Published Paper Details:

Evaluating subjective answers presents significant challenges for teachers. They must meticulously examine each word to assign scores,considering factors such as their own mental state, fatigue, and objectivity.While machines can easily assess Subjective answers by matching them with concise responses provided by students.Addressing these Challenges of assessment ,this system is being proposed.A previous proposed system for subjective answer evaluation using ML incorporates advanced NLP techniques. The system addresses challenges like handling ambiguity, domain adaptation, and bias mitigation offering an automated approach for fair subjective answer assessments.The proposed system have different modules like Preprocessing Module,Evaluation Module,Result Predicting Module and Similarity Measurement Module.The answers will be checked on basis of the Answer length, Keyword Check, Grammar check,Context and Semantic Similarity and Cosine Similarity against model answer. The software will take copy of the answer as an input and then after the preprocessing step it will extract the test of the answer. This text will again go through processing to build a model of keywords and feature sets. Model answer sets and keywords categorized as mentioned will be the input as well. Classifier will then, based on the training will give marks to the answers will be the final output