- Services & Solutions
- Clients & Partners
Center Director, SRI Education
Marie Bienkowski, Ph.D., directs the Center for Education Research & Innovation. She has more than three decades of experience at SRI in educational technology research, education project and program evaluation, and artificial intelligence software design and development. Bienkowski’s current research interest is in computer science (CS) education and student learning assessment in high school and middle school. She directs projects on learning analytics to study novice programmers in instrumented, block-based programming environments; on studying implementation of an equity-focused high school CS curriculum and its effects on student learning; and using online communities of practice to support high school computer science teachers, including with online assessments. She also consults on evaluations of game-based CS initiatives and is advising on the selection and most effective use of software that can customize digital instruction to improve results for students with disabilities.
Bienkowski has developed a broad policy-level perspective on education research through her participation on National Science Foundation (NSF) program evaluations. For example, she was the project director for SRI’s evaluation of the NSF Innovative Technology Experiences for Students and Teachers (ITEST) program. For the Office of Educational Technology at the U.S. Department of Education, Bienkowski has prepared policy briefs for diverse audiences, such as the Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics brief released in 2012 and the Ed Tech Developer's Guide: A Primer for Software Developers, Startups, and Entrepreneurs brief released in 2015. She was a coauthor on the research brief on Using Technology to Personalize Learning in K–12 Schools. For the U.S. Department of Education’s National Institute on Disability and Rehabilitation Research, Bienkowski investigated the utility of the Learning Registry, a service to provide access to accessibility metadata information and, by extension, accessible resources. She recently completed research on the effectiveness of digital technology that can provide online or blended learning approaches to basic education for low-skilled adults.
Bienkowski is co-principal investigator on several NSF computer science and information technology grants: design of an assessment framework for computational thinking and assessments for the Exploring Computer Science curriculum, study of implementation factors in secondary computer science teaching, study of analytics for insight into programming process in blocks-based environments, online community of practice activities for computer science teachers, and afterschool curriculum for middle school girls.
She lead a team on an assessment software platform deployment, including secure server and databases, interactive item development with backend analytics, and integration of cloud-based microservices, including automated text scoring.
Bienkowski is a regular reviewer for the AERA Division C section on Engineering and Computer Science, for the Learning Analytics & Knowledge annual conference, and for the annual conference on Innovation and Technology in Computer Science Education (ITiCSE). She is an associate program chair for the annual meeting of the Association for Computing Machinery’s Special Interest Group on Computer Science Education (SIGCSE). She regularly reviews for computer science education-related journals (e.g., ACM’s Transactions on Computing Education) and ACM InRoads, and she was on the editorial board for Teachers College Record.
Before joining SRI Education, Bienkowski was a senior computer scientist and, later, director of SRI's Applied Artificial Intelligence Technology Program.
Bienkowski has a B.S. in computer science and psychology from Wayne State University and an M.S. and Ph.D. in computer science from the University of Connecticut.
- Krumm, A., Means, B., & Bienkowski, M. (2018). Learning analytics goes to school: A collaborative approach to improving education. New York, NY: Routledge
- Bienkowski, M. A. (2018). Putting the learner at the center: Sharing analytics with learning participants. In D. Niemi, R. D. Pea, B. Saxberg, & R. E. Clark (Eds.), Learning analytics in education (pp. 113–137). Charlotte, NC Information Age.
- Grover, S., Basu, S., Bienkowski, M., Eagle, M., Diana, N., & Stamper, J. (2017). Toward a framework for using hypothesis-driven approaches to support data-driven learning analytics in measuring computational thinking in block-based programming environments. ACM Transactions on Computing Education, special issue on Learning Analytics in Computing Education, 17(3). doi:10.1145/3105910
- Snow, E., Rutstein, D., Bienkowski, M., & Basu, S. (in press). Leveraging evidence-centered design to develop assessments of computational thinking practices. International Journal of Testing, Special Issue on Challenges and Opportunities in the Design of Next-Generation Assessments of 21st Century Skills in Evidence-Centered Design.
- Snow, E., Rutstein, D., Bienkowski, M., & Xu, Y. (2017). Principled Assessment of Student Learning in High School Computer Science. Proceedings of the 2017 ACM Conference on International Computing Education Research (pp. 209–216). Tacoma, WA.