Decision-Making Model for Student Assessment by Unifying Numerical and Linguistic Data

Abstract

Learning assessment deals with the process of making a decision on the quality or performance of student achievement in a number of competency standards. In the process, teacher’s preferences are provided through both test and non-test, generally in a numeric value, from which the final results are then converted into letters or linguistic value. In the proposed model, linguistic variables are exploited as a form of teacher’s preferences in non-test techniques. Consequently, the assessment data set will consist of numerical and linguistic information, so it requires a method to unify them to obtain the final value. A model that uses the 2-tuple linguistic approach and based on matrix operations is proposed to solve the problem. This study proposed a new procedure that consists of four stages: preprocessing, transformation, aggregation and exploitation. The final result is presented in 2-tuple linguistic representation and its equivalent number, accompanied by a description of the achievement of each competency. The α value of 2-tuple linguistic in the final result and in the description of each competency becomes meaningful information that can be interpreted as a comparative ability one student has related to other students, and shows how much potential is achieved to reach higher ranks. The proposed model contributes to enrich the learning assessment techniques, since the exploitation of linguistic variable as representation preferences provides flexible space for teachers in their assessments. Moreover, using the result with respect to students’ levels of each competency, students’ mastery of each attribute can be diagnosed and their progress of learning can be estimated.