Predicting Student's Performance Through Data Mining
This addresses a problem for educational institutions by enabling early intervention for at-risk students, but it appears incremental as it applies existing methods to a specific domain.
The paper tackles the challenge of predicting student performance early and accurately by using machine learning on data from Learning Management Systems, aiming to identify strengths and weaknesses to improve exam outcomes.
Predicting the performance of students early and as accurately as possible is one of the biggest challenges of educational institutions. Analyzing the performance of students early can help in finding the strengths and weakness of students and help the perform better in examinations. Using machine learning the student's performance can be predicted with the help of students' data collected from Learning Management Systems (LMS). The data collected from LMSs can provide insights about student's behavior that will result in good or bad performance in examinations which then can be studied and used in helping students performing poorly in examinations to perform better.