Tutorial requirements of student modeling are traditionally identified by human tutors through crisp mental calculations, and the accuracy of this identification enhances with the experience of the tutor. These identifications become challenging when the mode of delivery is through e-learning. Automatic identification of tutorial requirements must be included in e-learning through ITS where some intelligent computational paradigms can help in automatic identification of the said requirements. This article proposes some fuzzy logic-based techniques toward the said purpose of automatic tutorial gap identification where analysis of the learner's responses to purposefully designed modular MCQ tests and verbal explanations are taken in to consideration as inputs for the purpose. Variation of the fuzzy logics was taken in to account to justify the usefulness of methods under consideration and to provide a realistic and adaptable approach. The experimental results show that the proposed methods are successful and efficient towards the said purpose.