An active research area in intelligent tutoring systems and game-based learning is personalized learning. In these environments, the computer analyzes learner behaviors in real time and builds individual models of critical aspects of learning (e.g., level of engagement, knowledge acquisition) to adapt the system. While these player models capture essential elements of a learner, the valuable information they contain is usually opaque and hidden from the learners. As a result, players of adaptive learning games do not know how the game categorizes them or why it changes. This then affects their learning, especially when learning complex concepts. 

Further, there has been a lot of work in the learning science community on the concept of Open Learner Models to help learners visualize and reflect on their learning. However, these models often do not represent the process of learning or problem-solving, as such a process is often contextual and hard to represent for many learners. 

This project aims to develop a novel approach to such open models, which we call Open Player and Community Model. The project will explore how students learn problem-solving with tools that allow them to reflect on their practices and how others solved similar problems. The project will develop a set of tools and assess their ability to facilitate metacognitive learning processes towards facilitating self regulated learning.