  Grading of examination papers is a hectic, time-labor intensive task and is
often subjected to inefficiency and bias in checking. This research project is
a primitive experiment in the automation of grading of theoretical answers
written in exams by students in technical courses which yet had continued to be
human graded. In this paper, we show how the algorithmic approach in machine
learning can be used to automatically examine and grade theoretical content in
exam answer papers. Bag of words, their vectors & centroids, and a few semantic
and lexical text features have been used overall. Machine learning models have
been implemented on datasets manually built from exams given by graduating
students enrolled in technical courses. These models have been compared to show
the effectiveness of each model.
