Cracking the Code of Learning Gains: Using Ordered Network Analysis to Understand the Influence of Prior Knowledge

Andres Felipe Zambrano, Amanda Barany, Jaclyn Ocumpaugh, Nidhi Nasiar, Stephen Hutt, Alex Goslen, Jonathan P. Rowe, James C. Lester, Eric N. Wiebe, Bradford W. Mott

Abstract: Prior research has shown that digital games can enhance STEM education by providing learners with immersive and authentic scientific experiences. However, optimizing the learning outcomes of students engaged in game-based environments requires aligning the game design with diverse student needs. Therefore, an in-depth understanding of player behavior is crucial for identifying students who need additional support or modifications to the game design. This study applies an Ordered Network Analysis (ONA)—a specific kind of Epistemic Network Analysis (ENA)—to examine the game trace log data of student interactions, to gain insights into how learning gains relate to the different ways that students move through an open-ended virtual world for learning microbiology. Our findings reveal that differences between students with high and low learning gains are mediated by their prior knowledge. Specifically, level of prior knowledge is related to behaviors that resemble wheel-spinning, which warrant the development of future interventions. Results also have implications for discovery with modeling approaches and for enhancing in-game support for learners and improving game design.