I’m a PhD candidate and data scientist at the University of Minnesota’s Learning Informatics Lab, where I apply computational and qualitative methods to explore questions about human learning and effective instructional design.

Specifically, (1) how people set, shift, and achieve their goals; (2) how individuals get stuck / unstuck; (3) the influence of digital environments -- their design and informational content -- on goals and behavior; and (4) the learning processes that occur along the way.

I explore these questions in various contexts — software, games, schools — with a mission to develop tools and interventions that enhance learning outcomes and augment goal achievement for everyone. My prior work has involved analyzing goal dynamics using unsupervised learning techniques, applying sequence mining to understand emergent behaviors during problem-solving activities, and modeling the ways students engage with smartphones when learning.

My research and collaborations have been published in ISLS, LAK, ICQE, and other venues. For more context, please visit my GitHub or download my CV.

With my work, I aim to contribute to the development of more goal-sensitive AI systems and interfaces, that can meet users where they are, and dynamically assist in calibrating complex goals in various contexts.

Research Interests: Human-Computer Interaction, Human-Centered Data Science, Instructional Design, Human-AI Partnership, Generative AI, Goal Setting, Problem Solving, Measurement

Selected Projects

  • Designing Tutorials for Learning

    Designing Tutorials for Learning

    Comparing different design approaches for facilitating learning of key concepts early in a learning game.

  • Productive Discrepancies

    Layering impasses over time can deepen understanding of complex game-mechanics.

  • Picking Your Goal

    Applying clustering analysis to predict how students set goals in open-ended contexts.

  • Shifts in Goal Setting

    Exploring how students change how they set and calibrate goals based on their environment.

  • Learning from Impasses

    Studying the interplay between failure and learning in the context of Baba Is You.

  • Smartphone Use in Classrooms

    Modeling student smartphone use during school-hours.

  • Playful Problem Solving

    Sequence mining patterns in problem-solving behavior using log data, and interaction analysis.

  • iLearn

    iLearn is an interface for supporting students in decomposing complex goals, monitoring progress, and recommending relevant content to help students complete complex learning tasks.

  • Overnight Adolescent Smartphone Use

    Observing overnight adolescent smartphone use using high-intensity objective observations.

  • Text-Network Analysis

    Conduct research on the promise of natural-language processing techniques in supporting student knowledge construction.

  • Sentiment Analysis of Tweets During COVID

    Topic-modeling using Tweets extracted before and after shift to online learning during COVID pandemic.

  • Network Dynamics of Student's Online Annotations

    Modeling student interactions in an online course based on student attributes, course-design, and network factors.