Fuzzy learning performance assessment based on decision making under internal uncertainty

The paper delves into decision making with partial knowledge focused on students' behaviour in multiple-choice testing. To address to this problem, we first provided a binary knowledge model and, then, relaxing some assumptions, allowed for the more realistic framework of partial knowledge which, in turn, adds uncertainty to the assessment of students' knowledge due to their incentive to make a guess in multiple-choice testing. The aim of this paper is to propose a fuzzy assessment model formalised according to Reiter's Theory of Diagnosis to reduce this uncertainty and to draw a distinction between the level of students' ability and the degree of guessing. The provided assessment model was tested on modelled answers with respect to the knowledge frameworks and validated in a real-world context. The findings of this research present the fuzzy learning performance assessment model which may enable a teacher to estimate the level of partial knowledge and, thus, to specify a student's score as well as the results of computational experiments that confirm the validity of the theoretical outcomes.