Integrating the Bisection Method into a STEM-Based Learning Model on Linear Programming for Robotics-Oriented Problem Solving and 21st-Century Skills Enhancement

Abstract

This research aims to develop a STEM-based learning model on linear programming that integrates the bisection method to minimize modeling errors and enhance 21st-century skills among senior high school students. The study adopts a Research and Development (R&D) approach using the ADDIE model, encompassing analysis, design, development, implementation, and evaluation stages. The novelty of this model lies in embedding the bisection method not only as a numerical technique but also as a pedagogical strategy to support students in formulating more accurate mathematical models. Importantly, this study connects mathematical modeling in linear programming with real-world applications in robotics and automation, where optimization under constraints is critical. By engaging students in problem-solving scenarios inspired by robotics—such as task scheduling, energy management, and path planning—students learn to apply iterative logic similar to the reasoning used in robotic decision-making processes. The bisection method enables them to systematically refine solutions, reduce conceptual errors, and strengthen logical consistency. Data were collected through expert validation, pre- and post-tests, and observations of 21st-century skills, including critical thinking, creativity, collaboration, and communication. The findings reveal that the integration of the bisection method significantly reduces modeling errors and promotes interdisciplinary thinking relevant to robotic system design. This model offers a promising instructional innovation that bridges mathematical theory, STEM-based pedagogy, and the practical reasoning frameworks of intelligent robotic systems.