Mingyu Feng

Overview

Mingyu Feng is a nationally recognized expert in development and research of education innovations with a focus on the impact, implementation, and cost of innovative computer technologies and platforms. As a Research Director in the Learning and Technology area, Feng has over 20 years of experience in research and has led many complex, multi-institutional projects where she has collaborated with experts from nonprofit organizations, universities, and commercial partners.

Feng serves as Principal Investigator or Project Director on several large-scale Institutes of Education Sciences (IES), Education and Innovation Research (EIR), and National Science Foundation (NSF) grants. These projects aim to develop transformative education interventions, leveraging the most advanced large language models (LLM) and generative artificial intelligence (AI) techniques, to use machine learning and multimodal analysis to scaffold science modeling, or to investigate the efficacy of digital math and science interventions in kindergarten, elementary, and middle schools. Previously, she has also led foundational research investigating student perseverance, persistence, and engagement in STEM learning and projects that established researcher-practitioner partnerships with charter schools.

Before joining WestEd, Feng was a Principal Research Scientist at the Center of Technology for Learning of SRI International. As a computer scientist by training, Feng is committed to using technology to help student learning and teacher instruction, especially in STEM, by providing a supportive, personalized experience. She is an expert in educational data mining and learning analytics and has extensive experience in analyzing usage data from different kinds of digital learning systems to understand student learning behaviors and patterns and using leveraging analytics to monitor implementation fidelity of learning interventions. She served as Program Committee co-chair of the International Conference on Educational Data Mining in 2023 and has collaborated with experts around the world.

Education

• PhD in computer science, Worcester Polytechnic Institute
• MS in computer science, Tianjin University 
• BS in computer science, Tianjin University  

Select Publications

Feng, M., Heffernan, N., Collins, K., Heffernan, C., & Murphy, R. F. (2023). Implementing and evaluating ASSISTments online math homework support at large scale over two years: Findings and lessons learned. In N. Wang, G. Rebolledo-Mendez, N. Matsuda, O. C. Santos, & V. Dimitrova (Eds.), Artificial intelligence in education. AIED 2023. Lecture notes in computer science (pp. 28–40). Springer. https://doi.org/10.1007/978-3-031-36272-9_3

Feng, M., Huang, C.-W., & Collins, K. (2023). Technology-based support shows promising long-term impact on math learning: Initial results from a randomized controlled trial in middle schools. WestEd. https://www.wested.org/resources/technology-based-support-shows-promising-long-term-impact-on-math-learning-2/

Murphy, R., Roschelle, J., Feng, M., & Mason, C. (2020). Investigating efficacy, moderators and mediators for an online mathematics homework intervention. Journal of Research on Educational Effectiveness, 13(2), 235–270. https://doi.org/10.1080/19345747.2019.1710885

Tate, E., Ibourk, A., McElhaney, K., & Feng, M. (2020). Middle school students’ mechanistic explanation about trait expression in rice plants during a technology-enhanced science inquiry investigation. Journal of Science Education and Technology, 29(5), 677–690. https://doi.org/10.1007/s10956-020-09846-4

Feng, M., Brenner, D., & Coulson, A. (2019). Using exploratory data analysis to support improvement of education technology product. In S. Isotani, E. Millán, A Ogan, P. Hastings, B. McLaren, & R. Luckin (Eds.), Artificial intelligence in education: Proceedings of 20th International Conference on Artificial Intelligence in Education (pp. 79–83). Chicago, IL, United States.

Shechtman, N., Roschelle, J., Feng, M., & Singleton, C. (2019). Efficacy of an integrated core digital curriculum for elementary school mathematics. AERA Open, 5(2). https://doi.org/10.1177/2332858419850482

Feng, M., Krumm, A., & Grover, S. (2018). Applying learning analytics to support instruction. In D. Zapata-Rivera (Ed.), Score reporting: Research and applications (pp.126-144). Routledge. 

Feng, M., Roschelle, J., Mason, C., & Bhanot, R. (2016). Investigating gender differences on homework in middle school mathematics. In T. Barnes, M. Chi, & M. Feng (Eds), Proceedings of the 9th International Conference on Educational Data Mining (pp. 364–369), Raleigh, NC, United States.

Krumm, A. E., Beattie, R., Takahashi, S., D’Angelo, C., Feng, M., & Cheng, B. (2016). Practical measurement and productive persistence: Strategies for using digital learning system data to drive improvement. Journal of Learning Analytics, 3(2), 116–138.

Roschelle, J., Feng, M., Murphy, R., & Mason, C. (2016). Online mathematics homework increases student achievement. AERA Open, 2(4). https://doi.org/10.1177/2332858416673968

Feng, M., Roschelle, J., Heffernan, N., Fairman, J., & Murphy, R. (2014). Implementation of an intelligent tutoring system for online homework support at large scale. In S. Trausan-Matu, K. Boyer, M. Crosby & K. Panourgia,  Proceedings of the 12th International Conference on Intelligent Tutoring Systems (pp. 561–566). 

Feng, M., Roschelle, R., Murphy, R., & Heffernan, N. (2014). Using analytics for improving implementation fidelity in a large-scale efficacy trial. In J. L. Polman, E. A. Kyza, D. K. O’Neill, I. Tabak, W. R. Penuel, A. S. Jurow, K. O’Connor, T. Lee, & L. D’Amico. (Eds.), Learning and becoming in practice: The International Conference of the Learning Sciences (ICLS) 2014 (Vol. 1, pp. 527–534). International Society of the Learning Sciences.

Roschelle, J., Feng, M., Gallagher, H., Murphy, R., Harris, C., Kamdar, D., & Trinidad, G. (2014). Recruiting participants for large-scale random assignment experiments in school settings. SRI International.

Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. U.S. Department of Education.

Feng, M., Heffernan, N. T., & Koedinger, K. R. (2010). Student modeling in an intelligent tutoring system. In S. Stankov, V. Glavinc, & M. Rosic (Eds.), Intelligent tutoring systems in e-learning environments: Design, implementation and evaluation (pp. 208–236). Information Science Reference.

Feng, M., Heffernan, N., Heffernan, C., & Mani, M. (2009). Using mixed-effects modeling to analyze different grain-sized skill models in an intelligent tutoring system. IEEE Transactions on Learning Technologies, 2(2), 79–92 (2009-2010 IEEE Computer Society Publications Sampler). https://doi.org/10.1109/TLT.2009.17

Feng, M., Heffernan, N. T., & Koedinger, K. R. (2009). Addressing the assessment challenge in an online system that tutors as it assesses. User Modeling and User-Adapted Interaction: The Journal of Personalization Research, 19(3), 243–266. (Winner of James Chen Award for the Best Paper of the Year)

Honors, Awards, and Affiliations

Board member and Treasurer of International Society of Educational Data Mining (2016–2022)

Elected Program Committee Co-Chair, the 9th and 16th International Conference on Educational Data Mining (2016, 2023)

Program Committee member: International Conferences on Artificial Intelligence in Education (AIED); International Conferences on Educational Data Mining (EDM), 2010–2023; International Conferences on Computer Supported Education (CSEDU)

Invited Panelist: 2016 NAEP Innovative Symposium on Use of Process Data, 2023 Washington Education Research Association Annual Conference Panel on Navigating the Future: Exploring the Pros and Cons of AI in K–12 Education; 2023 Loyola Marymount University Innovation Symposium on Disruptive Technologies and Education

Recent Media Appearances

PROOF POINTS: The value of one-size-fits-all math homework, The Hechinger Report, September 11, 2023

The Education System Isn’t Ready for Another Widespread Closure, Undark Magazine, July 12, 2023