Engineering education is a complex that involves multiple stakeholders, including students, educators, administrators, and industry partners. It is continuously evolving to meet the demands of modern industry and society. The traditional teaching and learning methodologies are being replaced by a more integrated skillset that focuses on developing students' cognitive, social, and emotional skills. The shift towards this integrated approach is gaining momentum, and it is important to have a framework that has been proven to solve complex systems. The usage of systems engineering tools to model engineering education systems is not often seen. In this paper, a novel application of Model-Based Systems Engineering using Systems Modeling Language (SysML) to develop an Engineering Learning Analytic System (ELAS) framework that consists of multi-dimensional elements related to educational systems. The core of this study involves a rigorous Requirements Verification and Validation (V&V) process to ensure stakeholder needs which systematically were map with system capabilities. ELAS model simulations provided predictive insights into soft skill development, enabling decision-making via targeted interventions that could significantly enhance students' skill sets. ELAS highlights that a data-driven approach, enabled by SysML, significantly enhances the ability to enact timely by relevant interventions at various levels of the educational management process. The proposed ELAS model offers a strategic blueprint for continuous improvement within educational institutions, demonstrating a pathway toward a responsive and self-improving educational system. The refining of the ELAS model, for broadening simulation scopes, and further integrating predictive analytics into administrative decision-making processes is an ongoing endeavor.

The research addresses a critical aspect of organizational settings â\euro“ team formation â\euro“ which is vital for effective team-based learning. In many organizational environments, successful teamwork depends on the composition of teams that consider various criteria, such as skill levels, background, and personality traits. Meanwhile, maintaining fairness and equity among teams is crucial to ensure equal opportunities for all team members. Our research framework lies in its ability to model a diverse range of factors, while simultaneously maximizing within-team diversity and minimizing conflict levels. In this study, we introduce a novel application of multi-criteria integer programming (MCIP) that diverges from traditional and single-objective optimization methods to address the complexity of team formation with a focus on multiple criteria. This approach offers a comprehensive solution that accommodates multiple criteria simultaneously. The contributions of this paper are: 1) An innovative optimization model for team formation that addresses the complex task of maximizing intra-group diversity while minimizing inter-group diversity. 2) Addressing the challenge of forming balanced and diverse student teams and providing educators with a practical tool for effective team formation.