An Efficient Comparison Neural Network Methods to Evaluate Student Performance

In present educational frameworks, student performance prediction be getting worsen step by step. Predicting student performance ahead of time can support students as well as to their instructor for monitor progress of a student. Numerous organizations have adopt persistent assessment framework today. Such frameworks are advantageous to the students in improving performance about student studies. This cause about continuous evolutional work toward helped to regular students. As of late, Neural Networks have seen far reaching and effective usage in a wide scope of information mining applications, frequently surpassing different classifiers. This investigation means to explore if Neural Networks are a fitting classifier to predict student performance from Learning Management System information with regards to Educational Data Mining. To survey the applicable of Neural Networks, we think about their predictive performance against six other classifiers on this dataset. These classifiers are Naive Bayes, k-Nearest Neighbours, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression and will be prepared on information acquired during each course. The features utilized for preparing originated through LMS information acquired while performing every course, as well as range from utilization information like time spent on each course page, to grades got for course tasks and tests. Subsequent to preparing, the Neural System beats every one of the six classifiers as far as precision and is comparable to the best classifiers regarding review. We can infer that Neural Networks beat the six different calculations tried on this dataset and could be effectively utilized toward predicting the student Performance. Citation: Dr. V. S. R. Kumari, Suresh Veesa, Ch. Srinivasa Rao. "An Efficient Comparison Neural Network Methods to Evaluate Student Performance ." Global Research and Development Journal For Engineering 6.1 (2020): 4 - 7.