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.

Computing with words (CWW) has emerged as a powerful tool for processing the linguistic information, especially the one generated by human beings. Various CWW approaches have emerged since the inception of CWW, such as perceptual computing, extension principle based CWW approach, symbolic method based CWW approach, and 2-tuple based CWW approach. Furthermore, perceptual computing can use interval approach (IA), enhanced interval approach (EIA), or Hao-Mendel approach (HMA), for data processing. There have been numerous works in which HMA was shown to be better at word modelling than EIA, and EIA better than IA. But, a deeper study of these works reveals that HMA captures lesser fuzziness than the EIA or IA. Thus, we feel that EIA is more suited for word modelling in multi-person systems and HMA for single-person systems (as EIA is an improvement over IA). Furthermore, another set of works, compared the performances perceptual computing to the other above said CWW approaches. In all these works, perceptual computing was shown to be better than other CWW approaches. However, none of the works tried to investigate the reason behind this observed better performance of perceptual computing. Also, no comparison has been performed for scenarios where the inputs are differentially weighted. Thus, the aim of this work is to empirically establish that EIA is suitable for multi-person systems and HMA for single-person systems. Another dimension of this work is also to empirically prove that perceptual computing gives better performance than other CWW approaches based on extension principle, symbolic method and 2-tuple especially in scenarios where inputs are differentially weighted.