International Journal of Science, Engineering and TechnologyAuthors: Rishi Kumar (PhD Scholar), , Dr. Pritaj Yadav (Assoc. Prof.), Dr. Alok Katiyar (Prof.)
Abstract: The rise of digital platforms has significantly enhanced the management and analysis of educational data. In this context, data mining techniques play a crucial role in extracting meaningful patterns from raw student data, which can be utilized for predicting academic performance and improving learning outcomes. This paper presents a comprehensive survey of recent research focused on student data mining, with particular emphasis on indicators and techniques used for grade prediction. Various data mining methods such as classification, clustering, and regression are discussed in detail, highlighting their applications in academic settings. Additionally, the study examines key indicators that influence student performance and explores privacy-preserving approaches to ensure the ethical use of student data. Evaluation parameters for comparing the effectiveness and issues aspects of different techniques are also analyzed to guide future research in this domain.
DOI: http://doi.org/
