Analysis of Educational Data Mining

This research paper aims to compare the performance of various clustering and classification algorithms which are applied on the same educational dataset. Educational Data Mining (EDM) uses these algorithms to explore educational statistics to discover patterns and predictions in data that illustrate learner’s performance. Various design challenges such as accuracy, objective and functionality, and overheads when the data set is extremely large, etc., have been highlighted. The algorithms discussed here can be classified as centroid-based clustering, graph-based clustering, and various supervised classification algorithms. Further, after comparison of these algorithms, this paper aims at using the cited literature survey to determine the most suited algorithm according to the need of EDM clustering or classification. The regular search is on to understand students better and to know the patterns in which they learn to make it more efficient for them. Nowadays, many educational institutions have educational databases that can be utilized in various ways to make it effective for students but are unutilized. Powerful tools are required to get benefits from these educational databases. EDM is one of those emerging tools that analyzes the data collected from learning and teaching and then applies the techniques from machine learning and data mining for predicting student’s future behavior by learning detailed information such as student’s grades, knowledge, achievements, motivation, and attitude.

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