Stacking Based Machine Learning Approach for Student Performance Analysis

Students academic performance forms an important aspect in education. Students who are not performing well faces multiple problems like graduating after the scheduled time, or dropping out of college or universities. Thus, educational institutes should observe the performance of their students' closely so that they can focus on the students with bad or low performance in the early stages to avoid such issues. This work is focused on building a model for the prediction of students' academic performance with the help of machine learning. In this work, multiple machine learning algorithms were implemented on the various academic and background data of the students' which were expected to impact the students' grades to predict their progress and their final grades which will help educators to optimize their teaching and learning strategies.