Diversion inference model of learning effectiveness supported by differential evolution strategy
AI-enabled Interactive learning environment has become an important platform. It needs to reduce or improve the ineffective and invalid learning behavior, and build a timely and effective diversion inference model of learning effectiveness to provide early warning. Through the early warning, Timely...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | Computers and Education: Artificial Intelligence |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666920X22000261 |
| _version_ | 1811207533137756160 |
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| author | Xiaona Xia |
| author_facet | Xiaona Xia |
| author_sort | Xiaona Xia |
| collection | DOAJ |
| description | AI-enabled Interactive learning environment has become an important platform. It needs to reduce or improve the ineffective and invalid learning behavior, and build a timely and effective diversion inference model of learning effectiveness to provide early warning. Through the early warning, Timely identify risk learners, make effective early warning responses and make targeted interventions. Early warning is an automatic measure based on learning behavior, it evaluates the risk trend, calculates the possible probability of learners' failure in assessment and leaving early. This study proposes and designs a diversion inference model of learning effectiveness. Firstly, we analyze and design one differential evolution strategy. Secondly, the convolutional neural network is improved and optimized based on the differential evolution strategy. Thirdly, the corresponding algorithms are designed and applied. Finally, based on the visualization of the experimental results, we put forward the intervention measures. The technical means might serve the precise teaching intervention and learning recommendation, and has strong theoretical value and practical significance for AI-enabled interactive learning processes. |
| first_indexed | 2024-04-12T04:06:09Z |
| format | Article |
| id | doaj.art-298ee0220adf418c85697cf1cb816682 |
| institution | Directory Open Access Journal |
| issn | 2666-920X |
| language | English |
| last_indexed | 2024-04-12T04:06:09Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computers and Education: Artificial Intelligence |
| spelling | doaj.art-298ee0220adf418c85697cf1cb8166822022-12-22T03:48:37ZengElsevierComputers and Education: Artificial Intelligence2666-920X2022-01-013100071Diversion inference model of learning effectiveness supported by differential evolution strategyXiaona Xia0Faculty of Education, Qufu Normal University, Qufu, Shandong, 273165, China; Chinese Academy of Education Big Data, Qufu Normal University, Qufu, Shandong, 273165, China; School of Computer Science, Qufu Normal University, Rizhao, Shandong, 276826, China; Faculty of Education, Qufu Normal University, Qufu, Shandong, 273165, China.AI-enabled Interactive learning environment has become an important platform. It needs to reduce or improve the ineffective and invalid learning behavior, and build a timely and effective diversion inference model of learning effectiveness to provide early warning. Through the early warning, Timely identify risk learners, make effective early warning responses and make targeted interventions. Early warning is an automatic measure based on learning behavior, it evaluates the risk trend, calculates the possible probability of learners' failure in assessment and leaving early. This study proposes and designs a diversion inference model of learning effectiveness. Firstly, we analyze and design one differential evolution strategy. Secondly, the convolutional neural network is improved and optimized based on the differential evolution strategy. Thirdly, the corresponding algorithms are designed and applied. Finally, based on the visualization of the experimental results, we put forward the intervention measures. The technical means might serve the precise teaching intervention and learning recommendation, and has strong theoretical value and practical significance for AI-enabled interactive learning processes.http://www.sciencedirect.com/science/article/pii/S2666920X22000261Interactive learning environmentDifferential evolution strategyConvolution neural networkInference model of learning effectiveness diversionEducation big dataLearning analytics |
| spellingShingle | Xiaona Xia Diversion inference model of learning effectiveness supported by differential evolution strategy Computers and Education: Artificial Intelligence Interactive learning environment Differential evolution strategy Convolution neural network Inference model of learning effectiveness diversion Education big data Learning analytics |
| title | Diversion inference model of learning effectiveness supported by differential evolution strategy |
| title_full | Diversion inference model of learning effectiveness supported by differential evolution strategy |
| title_fullStr | Diversion inference model of learning effectiveness supported by differential evolution strategy |
| title_full_unstemmed | Diversion inference model of learning effectiveness supported by differential evolution strategy |
| title_short | Diversion inference model of learning effectiveness supported by differential evolution strategy |
| title_sort | diversion inference model of learning effectiveness supported by differential evolution strategy |
| topic | Interactive learning environment Differential evolution strategy Convolution neural network Inference model of learning effectiveness diversion Education big data Learning analytics |
| url | http://www.sciencedirect.com/science/article/pii/S2666920X22000261 |
| work_keys_str_mv | AT xiaonaxia diversioninferencemodeloflearningeffectivenesssupportedbydifferentialevolutionstrategy |