Reading comprehension is a complex, foundational construct for knowledge building, problem solving, and lifelong learning. Whereas the demand for in-depth reading comprehension has increased since the implementation of the Common Core State Standards and the rise in digital device use following the pandemic (e.g., digital reading, AI tools), many K-12 students, especially multilingual ones, continue to experience difficulties in developing reading comprehension. Thus, our research team investigates how (potential) malleable factors explain individual differences in print-based and digital reading comprehension. We examine a wide range of factors, such as executive functions, inference-making, vocabulary, reading motivation, and standards of coherence. The findings of this line of research guide us to design and test evidence-based interventions to support students’ print-based and digital reading comprehension in K-12 settings. Our team’s intervention-related work currently focuses on knowledge-building litealouds, online reading). Our team collaborates with school partners and disseminates research findings to both practitioners and researchers.

Language, thought, and learning

Language offers a glimpse into how people think. The words we choose, the examples we reach for, or even the pauses in our speech all reveal something about how we understand the world. Subtle patterns – how someone frames a problem, connects ideas, or uses metaphors – reflect their knowledge, their curiosities, and their beliefs. In this way, language is not just how we share our thoughts, it’s also part of how we form them. Our team studies how these traces of thinking appear in everyday language as people learn, reason, and create. We look at how students explain challenging ideas out loud or in writing, how people express curiosity and confidence when they encounter new concepts, and how moments of insight or confusion show up in their words. To study these patterns at scale, we combine close analysis of language with computational methods that can detect subtle shifts in meaning or structure across thousands of examples. This approach lets us ask interesting questions about how ideas take shape and change over time. Through this work, we explore what people’s words can tell us about how they think, how they learn, and how they come to see the world in new ways.

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.

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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.