Enhancing Competition-Based Big Data Analytics Learning Through AI-Driven Distributed Scaffolding

Competition-Based Learning (CBL) provides an engaging educational approach by combining collaboration with competition. However, learners often struggle with illstructured problems, particularly in fields like Big Data Analytics. This study investigates how Generative AI, specifically ChatGPT, can support CBL through distributed scaffolding, combining both structural and problem-based approaches to enhance learning. Implemented in an undergraduate Big Data Analytics course, the scaffolding utilized Kaggle for practical problem-solving projects. ChatGPT provided personalized feedback, helping students navigate complex tasks and enhance critical thinking. A mixedmethod evaluation involving surveys and interviews showed that the ChatGPT-supported scaffolding significantly improved knowledge construction, problem-solving skills, and student engagement. These findings highlight the potential of integrating AI-driven scaffolding in CBL environments to address learning challenges, ultimately fostering more effective educational experiences.