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<p>In the contemporary era of big data, the educational landscape is undergoing significant transformations. Literature education, as a vital component, faces both emerging opportunities and challenges. This study develops a framework for analyzing learning behaviors specific to literature education. It employs both an enhanced K-means clustering algorithm and a refined Apriori algorithm for mining data on student learning behaviors. Through cluster analysis and the investigation of association rules, this research explores the interconnections between students’ learning behaviors and their literary education. The findings categorize students into four distinct groups based on their learning behaviors. Students in Category 1 are identified as the most proficient learners, consistently achieving test scores above 85. Conversely, Category 2 students display the least motivation and effectiveness, with their examination scores not exceeding 70. Students in Categories 3 and 4 exhibit comparable levels of performance. Crucially, the analysis reveals that the most significant predictors of students’ literary achievement are their regular and examination scores, with correlation coefficients of 0.627 and 0.653, respectively. This segmentation and analysis of student behaviors facilitate the early detection of atypical learning patterns by educational practitioners, enabling timely intervention strategies to enhance academic outcomes.</p>
</abstract>

In the contemporary era of big data, the educational landscape is undergoing significant transformations. Literature education, as a vital component, faces both emerging opportunities and challenges. This study develops a framework for analyzing learning behaviors specific to literature education. It employs both an enhanced K-means clustering algorithm and a refined Apriori algorithm for mining data on student learning behaviors. Through cluster analysis and the investigation of association rules, this research explores the interconnections between students’ learning behaviors and their literary education. The findings categorize students into four distinct groups based on their learning behaviors. Students in Category 1 are identified as the most proficient learners, consistently achieving test scores above 85. Conversely, Category 2 students display the least motivation and effectiveness, with their examination scores not exceeding 70. Students in Categories 3 and 4 exhibit comparable levels of performance. Crucially, the analysis reveals that the most significant predictors of students’ literary achievement are their regular and examination scores, with correlation coefficients of 0.627 and 0.653, respectively. This segmentation and analysis of student behaviors facilitate the early detection of atypical learning patterns by educational practitioners, enabling timely intervention strategies to enhance academic outcomes.

Analysis of Literature Education Strategies and Learning Behaviors in the Age of Big Data

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In the contemporary era of big data, the educational landscape is undergoing significant transformations. Literature education, as a vital component, faces...

Analysis of Literature Education Strategies and Learning Behaviors in the Age of Big Data

Publié en ligne: 02 juil. 2024

Pages: -

Reçu: 19 févr. 2024

Accepté: 14 mai 2024

DOI: https://doi.org/10.2478/amns-2024-1569

Mots clés
K-means, Apriori, Learning behavior, Literature education

© 2024 Chong Luo., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of Literature Education Strategies and Learning Behaviors in the Age of Big Data

Publié en ligne: 02 juil. 2024

Pages: -

Reçu: 19 févr. 2024

Accepté: 14 mai 2024

DOI: https://doi.org/10.2478/amns-2024-1569

Mots clésK-means, Apriori, Learning behavior, Literature education

© 2024 Chong Luo., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
K-means, Apriori, Learning behavior, Literature education