Automatic descriptive answer evaluator using machine learning.

Automating the evaluation of descriptive answers would be beneficial for academic institutions to

efficiently manage the online exam results of their students. Our project involves designing an

algorithm to automatically evaluate descriptive answers consisting of multiple sentences. Our

approach involves representing the student's answer and comparing it with pre-defined answers

created by the staff. To evaluate the answer, we use a pattern-matching algorithm and various

modules to achieve efficient evaluation without manual labor. This pattern can be used by many

organizations to reduce manpower and save time. Natural Language Processing (NLP) aims to

interpret human language in a meaningful way and typically involves machine learning techniques.

Evaluating the objective function involves assessing candidate solutions against a portion of the

training dataset, usually measured by an error score or loss. While the objective function is easy to

define, evaluating it can be costly.