# Effectiveness and Design of PBL-Based Project Approach for Non-Major University Computing Courses
## 1 Introduction  
With the advancement of digital transformation and artificial intelligence (AI) technologies, the importance of Computational Thinking (CT) has expanded [1]. CT refers to the ability to analyze problems and solve them through algorithmic and procedural structures. Beyond simple programming, it has become an essential mindset for systematically addressing complex problems across various fields and is widely regarded as a core competency required in the AI-driven era. Python, a versatile tool widely used across disciplines, plays a role similar to that of Excel in the past. Its applications in data analysis, automation, and artificial intelligence demonstrate that computing skills are no longer exclusive to developers. In the future, non-major students will also need problem-solving skills grounded in CT and AI-driven thinking to meet the demands of various fields [2]. In response to these needs, many universities have integrated computing courses into their general education curricula. These courses are designed not only to teach programming skills but also to help students understand the importance of problem-solving processes using concepts such as data structures and algorithms. Rather than training students to become developers, these courses aim to equip them with fundamental CT and collaboration skills necessary to work effectively with developers and solve problems together. This approach enables students from diverse academic backgrounds to logically address real-world problems that they may encounter in their personal and professional lives [3; 4]. Previous studies have primarily focused on the theoretical approaches and effectiveness of Problem-Based Learning (PBL) and Project-Based Learning (PJBL) in computing education for non-major learners [5; 6; 7; 8; 9]. These studies often emphasize providing non-majors with introductory experiences in computing and artificial intelligence, enabling basic engagement with the subject matter. However, there is a noticeable lack of research on how computing education can be tailored for non-majors, particularly in foundational areas such as programming, data structures, and algorithms, and how these can be integrated with learners' respective disciplines to foster interdisciplinary convergence. This research gap poses challenges for the design of learning environments that equip non-major learners with practical problem-solving skills. To address this, our study aims to design computing education programs for non-majors that focus on solving real-world problems and to evaluate their effectiveness. This study introduces a PBL-based Project-Based Learning (PJBL) approach, which extends the traditional PBL framework by incorporating long-term project development and implementation. PBL serves as the foundation, focusing on analyzing and resolving smaller, well-defined problems through collaborative inquiry and decision-making. Building on this foundation, the PJBL model requires students to design, plan, and execute complex, real-world projects that demand the application of problem-solving strategies in authentic contexts [10]. The PBL-based PJBL model employed in this study goes beyond individual problem-solving tasks. Students not only analyze and extend existing code written by their peers to apply it to new problems but also engage in feedback exchanges with their peers on the extended code. This process helps students understand how data structures and algorithms are applied to real-world problem-solving and provides them with essential collaborative coding and communication skills. Building on previous research, this study compares the outcomes of a PBL-based PJBL project evaluation with those of traditional written and practical exams. Through this comparison, the study seeks to derive insights into the learning outcomes and educational implications of computing courses for non-major students.