The prediction of student’s academic performance using RapidMiner

Abstract

Students’ performance analysis basically consists of determining the factors influencing the performance and how it will give impact towards success. It will help us to understand students’ behavior and how to improve their academic performance. The efficiency of this analysis depends on the information given by the user through learning management system (LMS). In order to improve the information, we have applied algorithms on the dataset and prepared a model by using Tableau and RapidMiner. Cross-validation with decision tree also has been applied on datasets. This can help in evaluating statistical computational results into a generalized data set. Based on the calculation of data mining, it can analyze that our model is quite stable since it has high accuracy with lower standard deviation. So, the processes like testing and validation, applying the model and decision tree on RapidMiner generates the output in a specific form. The result shows that the percentage of students who are absence is better than students who are absence more than 7 days. At last, a model is prepared, and it can help the schools, students, and the parents in adapting appropriate measures to ensure the success of students at school.