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Construction of personalized learning and knowledge system of chemistry specialty via the internet of things and clustering algorithm

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Abstract

The present work aims to construct knowledge systems of different specialties, provide personalized learning approaches for students, improve the teaching effectiveness of teachers and learning interests of students, and help students accurately diagnose their learning problems. First, the existing issues of students’ chemistry learning and knowledge systems are analyzed. Then, a personalized learning model is constructed based on the distributed computing method of the Internet of Things (IoT) and the clustering algorithm of deep learning (DL). This model realizes the effective classification of students by the DL algorithm according to the students’ learning data and designs diverse customized teaching contents in line with the performance of IoT. Finally, this model’s effectiveness is verified through the data analysis of 2019 chemistry students at M University. The research results indicate that 96.67% of students express satisfaction with the learning effect of the personalized learning model, and 100% of learners are satisfied with the various forms of teaching resources offered by this model. Besides, this model’s accuracy can reach 85% on the personalized learning platform based on IoT and DL algorithms. Compared with the latest research model, this model has a better performance in achievement prediction and customized recommendation. The test results of the actual effect demonstrate that the personalized learning system has achieved expected outcomes, significantly enhancing students’ understanding of knowledge with medium difficulty. In addition, learners are delighted with the model. They believe that the learning resources in the model can meet their learning needs, and they are willing to recommend the course to other learners actively. This model is of significant practical value to promote the development of IoT and DL technology in professional learning. This exploration innovatively stratifies learners’ level of understanding and provides personalized learning resources meeting the current cognitive competence of students to enhance learners’ knowledge and achieve personalized learning.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (No. 61902203) and Key Research and Development Plan—Major Scientific and Technological Innovation Projects of ShanDong Province (2019JZZY020101).

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Authors and Affiliations

  1. School of Chemistry and Chemical Engineering, Hunan University of Science and Technology, Xiangtan, 411201, Hunan Province, China

    Min Wang

  2. School of Data Science and Software Engineering, Qingdao University, Qingdao, 266071, Shandong Province, China

    Zhihan Lv

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  1. Min Wang
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  2. Zhihan Lv
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Correspondence to Min Wang.

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Wang, M., Lv, Z. Construction of personalized learning and knowledge system of chemistry specialty via the internet of things and clustering algorithm. J Supercomput 78, 10997–11014 (2022). https://doi.org/10.1007/s11227-022-04315-8

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  • DOI: https://doi.org/10.1007/s11227-022-04315-8

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