Libby Gerard, EdD, is a Research Scientist and Lecturer in the University of California, Berkeley Graduate School of Education. Her research examines how innovative learning technologies can capture student ideas and help teachers and principals use those ideas to make decisions about classroom instruction. Libby’s recent projects explore the use of automated scoring of student written essays using natural language processing to provide immediate guidance to students as they progress through an inquiry project, and, to support teachers’ customization of instruction both in real-time and between lessons. Libby designs and leads teacher and principal professional development by using student embedded assessment data and the knowledge integration framework to inform instructional customization and resource allocation. Libby’s research is published in leading peer-reviewed journals including Science and the Review of Educational Research.
Prior to being a Research Scientist and Lecturer, Libby was a postdoctoral scholar at UC Berkeley. She also taught preschool and elementary school in Oakland, California, and in Alessandria, Italy. In Italy, she taught in a Reggio-Emilia inspired elementary school that focused on developing mixed-media documentation to make students’ thinking visible. Students teachers and community members used the documentation to reflect on and adapt instructional practices to better respond to student’s interests and lines of reasoning. Libby continued this effort as a teacher in Oakland, incorporating novel uses of technology to create documentation. Prior to teaching, Libby worked in news production at the CNN Headquarters in Atlanta, Georgia.
Selected Refereed Journal Articles
Donnelly-Hermosillo, D., Gerard, L., & Linn, M.C. (2020) Impact of Graph Technologies in K-12 Science and Mathematics Education. Computers and Education, 146.
Gerard, L. Kidron, A., & Linn, M.C. (2019). Teacher guidance for collaborative revision of science explanations. International Journal of Computer Supported Collaborative Learning, 14(3), 291-324.
Linn, M.C., McBride, E., Gerard, L., & Kidron, A., (2019). For the future of education – Technology matters. In Focus, Annual magazine of the United Nations Educational, Scientific and Cultural Organization (UNESCO).
Applebaum, L. A., Vitale, J., Gerard, L.F., & Linn, M.C. (2017). Comparing design constraints to support learning in technology-guided inquiry projects. Educational Technology & Society, 20(4), 179-190.
Tansomboon, C., Gerard, L., Vitale, J., & Linn, M.C. (2017). Designing Automated Guidance to Promote Productive Revision of Science Explanations. International Journal of Artificial Intelligence in Education (IJAIED), 1-29.
Gerard, L., Matuk, C., & Linn, M.C. (2016). Technology as inquiry teaching partner (Special Issue Editorial). Journal of Science Teacher Education, 27(1), 1-9.
Gerard, L. & Linn, M.C. (2016). Using automated scores of student essays to support teacher guidance in classroom inquiry. Journal of Science Teacher Education, 27(1), 111-129.
Matuk, C., Gerard, L., Lim-Breitbart, J., & Linn, M.C. (2016). Gathering requirements for teacher tools: Strategies for empowering teachers through co-design. Journal of Science Teacher Education, 27(1), 79-110.
Liu, L., Rios, J., Heilman, M., Gerard, L., & Linn, M. (2016). Validation of automated scoring of science assessments. Journal of Research in Science Teaching, 53(2), 215-233.
Gerard, L., Ryoo, K., McElhaney, K., Liu, L., Rafferty, A., & Linn, M.C. (2015). Automated guidance for student inquiry. Journal of Educational Psychology, 108(1), 60-81.
Linn, M.C., Palmer, E., Baranger, A., Gerard, L., & Stone, E. (2015). Improving Undergraduate research experiences: What works? Science, 347(6222). 627-633.
Gerard, L., Matuk, C., McElhaney, K., Linn, M.C. (2015). Automated, adaptive guidance for K-12 education. Educational Research Review, 15, 41-58.
Linn, M.C., Gerard, L., Ryoo, K., McElhaney, K., Liu, L., & Rafferty, A., (2014). Computer-guided inquiry to improve science learning. Science, 344 (6180), 155-156.
Liu, O. L., Brew, C., Blackmore, J., Gerard, L. F., Madhok, J., & Linn, M. C. (2014). Automated Scoring in Inquiry Science Assessment: Application of c-rater. Educational Measurement: Issues and Practice, 33(2), 19-28.
Selected Conference Proceedings and Presentations
Gerard, L., Wiley, K., Bradford, A., King Chen, J., Breitbart, J., & Linn, M. C. (2020). Impact of a Teacher Action Planner that Captures Student Ideas on Instructional Customization Decisions. Paper accepted for presentation at the Annual Meeting of the International Conference of the Learning Sciences, Nashville, TN.
Riordan, B., Cahill, A., Chen, J. K., Wiley, K., Bradford, A., Gerard, L., & Linn, M. C. (2020). Identifying NGSS-Aligned Ideas in Student Science Explanations. Paper presented at the Thirty-Fourth AAAI Conference on Artificial Intelligence, New York, NY. Awarded Best Paper for the Artificial Intelligence for Education Workshop (AI4EDU)
Gerard, L., Bradford, A., Lim-Breitbart, J., Wiley, K., & Linn, M.C. (2019). How does a research-based instructional framework support teachers’ customization of web-based curriculum? Paper presented at the annual international conference of the National Association of Research in Science Teaching (NARST), Strand: Science Teaching, Middle and High School, Baltimore, MD. Paper selected for presentation in NARST Regional Teacher meeting
Gerard, L., Vitale, J., & Linn, M.C. (2017). Argument construction to drive inquiry. In Finlayson, O.E., McLoughlin, E., Erduran, S., & Childs, P. (Eds.), Electronic Proceedings of the ESERA 2017 Conference. Research, Practice and Collaboration in Science Education. Dublin, Ireland: Dublin City University
Gerard, L. F., Linn, M. C., & Madhok, J. J. (2016). Examining the Impacts of Annotation and Automated Guidance on Essay Revision and Science Learning. In C-K. Looi, J. Polman, U. Cress, & P. Reimann (Eds.), International Conference of the Learning Sciences (Vol. 1, pp. 394-401). Singapore: International Society of the Learning Sciences
Gerard, L., Vitale, J., & Linn, M.C. (2016). Combining automated scoring and teacher guidance to improve students’ science learning. Paper presented at the annual meeting of the American Education Research Association (AERA), Washington D.C.
Current Funded Research Projects
2020-2022: Co-Principal Investigator, Hewlett Foundation, Anti-Racism Interactive Science Education (ARISE)
2018-2022: Co-Principal Investigator, NSF Division of Research on Learning in Formal and Informal Settings, DRK-12, National Science Foundation, Supporting Teachers in Responsive Instruction for Developing Expertise in Science (STRIDES)
2018-2020: Co-Principal Investigator, Hewlett Foundation, Personalizing Open Web-Based Educational Resources: Evaluator and Designer (POWER)
2015-2020: Co-Principal Investigator, NSF Directorate for Computer & Information, Science & Engineering, Cyber Learning and Future Learning Technologies, Project Learning with Automated Networked Support (PLANS)
Research Methods for Science and Mathematics Teachers, Educ 122c, Spring
The goals of this course are to develop the knowledge and skills to utilize data to inform your practice as both a STEM disciplinary expert and a STEM educator. You will use this knowledge to conduct investigations in STEM and STEM education and formulate criteria to review and customize curriculum designed to support secondary school students in data-based STEM research projects.
Apprentice Teaching in Science and Mathematics, CalTeach UGIS 303, Fall and Spring
This course is designed to promote effective teaching methods for science and mathematics classrooms, including strategies for lesson planning, assessment, and English language learner support. The course supports student and intern teachers of secondary science and mathematics in undertaking an inquiry project on their own teaching practice and earning a credential for teaching in California secondary schools.