Analysis and Visualization of High School Student Achievement Data Using Decision Tree and Cross-Validation in Rapidminer

Abstract

The vision, mission, and work indicators of high school education are all geared toward producing graduates of excellent quality. Students’ academic performance in the first and second grades indicates their eventual excellence as graduates. However, it is not feasible to ensure that students in the third year have good results only by comparing their academic achievements during the first and second grades. School leaders and policymakers can benefit from data analysis and visualization by gaining insight into recurring patterns and emerging trends in their stored data. Leaders and management at schools can benefit significantly from Rapidminer’s decision tree and cross-validation data analysis methods when trying to figure out what to do about students performing well or poorly academically.