Dr. Maestrales is an AI Strategist, Psychometrician, and Measurement Expert based in Santiago de Compostela, Spain. Sarah has a Ph.D. in Measurement & Quantitative Methods, a B.S. in Physics, and an A.A. in Psychology. Her broad educational background allows her to take on, and enjoy, diverse roles within projects that provide opportunities to apply her skills in new situations. Dr. Maestrales’ primary focus is the creative application of statistical models and data pipelines to create and optimize assessments, improve GenAI output, and reduce AI expenditures.
I have helped to design NGSS aligned science curriculum and assessments with the $5m CESE project-based learning intervention & CREATE4STEM Institute. I have lead investigations and published research on the applications of quantitative methods to ensure quality in classification algorithms and quantifying engagement in online learning environments. I have reviewed numerous learning platforms. I have ensured content coverage and standards alignment for the development of 11 Advanced Placement Courses in History, Science, and Social Science. And I have studied the use generative AI in multiple tasks related to the nearly 1000 topics across those 11 courses.
You can see my CV here.
Publications
- Maestrales, S., Zhai, X., Touitou, I., Baker, Q., Schneider, B., & Krajcik, J. (2021). Using machine learning to score multi-dimensional assessments of chemistry and physics. Journal of Science Education and Technology, 30(2), 239-254.
- Schneider, B., Krajcik, J., Lavonen, J., Salmela-Aro, K., Klager, C., Bradford, L., … & Bartz, K. (2022). Improving science achievement—Is it possible? Evaluating the efficacy of a high school chemistry and physics project-based learning intervention. Educational Researcher, 51(2), 109-121.
- Maestrales, S., Marias Dezendorf, R., Tang, X., Salmela‐Aro, K., Bartz, K., Juuti, K., … & Schneider, B. (2022). US and Finnish high school science engagement during the COVID‐19 pandemic. International Journal of Psychology, 57(1), 73-86.
- Frank, K., Lin, Q., Maroulis, S., Dai, S., Jess, N., Lin, H. C., … & Tait, J. (2022). Improving Oster’s δ*: Exact Calculation for the Coefficient of Proportionality Without Subjective Specification of a Baseline Model. Ellen and Tait, Jordan, Improving Oster’s δ*: Exact Calculation for the Coefficient of Proportionality Without Subjective Specification of a Baseline Model (December 16, 2022).
- Maestrales, S. (2024). Project-Based Science Learning Facilitated Through Technology. Michigan State University.
Conference Papers & Presentations
- Adapting Current Events into a Socially Relevant Curriculum. Sarah Maestrales, Emily Miller, Kayla Bartz, Rand Spiro. Presented at WERA Santiago de Compostela & South Africa 2021.
- Exploring for Racial and Ethnic Bias When Using Machine Learning to Score Multi-Dimensional Science Assessments. Sarah Maestrales, Xiaoming Zhai, Israel Touitou, Quinton Baker, Barbara Schneider, Joseph Krajcik. Presented at PIRE Helsinki 2021.
The other stuff: Adventure & Art
I love to travel and see new places as often as possible. I took a contract 2 years ago that would allow me to save enough money to take some time off for my travels and a few personal projects. You can follow along with my adventures here as I get caught up on the backlog and fill in the new ones.
NWD Consulting 