PROJECT TITLE :

Predicting Student Performance Using Personalized Analytics

ABSTRACT:

To assist solve the continued downside of student retention, new expected performance-prediction techniques are required to facilitate degree coming up with and confirm who would possibly be in danger of failing or dropping a class. Personalized multiregression and matrix factorization approaches based mostly on recommender systems, initially developed for e-commerce applications, accurately forecast students' grades in future courses and on in-category assessments.