A Qualitative Evaluation and Comparision of Cognitive Learning Analytics for Blended Learning Technology - V. Lahari Sowmya , Dr. A. Kousar Nikhath (International Journal of Advanced Science and Technology, 2020)
Recent education frameworks, students performances prediction getting worsen step by step. Predicting students performance  ahead of time able to support students  as well as toward their instructor for  monitor progress of a student. Numerous organizations have adopt persistent assessment framework  now. That frameworks be advantageous toward students with  performancess about studies. This cause about continuous evaluational work toward helped to regular students based on the 5 types of learning’s like Online Learning, Blackboard learning, WIT &WILL, Flipped classrooms and Learning by doing. As of late  Neural Networks be far reaching as well as effective usage with a broad scope about information mining application, frequently surpassing different classifier. The investigation means toward explore about Neural Networks be fit classifiers toward predicts students performancess  through Learning Management System information with regards to Educational Information Mining. Toward survey  applicable about Neural Networks, we think about prediction performancess among six  classifiers upon the datarecords. These classifiers are Naive Bayes, k-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression as well as  be prepared upon information acquired while every thing. These features utilized intended to preparing originated through  LMS information acquired while performing every course, as well as range from utilization information 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 able to infer so as to Neural Networks beat this different calculations tried upon the datarecords  as well as able to effectively utilized toward predicting the student Performance.