recommendation and Application of Learning Resources in English Fragmented Reading |
| title_full | Adaptive Algorithm Recommendation and Application of Learning Resources in English Fragmented Reading |
| title_fullStr | Adaptive Algorithm Recommendation and Application of Learning Resources in English Fragmented Reading |
| title_full_unstemmed | Adaptive Algorithm Recommendation and Application of Learning Resources in English Fragmented Reading |
| title_short | Adaptive Algorithm Recommendation and Application of Learning Resources in English Fragmented Reading |
| title_sort | adaptive algorithm recommendation and application of learning resources in english fragmented reading |
| url | http://dx.doi.org/10.1155/2021/5592534 |
| work_keys_str_mv | AT jinyucheng adaptivealgorithmrecommendationandapplicationoflearningresourcesinenglishfragmentedreading AT hongwang adaptivealgorithmrecommendationandapplicationoflearningresourcesinenglishfragmentedreading |