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Research on the Application of Enhanced NVivo Model in Education Competition through Data Mining and Visualizations

Published: 30 May 2024 Publication History

Abstract

The primary objective of this study is to investigate the application of an enhanced NVivo model, incorporating data mining and visualization techniques, in the context of educational competitions. By utilizing an improved NVivo model and relevant data mining and visualization techniques, we aim to analyze data pertaining to participant characteristics, project themes, trends, and evaluation outcomes in educational competitions, with the sample being derived from the list of winners of the “Internet Plus” University Student Innovation Competition between 2015 and 2022. Through comprehensive data mining and visualization, we seek to acquire a profound understanding of educational competitions and provide valuable insights and decision-making support to both organizers and participants. This research endeavor is anticipated to contribute significantly to the advancement of educational competitions by enhancing their quality and efficacy, thereby fostering the cultivation and development of students’ innovation and entrepreneurial capabilities.

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Li, X., & Wang, H. 2018. The Application of Data Mining in Educational Competitions: A Case Study of Math Competitions. International Journal of Emerging Technologies in Learning, 13(4), 65-74.
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Wang, Y., & Zhang, L. 2019. Application of Data Visualization in Educational Competition Research. Modern Educational Technology, 29(4), 120-126.
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Zhang, J., & Zhang, L. 2017. Research on the Application of NVivo in Educational Competition Analysis. Journal of Distance Education, 35(3), 63-70.
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Zhu, W., & Wu, Y. 2016. Analysis of Educational Competition Data Based on Data Mining and Visualization. Journal of Educational Technology Development and Exchange, 9(3), 76-84.
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Baker, R. S., & Inventado, P. S. 2014. Educational data mining and learning analytics. In International Handbook of Information Technology in Primary and Secondary Education (pp. 135-152). Springer.
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Romero, C., & Ventura, S. 2013. Educational data mining: A review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601-618.
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    ICIEAI '23: Proceedings of the 2023 International Conference on Information Education and Artificial Intelligence
    December 2023
    1132 pages
    ISBN:9798400716157
    DOI:10.1145/3660043
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 30 May 2024

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