Leveraging Student Planning in Game-Based Learning Environments for Self-Regulated Learning Analytics

The process of setting goals and creating plans is crucial for self-regulated learning (SRL), yet students often struggle to construct efficient plans and establish goals. Adaptive learning environments hold promise for assisting students with such processes through adaptive scaffolding. Through the examination of data collected from 144 middle school students, we present a data-driven analysis of students' explicit planning activities in Crystal Island, a narrative game-based learning environment. In this game, students are provided with a planning support tool that aids them in externalizing their science-related goals and plans before putting them into action. We extracted features from their planning tool use and connected them to several SRL processes and problem-solving outcomes. We found that students who engaged with the planning support tool were more likely to successfully complete the learning scenario. To investigate the potential for adaptive support with this tool, we also constructed a student plan recognition framework aimed at predicting students' goals and planned action sequences. This framework uses student gameplay sequences as input and student interactions with the planning tool as labels for both prediction tasks. We evaluated these tasks using six machine learning models and found that all approaches improved on the majority baseline classification performance. We then investigated additional machine-learning architectures and a technique for detecting when students enact all steps in their plans as methods for improving the framework. We demonstrated performance improvement with these enhancements. Overall, results demonstrated that the planning support tool can help students engage in SRL activities and drive adaptive support in real time.