Computer Science Seminar: Compassionate, Data-Driven Tutors for Problem Solving and Persistence
Please just the Computer Science department for an exciting seminar on Thursday, December 16, 12:30 – 1:30pm at Buttrick Hall 101 by Prof. Tiffany Barnes from North Carolina State University on Compassionate, Data-Driven Tutors for Problem Solving and Persistence.
Abstract: Determining how, when, and whether to provide personalized support is a well-known challenge called the assistance dilemma. A core problem in solving the assistance dilemma is the need to discover when students are unproductive so that the tutor can intervene. This is particularly challenging for open-ended domains, even those that are well-structured with defined principles and goals. In this talk, I will present a set of data-driven methods to classify, predict, and prevent unproductive problem-solving steps in the well-structured open-ended domains of logic and programming. Our approaches leverage and extend my work on the Hint Factory, a set of methods that to build data-driven intelligent tutor supports using prior student solution attempts. In logic, we devised a HelpNeed classification model that uses prior student data to determine when students are likely to be unproductive and need help learning optimal problem-solving strategies. In a controlled study, we found that students receiving proactive assistance on logic when we predicted HelpNeed were less likely to avoid hints during training, and produced significantly shorter, more optimal posttest solutions in less time. In a similar vein, we have devised a new data-driven method that uses student trace logs to identify struggling moments during a programming assignment and determine the appropriate time for an intervention. We validated our algorithm’s classification of struggling and progressing moments with experts rating whether they believe an intervention is needed for a sample of 20% of the dataset. The result shows that our automatic struggle detection method can accurately detect struggling students with less than 2 minutes of work with 77% accuracy. We further evaluated a sample of 86 struggling moments, finding 6 reasons that human tutors gave for intervention from missing key components to needing confirmation and next steps. This research provides insight into the when and why for programming interventions. Finally, we explore the potential of what supports data-driven tutors can provide, from progress tracking to worked examples and encouraging messages, and their importance for compassionately promoting persistence in problem solving.
Bio: Dr. Tiffany Barnes is a Distinguished Professor of Computer Science at North Carolina State University, and a Distinguished Member of the Association of Computing Machinery (ACM). Prof. Barnes is Founding Co-Director of the STARS Computing Corps, a Broadening Participation in Computing Alliance funded by the U.S.A. National Science Foundation. Her internationally recognized research program focuses on transforming education with AI-driven learning games and technologies, and research on equity and broadening participation. Her current research ranges from investigations of intelligent tutoring systems and teacher professional development to foundational work on educational data mining, computational models of interactive problem-solving, and design of computational thinking curricula. Her personalized learning technologies and broadening participation programs have impacted thousands of K-20 students throughout the United States.