Learning through Simulation: A Role-Based Learning Exercise to Equip Students in Big Data Requirement Elicitation Skills and Challenges - PPG Dinesh Asanka; Thilini V. Mahanama (Universitas Dian Nuswantoro, 2024)
Bridging the gap between theoretical knowledge and practical application remains challenging in big data education. Our paper proposes a platform where students actively participate in a role-based learning (RBL) exercise designed to simulate real-world big data projects. This RBL exercise assigns each student participant to a business analyst role tasked with eliciting requirements or a customer role that provides those big data requirements. We conducted RBL sessions for postgraduate and undergraduate students learning big data analytics/data warehousing. Through role-playing in a collaborative environment, students face challenges such as unclear objectives, data privacy concerns, and evolving requirements. We evaluated the effectiveness of RBL simulations through brainstorming sessions, which verified that the students achieved the learning outcomes through RBL exercises. We also collected students’ feedback through a survey and found the RBL experience helped us understand the big data requirement elicitation skills, highlighting the significance of communication and collaboration skills. Further, we have evaluated students through a final exam question, and identified that students who participated in the RBL exercise outperformed in the big data requirement elicitation question. In summary, our research demonstrates that this RBL approach offers a valuable learning experience by enabling students to directly experience the complexities of big data requirement elicitation and identify the future requirements or challenges that encourage them to acquire the required skills.