I am a PhD candidate in Learning Sciences and Technology Design at Stanford Graduate School of Education, advised by Jason Yeatman, Nick Haber, and Ben Domingue. I am broadly interested in psychometrics, reading, and human-centered AI, with a specific emphasis on their applications in improving the future of special education.

In 2023, I was awarded as the Stanford Interdisciplinary Graduate Fellow. My current research, as part of Rapid Online Assessment of Reading, aims to create advanced dyslexia screening and monitoring tools. In one aspect of my work, I develop an open-source library, jsCAT, enabling real-time, browser-based computerized adaptive testing for broad application in behavioral research. At the same time, I explore the potential of leveraging generative AI, assessment data, and human expertise to scale and diversify the item bank of reading assessments. This involves employing psychometric methods to evaluate the relevance, difficulty, and reliability of the generated items, using the evaluation results to provide feedback for improved item generation. This innovation will provide teachers with a more efficient means of identifying and supporting struggling readers, further the research on dyslexia, and lead to positive policy changes.

Prior to Stanford, I was a middle school Chemistry teacher in Brooklyn, NY. My previous research focused on science education and learning analytics under the guidance of Susan Kirch, Camillia Matuk, and Ryan Baker.

My current CV is available for download here.

Selected Publications

Note: *These authors contributed equally to this work

Ma, W. A., Richie-Halford, A., Burkhardt, A., Kanopka K., Chou, C., Domingue, B., Yeatman, J. D. (2023, September). ROAR-CAT: Rapid Online Assessment of Reading ability through computerized adaptive testing. Preprint

Zelikman, E.*, Ma, W. A. *, Tran, J. E., Yang, D., Yeatman, J. D., Haber, N. (2023). Generating and Evaluating Tests for K-12 Students with Language Model Simulations: A Case Study on Sentence Reading Efficiency. 2023 Conference on Empirical Methods in Natural Language Processing, EMNLP. PDF

Matuk, C., Ma, W., Sharma, G., Linn, M. C. (2019). The Lifespan and impact of students’ ideas shared during classroom science inquiry. In K. Lund, G. P. Niccolai, E. Lavoué, C. E. Hmelo-Silver, G. Gweon, M. Baker (Eds.), A Wide Lens: Combining Embodied, Enactive, Extended, and Embedded Learning in Collaborative Settings, 13th International Conference on Computer Supported Collaborative Learning (CSCL) 2019 (Vol. 1, pp. 49-56). Lyon, France: International Society of the Learning Sciences. PDF Best Paper Nomination

Ma, W. (2017). A computer tool that will allow secondary science teachers to differentiate reading materials for students with varied reading abilities. In M. J. Mohr-Schroeder J. N. Thomas (Eds.), Proceedings of the 116th Annual Convention of the School Science and Mathematics Association (Vol. 4, pp. 14-21).PDF