Anmol Mishra, Aniket Bharti, Praful Mishra, Sarita Ambadekar


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)[1],Bilingual Evaluation
Understudy(BLEU)[2] and Fuzzy logic[4], a model
based on degree of truth to output scores. In other
words LSA[1] is used to identify the semantic
similarity between two concepts. Word Mover’s
Distance (WMD)[5], 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.


Latent Semantic Analysis, Singular Value Decomposition, N-gram, Word Mover Distance, Fuzzy Logic.

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