How to identify good and poor problem solvers? An application using the Z-MABAC method

This study was conducted to identify good and poor problem solvers using the classical MABAC and Z-MABAC methods, which are multi-criteria decision-making methods. A multiple case study, a qualitative research design, was used in the study. This research process consisted of three phases: preparation, implementation, and evaluation. During the preparation phase, the literature was first reviewed to identify criteria and sub-criteria related to these criteria that could distinguish good and poor problem solvers. Three different criteria were identified: cognitive, affective, and environmental. The cognitive domain included a problem-solving achievement test, mathematics achievement, and metacognition sub-criteria; the affective domain included mathematics attitude, mathematics anxiety, and perception of mathematics self-efficacy sub-criteria; and the environmental domain included a teacher opinion form, a parent opinion form, and a peer opinion form. The SCOPUS database was then searched to identify decision-makers. Four decision-makers, experts in mathematics education, participated in the study. After determining the criteria and decision-makers, alternatives were selected. As an alternative, seven 8th-grade students from a middle school in a province in northeastern Turkey were included in the study. During the implementation phase, the alternatives were ranked using the classical MABAC and Z-MABAC methods. During the evaluation phase, the results obtained from the classical MABAC and Z-MABAC methods were compared. The study results showed that the comparison using the classical MABAC and Z-MABAC methods highlighted different alternatives. While the ranking scores of the alternatives were very close in the classical MABAC, they were found to diverge and be more distinguishable in the Z-MABAC method.