Learning is complex. Decades of research in the science of human learning have produced results that could transform higher education; however, research findings have not often made a positive impact on teaching practice, educational technology design, or student learning. My mission is to build learning environments and data systems that not only facilitate the transfer of knowledge from learning research into teaching practice, but also engage researchers, practitioners and learners in making progress on our fundamental understanding of human learning. The focus of my work is in applying the results from research in the science of learning to the design and evaluation of open web-based learning environments for college level courses, and in using those environments to conduct research in human learning.

Academic Appointments

Administrative Appointments

  • Co-Director, Stanford Lytics Lab (2018 - 2020)
  • Affiliate Faculty, Stanford Neurosciences Interdepartmental Program (2015 - Present)
  • Assistant Professor, Stanford Graduate School of Education (2013 - Present)
  • Director, Stanford Open Learning Initiative (2013 - 2018)

Boards, Advisory Committees, Professional Organizations

  • Faculty Director for Adult and Workforce, Stanford Accelerator for Learning (2023 - Present)
  • Trustee, Educational Testing Service (2019 - Present)
  • Advisory Council Member, California Education Learning Lab (2018 - Present)
  • Director, Learning Science, Amazon (2018 - 2023)
  • Advisory Board Member, The William E. Kirwan Center for Academic Innovation University System of Maryland (2018 - 2020)
  • Co-Editor, MIT Press Learning at Scale Book Series (2017 - 2018)
  • Steering Committee, Association for Computing Machinery Learning at Scale Conference (ACM L@S2018) (2016 - Present)
  • Program Committee Chair, Association for Computing Machinery Learning at Scale Conference (ACM L@S2017) (2016 - 2017)
  • Advisory Council Member, Advisory Council of NSF Education and Human Resources (2014 - 2018)
  • Board Member, American Association of Colleges and Universities (AAC&U) Board of Directors (2014 - 2018)
  • Member, Assessment 2020 Task Force of the American Board of Internal Medicine (2013 - 2016)
  • Advisory Council Member, Technical Advisory Council of the American Association of Universities STEM initiative (2012 - 2018)
  • Fellow, International Society for Design and Development in Education (2010 - Present)
  • Founding Director, Open Learning Initiative, Carnegie Mellon University (2002 - 2013)
  • Executive Vice President, West Coast Regional Managing Director, CFO, CIO, Consultant, Interaction Associates, LLC (1984 - 2002)

Professional Education

  • Ed.D., University of Pennsylvania, Higher Education (2013)
  • M.S., Carnegie Mellon University, Information Technology (2005)
  • B.A., University of California, Berkeley, Sociology (1980)

Research Interests

  • Assessment, Testing and Measurement
  • Brain and Learning Sciences
  • Data Sciences
  • Equity in Education
  • Higher Education
  • Lifelong Learning
  • Technology and Education

Current Research and Scholarly Interests

CIF21 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.

2023-24 Courses

Stanford Advisees

All Publications

  • Replicability of neural responses to speech accent is driven by study design and analytical parameters. Scientific reports Strauber, C. B., Ali, L. R., Fujioka, T., Thille, C., McCandliss, B. D. 2021; 11 (1): 4777


    Recent studies have reported evidence thatlisteners'brains processmeaning differently inspeech withan in-group as compared to anout-group accent. However, among studies that have used electroencephalography (EEG) to examine neural correlates of semantic processing of speech in different accents, the details of findings are often in conflict, potentially reflecting critical variations in experimental design and/or data analysis parameters. To determine which of these factors might be driving inconsistencies in results across studies, we systematically investigate how analysis parameter sets from several of these studies impact results obtained from our own EEG data set. Data were collected from forty-nine monolingual North American English listeners in an event-related potential (ERP) paradigm as they listened to semantically congruent and incongruent sentences spoken in an American accent and an Indian accent. Several key effects of in-group as compared to out-group accent were robust across the range of parameters found in the literature, including more negative scalp-wide responses to incongruence in the N400 range, more positive posterior responses to congruence in the N400 range, and more positive posterior responses to incongruence in the P600 range. These findings, however, are not fully consistent with the reported observations of the studies whose parameters we used, indicatingvariation in experimental design may be at play. Other reported effects only emerged under a subset of the analytical parameters tested, suggesting that analytical parameters also drive differences. We hope this spurs discussion of analytical parameters and investigation of the contributions of individual study design variables in this growing field.

    View details for DOI 10.1038/s41598-021-82782-4

    View details for PubMedID 33637784

  • Reinforcement Learning for the Adaptive Scheduling of Educational Activities Bassen, J., Balaji, B., Schaarschmidt, M., Thille, C., Painter, J., Zimmaro, D., Gamest, A., Fast, E., Mitchell, J. C., ACM ASSOC COMPUTING MACHINERY. 2020
  • The heart of educational data infrastructures-Conscious humanity and scientific responsibility, not infinite data and limitless experimentation BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY Johanes, P., Thille, C. 2019

    View details for DOI 10.1111/bjet.12862

    View details for Web of Science ID 000479642500001

  • OARS: Exploring Instructor Analytics for Online Learning Bassen, J., Howley, I., Fast, E., Mitchell, J., Thille, C., ACM ASSOC COMPUTING MACHINERY. 2018
  • Exploring the Impact of the Default Option on Student Engagement and Performance in a Statistics MOOC Brunskill, E., Zimmaro, D., Thille, C., ACM ASSOC COMPUTING MACHINERY. 2018
  • Incorporating Learning Analytics in the Classroom New Directions for Higher Education Thille, C., Zimmaro, D. 2017; 2017 (179): 19-31
  • Community Based Educational Data Repositories and Analysis Tools Koedinger, K., Liu, R., Stamper, J., Thille, C., Pavlik, P., ACM ASSOC COMPUTING MACHINERY. 2017: 524-525
  • The Future of Data-Enriched Assessment. Research & Practice in Assessment Thille, C., Schneider, E., Kizilcec, R. F., Piech, C., Halawa, S. A., Greene, D. K. 2014; 9: 5-16
  • MOOCs and Technology to Advance Learning and Learning Research Opening Statement: MOOCs and technology to advance learning and learning research (Ubiquity symposium) Ubiquity Thille, C. 2014; 2014 (April): 1
  • Open Learning Initiative courses in community colleges: Evidence on use and effectiveness Mellon University, Pittsburgh, PA. Available online: http://www. hewlett. org/sites/default/files/CCOLI_Rep ort_Final_1. pdf Kaufman, J., Ryan, R., Thille, C., Bier, N. 2013
  • The open learning initiative: Enacting instruction online Game Changers: Education and Information Technologies Strader, R., Thille, C. 2012: 201-213
  • Changing the production function in higher education Making Productivity Real. American Council on Education Thille, C. 2012
  • Technology: Conducive and disruptive roles in improving student success and college completion 21 st-‐CENTURY COMMISSION On the Future of Community Colleges Thille, C. 2012: 82
  • Cold Rolled Steel and Knowledge: What Can Higher Education Learn About Productivity? Change: The Magazine of Higher Learning Thille, C., Smith, J. 2011; 43 (2): 21-27