  In the framework of Granular Computing (GC), Interval type 2 Fuzzy Sets (IT2
FSs) play a prominent role by facilitating a better representation of uncertain
linguistic information. Perceptual Computing (Per C), a well known computing
with words (CWW) approach, and its various applications have nicely exploited
this advantage. This paper reports a novel Per C based approach for student
strategy evaluation. Examinations are generally oriented to test the subject
knowledge of students. The number of questions that they are able to solve
accurately judges success rates of students in the examinations. However, we
feel that not only the solutions of questions, but also the strategy adopted
for finding those solutions are equally important. More marks should be awarded
to a student, who solves a question with a better strategy compared to a
student, whose strategy is relatively not that good. Furthermore, the students
strategy can be taken as a measure of his or her learning outcome as perceived
by a faculty member. This can help to identify students, whose learning
outcomes are not good, and, thus, can be provided with any relevant help, for
improvement. The main contribution of this paper is to illustrate the use of
CWW for student strategy evaluation and present a comparison of the
recommendations generated by different CWW approaches. CWW provides us with two
major advantages. First, it generates a numeric score for the overall
evaluation of strategy adopted by a student in the examination. This enables
comparison and ranking of the students based on their performances. Second, a
linguistic evaluation describing the student strategy is also obtained from the
system. Both these numeric score and linguistic recommendation are together
used to assess the quality of a students strategy. We found that Per-C
generates unique recommendations in all cases and outperforms other CWW
approaches.
