Hybrid fuzzy analytical hierarchy process with fuzzy inference system on ranking stem approach towards blended learning in mathematics

In the era of Education 4.0, blended learning has been selected as one of the transformational pedagogies for the teaching and learning process that integrate Science, Technology, Engineering, and Mathematics (STEM), a new norm that needs to be adopted by Malaysia. Since the COVID-19 pandemic, the issue has been highlighted at most levels of study in the education field. However, limited knowledge of the implementation of 21st Century learning skills with Web 2.0 among teachers has made the students demotivated for their mathematics classroom. Moreover, dynamic changes in the standard curriculum have made the situation more challenging for teachers in selecting the appropriate STEM approach to ensure students are fully engaged. Inspired by the problem, this research used fuzzy multi-criteria decision-making (MCDM) concepts. A hybrid fuzzy MCDM model proposes a four stages process to rank and find the best implementation STEM approach in the mathematics classroom. The model is constructed by integrating the Fuzzy Analytical Hierarchy Process (FAHP) to determine the weights of STEM criteria and sub-criteria and the Fuzzy Inference System (FIS) to compute the best STEM approach in the mathematics classroom. The procedure involves exploring the issue associated with the selection problems, deriving decision criteria important weights, and ranking various alternatives with applied intuitive multiple centroids as a defuzzification method. The results showed hands-on activities as the best STEM approach while requisite knowledge is the important criterion with the greatest value of weights. Thus, the proposed model helps provide a clear picture for teachers in the implementation of STEM approach in Mathematics based on a comprehensive view and also lay a new foundation knowledge in fuzzy MCDM view, particularly in STEM education. Also, it helps the Ministry of Education (MoE) to achieve one of the initiatives in Wave 3 of the Malaysia Education Blueprint (2021-2025), which is to share the best practice in the classroom to cultivate a peer-led culture of professional excellence among teachers as the basis for improving the implementation and achievement of STEM at the national level

A Fuzzy Inference System for Multiple Criteria Job Evaluation Using Fuzzy AHP

Job evaluation is defined as the methods and practices of ordering jobs or positions with respect to their value or worth to the organization. The purpose of the job evaluation is eliminating the pay inequalities by developing a pay structure based on values of the jobs. The job evaluation problem may be treated as a managerial decision-making problem under multiple criteria. In this study, an integrated fuzzy approach is developed for job evaluation problem which aims to establish a base for an ideal compensation system. In the proposed approach initially, the relative importance weights of the job factors are determined and subsequently, job compensation groups are maintained by a fuzzy inference system integrated with importance weights of the factors. A sample case study has been considered and ten different jobs are classified into four different job compensation groups according to the proposed approach