A study on data analysis of student achievement under adult education through association rule algorithm

Yuan Dong

International Journal of Data Science, 2025, vol. 10, issue 2, 195-209

Abstract: Students receiving adult education must complete academic tasks while balancing work and life, which may cause poor learning results. The student learning situation can be better comprehended by analysing student performance data. In order to realise the analysis of students' course performance under adult education, this paper designed an improved Apriori algorithm based on the Boolean matrix for the problem of low efficiency. Through the experiments on the webdocs and mushroom datasets, it was found that when Sup_min = 0.1, the running time of the improved Apriori algorithm for the webdocs dataset was 1123 s, which was 20.64% shorter than the FPGrowth algorithm and 26.17% shorter than the Apriori algorithm. The running time for the Mushroom dataset was 27.38 s, which was 76.73% shorter than the Apriori algorithm and 55.31% shorter than the FPGrowth algorithm.

Keywords: association rule; adult education; student achievement; Apriori algorithm; Boolean matrix; minimum support; professional course performance; curriculum arrangement. (search for similar items in EconPapers)
Date: 2025
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