Computing with words for student strategy evaluation in an examination

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 student’s strategy can be taken as a measure of his/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 student’s strategy. Furthermore, the linguistic recommendation is useful for human beings as they naturally understand and express themselves using ‘words’, ‘words’ being treated as fuzzy information granules in the GC paradigm, which is perhaps the case with most of the human reasoning and concepts. In addition, through the comparison of the recommendations generated by different CWW approaches, we found that Per-C outperforms the others CWW approaches by generating unique recommendations in all the cases as well as modeling the word uncertainty in the best possible way.