Educational Data Mining: Techniques, Applications, and Challenges

@article{1707347,
      author = {Dr. Abhishek Raghuvanshi},
      title = {Educational Data Mining: Techniques, Applications, and Challenges},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {9},
      pages = {112-126},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707347.pdf},
      abstract = {Educational Data Mining (EDM) is an emerging interdisciplinary field that utilizes data mining techniques to analyze and extract valuable insights from educational data. The aim of this research paper is to provide a comprehensive overview of the various methods used in EDM, explore their practical applications, and discuss the challenges and opportunities in this rapidly evolving field. As educational institutions increasingly leverage digital technologies, the ability to harness data from student performance, behavior, and interactions provides significant opportunities to enhance learning outcomes, personalize instruction, and improve overall educational processes. This paper examines key EDM techniques such as classification, clustering, regression, and association rule mining, and highlights their applications in predictive analytics, student modeling, curriculum design, and learner support.},
      month = {March},
  }