A Comparative Analysis of Techniques for Predicting Tutorial Performance Exploitation Tool Base Data Processing

This “big data” affordance can facilitate learners by distinctive that learning methods would possibly be
best for them, teachers by recommending approaches for serving to students World Health Organization square
measure troubled, and researchers by enabling them to check principles of learning and instruction in authentic
learning environments at scale. Data mining may be a method that uses spread information of knowledge of
information analysis tools to get patterns and relationships in data that will be accustomed create valid predictions.
Most commonly used techniques in data processing are: artificial neural networks, genetic algorithms, rule
induction, nearest neighbor method and memory primarily based reasoning, logistic regression, discriminate an
analysis and Cobweb algorithms. As a first step toward capitalizing on these opportunities, we conducted associate
initial investigation supposed to use giant existing datasets to predict student success and failure in a university. The study we conducted involves the mining and analysis of “big data”, which in our case refers to giant existing datasets that will be analyzed to distinguish patterns in student and teacher performance, identify at-risk students, and study relationships among important variables, such as attendance, learning, and student Satisfaction .