Playful problem solving

Play is an exceptionally generative context for learning. Play is also a context where you push your boundaries and get stuck. In these moments, players often experience impasses and the need to problem solve as fundamental, even essential parts of the play process. Because learning to problem-solve requires working through impasses, there are substantial benefits that play can provide when it comes to lowering the cost of failure and fostering exploration and iteration. Building on these threads, our team of researchers is studying the genre of puzzle games, in particular Baba Is You, through video-based research methods (e.g., players thinking aloud while puzzle solving) and multimodal learning analytics (e.g., studying patterns in how often players restart levels, bump into walls). We are exploring how players solve problems at impasses, express confidence during play, learn the game through different types of onboarding, and receive scaffolding from experts when they get stuck. For a summary of some of these findings, check out this recording of a research talk we gave at ThinkyCon in 2024. In upcoming threads of this work, we are exploring how different types of onboarding – easier initial levels, levels with good hints, hard initial levels – impact players’ long-term success in the game. We are also interviewing a wide range of developers who build puzzle-based video games and aiming to understand the creative, effective ways they design their games to support players’ learning.

Knowledge and misinformation revision

Digital spaces are exceptionally fertile ground for misinformation spread. They are also contexts where people encounter conflicting information and must navigate complex credibility judgments. In these moments, individuals often need to revise their knowledge. Because learning to discern accurate information requires working through misconceptions, there are substantial benefits that effective correction strategies can provide when it comes to lowering the psychological costs of belief revision and fostering critical evaluation of information sources. Building on these threads, our team is studying knowledge revision processes, particularly through the lens of the Knowledge Revision Components (KReC) framework, using both controlled experimental methods (e.g., measuring misconception reactivation during reading) and applied intervention research (e.g., studying patterns in how people respond to corrections on social media). We are exploring how people revise misconceptions when encountering refutation texts, process corrections from different credibility sources, integrate information across multiple corrective messages, and maintain revised knowledge over time. We are also exploring how AI-mediated interactions can influence knowledge revision and learning.