THEORETICAL ANSWER EVALUATION USING NLP AND FUZZY LOGIC

Assessment of student answers to grade their overall understanding of a subject is a critical task.However grading can be monotonous and sometimes can be tedious task for the teachers.Automatic Grading can reduce tedium on teachers but it is complicated by free form student inputs. The main task of automatic grading system is to assign ordinal scores to student answers, based on “model” or ideal answers. Here we introduce a novel framework comprising of three building blocks Word Mover Distance(WMD)a statistical model Latent Semantic Analysis(LSA),Bilingual Evaluation Understudy(BLEU) and Fuzzy logic, a model based on degree of truth to output scores. In other words LSA is used to identify the semantic similarity between two concepts.Word Mover’s Distance (WMD), uses vector encoding of words to calculate the minimum cumulative distance that words from a reference solution need to travel to match words from a student answer.This cumulative distance assess the distance between two documents in a meaningful way, even when they have no words in common. Fuzzy logic is a primitive model in this system which is used to output the final score based on inputs which are the outputs of LSA and WMD.This proposed method gives better precision, enhanced dependability of results, thus saving the effort and time of staff.