4.3 Article

Student Behavior Data Analysis Based on Association Rule Mining

Publisher

SPRINGERNATURE
DOI: 10.1007/s44196-022-00087-4

Keywords

Behavior data; Data analysis; Association rule; Mining; Algorithm

Funding

  1. Scientific Research Program - Education Department of Shaanxi Provincial Government [21JK0579]
  2. Program of Shaanxi Society of Technical and Vocational Education [2021SZXYB28]
  3. Program of Shaanxi Energy Institute [20XJZ04]

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This study focuses on the application of association rule mining in analyzing student behavioral data. It proposes a four-layer data association mining architecture and updates the mining algorithm to improve efficiency.
With the advancement of intelligent campus data acquisition technology, student behavioral data are growing in size, variety, and real-time throughput, posing challenges to the storage capacity and computing power of traditional behavioral data analysis methods. The study focuses on the application of association rule mining in student behavioral data analysis. Data collection, storage, computation, and analysis all comprise integral parts of a four-layer data association mining architecture, and the three-step mining process from data preprocessing to finding association rules to acquiring relevant knowledge is described. The existing mining algorithm is updated to address the issues of overscanning of the original dataset and excess iterations. The findings from the case study reveal that the number of iterations in the modified mining algorithm is greatly lessened, effectively improving the mining efficiency of the massive student behavioral dataset.

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