Construction of GCNN-based intelligent recommendation model for answering teachers in online learning system
In response to the limitations of the existing online learning system regarding the efficiency and accuracy of the question-and-answer (Q&A) teacher recommendation method, this research develops a Q&A teacher recommendation model utilizing a Graph Convolutional Neural Network. First, a time-...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2024-01-01
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| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2023-0229 |
| _version_ | 1797279178748329984 |
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| author | Lu Wenyi Wei Ting Guo Zijun Ren Jianhong |
| author_facet | Lu Wenyi Wei Ting Guo Zijun Ren Jianhong |
| author_sort | Lu Wenyi |
| collection | DOAJ |
| description | In response to the limitations of the existing online learning system regarding the efficiency and accuracy of the question-and-answer (Q&A) teacher recommendation method, this research develops a Q&A teacher recommendation model utilizing a Graph Convolutional Neural Network. First, a time-sensitive online learning Q&A teacher recommendation model (A Time Sensitive Online Learning Q&A Teacher Recommendation Model; TSRM) is proposed to address the shortcomings that current recommendation methods ignore, i.e., the teacher’s ability to answer questions with time changes. Then, a TSRM based on Short and Long Term Interest for Answering Questions (LSTR) is proposed to address the problem that the current recommendation methods ignore, i.e., the types of questions of student users’ concerns can change. Finally, we combine the TSRM model and LSTR model to build an intelligent recommendation model for answering teachers. The conclusion is that the accuracy rate of TSRM model on the test set is 99.5%, and the recommendation success rate of LSTR model reaches 98.4%, which are better than the other two models. The above results can show that the LSTR model and TSRM model constructed by the study have high performance and can effectively perform the recommendation of answering teachers in the online learning system, thus improving the efficiency of solving students’ problem, improving students’ learning effect, and contributing to the development of university education informatization. |
| first_indexed | 2024-03-07T16:20:36Z |
| format | Article |
| id | doaj.art-3d6e8a012f4b45e3aac8eb0f8d7b2ccf |
| institution | Directory Open Access Journal |
| issn | 2191-026X |
| language | English |
| last_indexed | 2024-03-07T16:20:36Z |
| publishDate | 2024-01-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj.art-3d6e8a012f4b45e3aac8eb0f8d7b2ccf2024-03-04T07:29:06ZengDe GruyterJournal of Intelligent Systems2191-026X2024-01-0133113678510.1515/jisys-2023-0229Construction of GCNN-based intelligent recommendation model for answering teachers in online learning systemLu Wenyi0Wei Ting1Guo Zijun2Ren Jianhong3School of Information Engineering, Jiangxi Vocational and Technical College of Communications, Nanchang, 330013, ChinaDepartment of Student Affairs, Jiangxi Vocational and Technical College of Communications, Nanchang, 330013, ChinaSchool of Information Engineering, Jiangxi Vocational and Technical College of Communications, Nanchang, 330013, ChinaEducational Media Construction Division, Jiangxi Education Evaluation and Assessment Institute, Nanchang, 330038, ChinaIn response to the limitations of the existing online learning system regarding the efficiency and accuracy of the question-and-answer (Q&A) teacher recommendation method, this research develops a Q&A teacher recommendation model utilizing a Graph Convolutional Neural Network. First, a time-sensitive online learning Q&A teacher recommendation model (A Time Sensitive Online Learning Q&A Teacher Recommendation Model; TSRM) is proposed to address the shortcomings that current recommendation methods ignore, i.e., the teacher’s ability to answer questions with time changes. Then, a TSRM based on Short and Long Term Interest for Answering Questions (LSTR) is proposed to address the problem that the current recommendation methods ignore, i.e., the types of questions of student users’ concerns can change. Finally, we combine the TSRM model and LSTR model to build an intelligent recommendation model for answering teachers. The conclusion is that the accuracy rate of TSRM model on the test set is 99.5%, and the recommendation success rate of LSTR model reaches 98.4%, which are better than the other two models. The above results can show that the LSTR model and TSRM model constructed by the study have high performance and can effectively perform the recommendation of answering teachers in the online learning system, thus improving the efficiency of solving students’ problem, improving students’ learning effect, and contributing to the development of university education informatization.https://doi.org/10.1515/jisys-2023-0229graph convolutional neural networkonline learningintelligent recommendation |
| spellingShingle | Lu Wenyi Wei Ting Guo Zijun Ren Jianhong Construction of GCNN-based intelligent recommendation model for answering teachers in online learning system Journal of Intelligent Systems graph convolutional neural network online learning intelligent recommendation |
| title | Construction of GCNN-based intelligent recommendation model for answering teachers in online learning system |
| title_full | Construction of GCNN-based intelligent recommendation model for answering teachers in online learning system |
| title_fullStr | Construction of GCNN-based intelligent recommendation model for answering teachers in online learning system |
| title_full_unstemmed | Construction of GCNN-based intelligent recommendation model for answering teachers in online learning system |
| title_short | Construction of GCNN-based intelligent recommendation model for answering teachers in online learning system |
| title_sort | construction of gcnn based intelligent recommendation model for answering teachers in online learning system |
| topic | graph convolutional neural network online learning intelligent recommendation |
| url | https://doi.org/10.1515/jisys-2023-0229 |
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