Applying Hybrid Fuzzy Conjoint Analysis and Cognitive Maps to Identify Influential Attribute Relationships for Assessment Model Development

Abstract

In order to remedy the attribute model's complexity issue, a more systematic strategy to getting started planning must be undertaken during developing an assessment model. This is necessary to resolve problems and controversies associated with the analysis and interpretation of attribute priority and relationships. The aim is to create a more structured model. Hence, this study proposes the use of hybrid triangular fuzzy conjoint and cognitive maps (TrFCCM) methods as an intelligent procedure for identifying attribute priority and constructing an influential relations map (IRM) during the early stage of assessment model development. The case study demonstrates the successful implementation and suitability of this procedure. The findings indicate that executive function plays a significant role in determining students' mathematics problem-solving ability, followed by attention, working memory, emotion, metacognition, and motivation attributes as alternative assessment factors. Furthermore, the resulting IRM provides insights into the relationship between attributes and enhances understanding of the importance of neuroscience mechanistic in mathematics problem-solving ability. The present research advances the scientific knowledge of how analyses multi-criteria decision-making and human decisions using a triangular fuzzy number-based conjoint and cognitive mapping procedure. It also introduces a more effective procedure for identifying and extracting influential relations among attributes during assessment model development. More impressively, this procedure demonstrates a higher level of application, usability, and performance compared to the state-of-the-art (SOTA) procedure.