Improved Evaluator for Subjective Answers Using Natural Language Processing

Examination is a preliminary process for evaluating our knowledge. While objective examinations consist of multiple-choice questions, which are easy to evaluate using simple methods, whereas subjective examinations, which often require written responses, having a significant challenge in uniform evaluation. This type of answer evaluation is a time-consuming process to undertake. This can lead to inconsistencies and biases in grading due to the stress of examiners having to carry out the same task repeatedly. With the technological advancements in the field of Natural Language Processing, which offers a range of techniques that can be employed to evaluate text responses, we have proposed a novel solution for creating an evaluator that increases the accuracy as near to human evaluators compared to other such existing systems for evaluating subjective answers. This model works by taking inputs like the user response, expected sentences and expected keywords and works with the help of numerical vectors, natural language processing and deep learning approaches and other mathematical calculations to calculate the aggregate score for an answer.