Graduate School of Education
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Associate Professor (Teaching) of Education
Current Research and Scholarly InterestsCIF21 DIBBs: Building a Scalable Infrastructure for Data-Driven Discovery and Innovation in Education: Funded by the National Science Foundation.
In collaboration with Carnegie Mellon, MIT, and the University of Memphis, we are creating a community software infrastructure, called LearnSphere, which supports sharing, analysis, and collaboration across a wide variety of educational data. LearnSphere supports researchers as they improve their understanding of human learning. It also helps course developers and instructors improve teaching and learning through data-driven course redesign.
The Learning Engineering Initiative: EdHub. Funded by the Chan Zuckerberg Initiative/Silicon Valley Community Foundation.
The EdHub project is a cross-sector initiative, to engineer the creation of a novel research and development hub in the Bay Area that is designed to integrate, by design, ongoing research in the Learning Sciences with ongoing approaches to enduring problems of practice within education.
Adaptable Learning Feedback for Instructors: The Open Analytics Research System (OARS). Funded by the Stanford VPTL Innovation Grant.
The activities and embedded assessments in online courseware provide support to students and generate fine-grained student learning data. The Open Analytic Research System (OARS) collects and models student learning data and and presents information to instructors in a dashboard to guide instruction and class activities.
Next Generation Courseware Challenge: A Partnership for Iterative Excellence in Online Courseware for College Learners. Funded by The Bill and Melinda Gates Foundation.
The OLI statistics courseware was created as an open educational resource (OER) on, the now proprietary, CMU OLI platform. In moving to Stanford, I moved the courseware to Lagunita, Stanford’s OpenEdx platform so that it would once again be an OER and extended the capabilities of the Lagunita platform to support the OLI statistics course. In collaboration with multiple partner institutions, we have continued to expand and update the courseware and conducted several learning studies. We have conducted studies in "Mindset" with Carol Dweck's (Stanford Psychology) PERTS group. In collaboration with Emma Brunskill (Stanford Computer Science), we are implementing an adaptive problem solver that uses Bayesian optimization algorithms to automatically identify which items to include in a practice set, and how to adaptively select these items in order to maximize student performance on the specified set of learning objectives and skills. Additional RCT studies that we are currently conducting in the OLI statistics courseware at our partner institutions include a study on the impact of prompting and scaffolding learners to make strategic choices about their use of course resources; and a separate study that builds affect detectors into the courseware to test the impact of timing interventions to the affective as well as cognitive state of the learner.