Score normalization for a university grades input system using a neural network

Abstract

A university grades input system requires for professors to enter the normalized total scores for the letter grades and to input the scores from six fields such as Midterm, Final, Quiz which sum up to the total. All six fields have specified bounds which add up to 100. Professors should scale in the total scores to match up the letter grades and scale in every field of each student's original scores within the bounds to sum up to the scaled total score. We solve this problem by a novel design of simple shallow neural network.