  In order to track and comprehend the academic achievement of students, both
private and public educational institutions devote a significant amount of
resources and labour. One of the difficult issues that institutes deal with on
a regular basis is understanding the exam shortcomings of students. The
performance of a student is influenced by a variety of factors, including
attendance, attentiveness in class, understanding of concepts taught, the
teachers ability to deliver the material effectively, timely completion of home
assignments, and the concern of parents and teachers for guiding the student
through the learning process. We propose a data driven approach that makes use
of Machine Learning techniques to generate a classifier called credit score
that helps to comprehend the learning journeys of students and identify
activities that lead to subpar performances. This would make it easier for
educators and institute management to create guidelines for system development
to increase productivity. The proposal to use credit score as progress
indicator is well suited to be used in a Learning Management System. In this
article, we demonstrate the proof of the concept under simplified assumptions
using simulated data.
