Agent-Based Gamified Learning Environments for Data Science Education

Abstract

Because of the rapid advancement of technology and the increasing importance of the inferences that can be drawn from the big data available in organizations, modern organizations require managers and data Analysts who are capable of data-driven decision-making. But data science students need a natural environment when it comes to learning data-driven decisionmaking, especially when it comes to predictive and prescriptive analytics. Due to costs and other associated risks in a natural organisation setting, it is hard for educational institutions to teach these aspects of decision-making for data science students. Even Though gamification has been implemented in the data analysis domain in various forms, the field still requires a suitable environment to learn predictive analytics interactively for the students. Even though Researchers have identified that Gamified learning environments can improve Predictive analytics learning can be improved by 15.8%, still there is the lack of proper implementation of a suitable gamified learning environment. This research focused on identifying drawbacks of existing learning environments and whether Agent-Based Modeling can be used in modelling a suitable gamified learning environment. Therefore, an agent-based prototype model of a parameterized environment that enables data-driven decision-making in a simulated environment was modeled using Agentbased modeling, which depicts real-life donor interactions. Results suggest that fill in blanks This Agent-based model can be used as a learning environment for data analysis. Upon further modification, A game that applies this Agent-based model can be developed.