Analysis of Student’s Education Data Based on Data Mining Techniques
Keywords:
Performance prediction, Education data mining, DM techniquesAbstract
One of the trickiest and most popular research fields in educational data mining (EDM) is student achievement analysis. Scientists get attracted to this issue owing to the fluctuating implications of multiple factors on functionality. This dedication is additionally ignited by the huge consumption of instructional records, especially when it comes to online learning. Although there are numerous EDM surveys in the scholarship, there aren't plenty that solely concentrate on student achievement evaluation and projection. These specialized assessments are small in focus and largely emphasize investigations that seek to find potential predictors or patterns of student achievement. This paper proposes data mining through the required algorithms for the accurate extraction of data for further analysis. A brief overview of the current situation of studies in that field is the goal that this literature review attempts to communicate. We employed a couple of methods for performing a literature review: initially, we employed the primary search engines to identify documents, and then we picked them based on previously established requirements. The info collected from student conversations with learning management systems and assessment tasks were the most important elements in early forecasting. At last, the kind of schooling system identified how early projections could be formed.
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