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Data analysis and personalized recommendation of western music history information using deep learning under Internet of Things

To improve the teaching effect of western music history, the curriculum reform of history education needs to be promoted under the background of the Internet of Things (IoT). At first, a discussion is made on the characteristics of history course, which is combined with the characteristics of teachi...

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Autor principal: Yang, Zongye
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791487/
https://www.ncbi.nlm.nih.gov/pubmed/35081140
http://dx.doi.org/10.1371/journal.pone.0262697
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author Yang, Zongye
author_facet Yang, Zongye
author_sort Yang, Zongye
collection PubMed
description To improve the teaching effect of western music history, the curriculum reform of history education needs to be promoted under the background of the Internet of Things (IoT). At first, a discussion is made on the characteristics of history course, which is combined with the characteristics of teaching data easy to collect under the background of IoT. An analysis is conducted on the related theory of educational data mining. Then, the concept of personalized recommendation is proposed based on deep learning (DL) algorithm. Finally, online and offline experiments are designed to verify the performance of the algorithm from review and investigation, smoothness, and participation of difficulty. The research results show that in terms of offline recommendation accuracy, the average record length in Math data set is 24.5, which is much smaller than that in range data set. The research has obvious innovation significance compared with other studies. In the process of target review and investigation, it is found that the research method here involves a wider range of knowledge and higher reliability. In terms of the difficulty of recommending questions, the Deep Reinforcement Exercise (DRE) recommendation algorithm can adaptively adjust the difficulty of recommending questions. It also allows students to set different learning goals through participation goals. But in the experiments on Math data set, Step 10’s recommendation results are not very good, and the difficulty level varies greatly. If the goal setting is high, the problem recommended to students is too difficult, students may answer these questions wrongly, forcing the algorithm to adjust the difficulty adaptively. According to the above results, DRE recommendation algorithm can adapt to different learning needs and customize the recommendation results, thus opening up a new path for the teaching of western music history. Besides, the combination of DL algorithm and western music history teaching design can recommend learning materials, which is of great significance in the teaching of history courses.
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spelling pubmed-87914872022-01-27 Data analysis and personalized recommendation of western music history information using deep learning under Internet of Things Yang, Zongye PLoS One Research Article To improve the teaching effect of western music history, the curriculum reform of history education needs to be promoted under the background of the Internet of Things (IoT). At first, a discussion is made on the characteristics of history course, which is combined with the characteristics of teaching data easy to collect under the background of IoT. An analysis is conducted on the related theory of educational data mining. Then, the concept of personalized recommendation is proposed based on deep learning (DL) algorithm. Finally, online and offline experiments are designed to verify the performance of the algorithm from review and investigation, smoothness, and participation of difficulty. The research results show that in terms of offline recommendation accuracy, the average record length in Math data set is 24.5, which is much smaller than that in range data set. The research has obvious innovation significance compared with other studies. In the process of target review and investigation, it is found that the research method here involves a wider range of knowledge and higher reliability. In terms of the difficulty of recommending questions, the Deep Reinforcement Exercise (DRE) recommendation algorithm can adaptively adjust the difficulty of recommending questions. It also allows students to set different learning goals through participation goals. But in the experiments on Math data set, Step 10’s recommendation results are not very good, and the difficulty level varies greatly. If the goal setting is high, the problem recommended to students is too difficult, students may answer these questions wrongly, forcing the algorithm to adjust the difficulty adaptively. According to the above results, DRE recommendation algorithm can adapt to different learning needs and customize the recommendation results, thus opening up a new path for the teaching of western music history. Besides, the combination of DL algorithm and western music history teaching design can recommend learning materials, which is of great significance in the teaching of history courses. Public Library of Science 2022-01-26 /pmc/articles/PMC8791487/ /pubmed/35081140 http://dx.doi.org/10.1371/journal.pone.0262697 Text en © 2022 Zongye Yang https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Yang, Zongye
Data analysis and personalized recommendation of western music history information using deep learning under Internet of Things
title Data analysis and personalized recommendation of western music history information using deep learning under Internet of Things
title_full Data analysis and personalized recommendation of western music history information using deep learning under Internet of Things
title_fullStr Data analysis and personalized recommendation of western music history information using deep learning under Internet of Things
title_full_unstemmed Data analysis and personalized recommendation of western music history information using deep learning under Internet of Things
title_short Data analysis and personalized recommendation of western music history information using deep learning under Internet of Things
title_sort data analysis and personalized recommendation of western music history information using deep learning under internet of things
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791487/
https://www.ncbi.nlm.nih.gov/pubmed/35081140
http://dx.doi.org/10.1371/journal.pone.0262697
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