Data Mining Algorithms and Models for Distance Education Management
Ye Qing
Journal of Intelligent Computing, Vol. 17, No. 1, 2026.
DOI: https://doi.org/10.6025/jic/2026/17/1/1-13
Link: https://www.dline.info/jic/fulltext/v17n1/jicv17n1_1.pdf
Abstract:
This paper explores the application of data mining techniques to enhance the management and evaluation of
distance education. It begins by contextualizing China's educational reforms, emphasizing decentralization
and institutional autonomy, which have increased the need for data driven decision making. The study
highlights the challenges of online learning remarkably low supervision, poor instructional quality, and
high dropout rates and proposes Educational Data Mining (EDM) as a solution. EDM leverages algorithms
like decision trees (especially C4.5), K-means, Apriori, SVM, KNN, and Naive Bayes to analyze student
behavior, predict performance, and support timely interventions. Among these, the decision tree algorithm
is selected for its interpretability, accuracy, and efficiency in handling diverse data types. The paper details
the algorithm's computational framework, including information entropy and gain calculations, and presents
an intelligent model for distance education management. Experimental results demonstrate that the optimized
C4.5-based model improves both accuracy and processing speed compared to traditional methods.
Association rule mining reveals significant behavioral patterns linked to student success, such as homework
scores and forum participation. The study concludes that integrating data mining into distance education
enables proactive, personalized support and more effective administrative oversight. While traditional
assessment persists, algorithmic approaches offer a scalable, equitable pathway to enhance teaching,
learning, and institutional management in the era of big data. Further refinement of these models is
recommended for broader and more robust application.