Peng He
Scholarly Assistant Professor (Career Track)
Science Education
WSU Pullman
Cleveland 363
509-335-7064
Peng He_Bios
Research Interests
My ultimate research endeavor is to create student-centered, evidence-based, and equitable learning environments (i.e., curriculum, instruction, assessment, and teacher professional learning) with innovative technologies and attention to students’ diverse backgrounds, interests, and affective experiences to foster deep learning in K-12 critical STEM education. I particularly have expertise in designing and testing curriculum and assessment, science measurement using advanced statistical methods, adaptive learning progression, and AI enhanced assessments for teacher instructional decisions.
1) I am passionate about designing, implementing, and testing innovative learning environments for science teachers and students using design-based research and advanced quantitative methods. I created a design framework to develop standards-aligned curriculum, instruction, and assessment materials and conducted experimental research design to test the effectiveness of the coherent learning system to prompt students’ science learning. As the PI, I have led an NSF-funded project-Three-dimensional Learning Progressions (3DLPs, Award ID: 2446701; 2201068) to guide teacher adaptation of their local curriculum materials and track student science learning with multiple learning pathways over time.
2) I developed my advanced statistics expertise when I was a Joint PhD student at University at Buffalo. I extended this to data science methods from NSF funded professional workshops (e.g., Learning Analytics) at NC State University. In my research work, I have applied advanced quantitative methods (e.g., Cognitive Diagnose Models, Factor Analysis, Hierarchical Linear Modeling, Rasch Measurement) to develop valid and reliable instruments for assessing student conceptual understanding, teacher pedagogical content knowledge and self-efficacy of teaching and investigate student knowledge-in-use development in secondary science education.
3) I leverage state-of-the-art technologies (e.g., machine learning and GenAI) to enhance teacher practices and student learning in science education. I used supervised machine learning approach (e.g., natural language processing, convolutional neural network) to develop algorithms for automatically scoring students written and drawn responses on performance-based assessments. To facilitate teachers use of automatic assessment information, I further developed instructional strategies to support teachers’ timely instructional decisions.
Education
| 2014-2016 | Joint Doctoral Student Learning Abroad Program, Department of Learning and Instruction, University at Buffalo, State University of New York |
| 2012-2016 | Ph.D., Curriculum and Instruction (in Chemistry), Northeast Normal University |
| 2010-2012 | M.Ed., Curriculum and Instruction (in Chemistry), Northeast Normal University |
| 2006-2010 | B.S., Chemistry, Northeast Normal University |
Professional Positions
| 2024-present | Scholarly Assistant Professor, Department of Teaching and Learning Washington State University, Pullman, WA |
| 2023-2024 | Research Assistant Professor, Counseling, Educational Psychology & Special Education Michigan State University, East Lansing, MI |
| 2019-2023 | Post-doc Research Associate & Visiting Professor, CREATE for STEM Institute Michigan State University, East Lansing, MI |
| 2016-2022 | Assistant Professor, Institute of Chemical Education, College of Chemistry Northeast Normal University, Changchun, CN |
Teaching
TCH&LRN584 Research on Teaching Math & Science (2024 Fall)
Selected Accomplishments
Peer-Reviewed Journal Articles
| # Graduate student co-author; + Collaborate teacher co-author; * Corresponding author; ^ equal contribution 16. #Chi, M., Zheng, C., & He, P. (2024). Assessing high school students’ chemical thinking: An instrument development and validation study. Chemistry Education Research and Practices. https://doi.org/10.1039/D4RP00106K. 15. Liu, R., # Liu, C., & * He, P. (2024). Chinese grades 1-9 students’ views of the Nature of Science: Do they differ by grade level, gender and parents’ occupation? Science & Education. 1-27. https://doi.org/10.1007/s11191-024-00519-x 14. Li, T., * He, P., & #Peng, L. (2024). Measuring high school student engagement in science learning: an adaptation and validation study. International Journal of Science Education, 46(6), 524-547. https://doi.org/10.1080/09500693.2023.2248668 13. * He, P., Krajcik, J. & Schneider, B. (2023). Transforming standards into classrooms for knowledge-in-use: An effective and coherent project-based learning system. In Special Issue “Science Education Policy, Standards, and Teaching Materials”. Disciplinary and Interdisciplinary Science Education Research. 5 (22): 1-23. https://doi.org/10.1186/s43031-023-00088-z 12. Huang, M. & * He, P. (2023). Pre-service science teachers’ understanding of socio-scientific issues instruction through a co-design and co-teaching approach amidst the COVID-19 pandemic. In Special Issue: Sustainability and Citizenship: Integration of Socio-Scientific Issues in Science Education. Sustainability. 15(10), 8211. https://doi.org/10.3390/su15108211 11. Li, T., Reigh, E., ^ He, P., & Adah Miller, E. (2023). Can we and should we use artificial intelligence for formative assessment in science? Journal of Research in Science Teaching,60 (6):1385–1389. https://doi.org/10.1002/tea.21867 10. #Chi, M., Zheng, C., & He, P. (2023).Reframing chemical thinking using the lens of disciplinary essential questions and perspectives. Science & Education. 1-26, https://doi.org/10.1007/s11191-023-00438-3 9.* He, P., Chen, I.-C., Touitou, I., Bartz, K., Schneider, B., & Krajcik, J. (2023). Predicting student science achievement using post-unit assessment performances in a coherent high school chemistry project-based learning system. Journal of Research in Science Teaching,60(4), 724- 760. https://doi.org/10.1002/tea.21815 8. Zhai, X., He, P., & Krajcik, J. (2022). Applying machine learning to automatically assess scientific models. Journal of Research in Science Teaching, 59(10), 1765–1794. https://doi.org/10.1002/tea.21773. 7.* He, P., Zheng, C., & Li, T. (2022). Development and validation of an instrument for measuring Chinese chemistry teachers’ perceived self-efficacy towards chemistry core competencies. International Journal of Science and Mathematics Education. 20(7),1337-1359. https://doi.org/10.1007/s10763-021-10216-8 6. * He, P., Zheng, C., & Li, T. (2022). High school students’ conceptions of chemical equilibrium in aqueous solutions: Development and validation of a two-tier diagnostic instrument. Journal of Baltic Science Education. 21(3), 428-444. https://doi.org/10.33225/jbse/22.21.428 5.* He, P., Zheng, C., & Li, T. (2021). Development and validation of an instrument for measuring Chinese chemistry teachers’ perceptions of pedagogical content knowledge for teaching chemistry core competencies. Chemistry Education Research and Practice, 22(2), 513-531. https://doi.org/10.1039/C9RP00286C 4. Zheng, C., #Li, L., & He, P. (2019). The development, validation, and interpretation of a content coding map for analyzing chemistry lessons in Chinese secondary schools. Chemistry Education Research and Practice, 20, 246-257., http://dx.doi.org/ 10.1039/C8RP00085A 3. Yang, Y., He, P., & Liu, X. (2018). Validation of an instrument for measuring students’ understanding of interdisciplinary science in grades 4-8 over multiple semesters: a Rasch measurement study. International Journal of Science and Mathematics Education, 16 (4), 639-654. https://doi.org/10.1007/s10763-017-9805-7 2. He, P., Liu, X., Zheng, C. & #Jia, M. (2016). Using Rasch measurement to validate an instrument for measuring the quality of classroom teaching in secondary chemistry lessons. Chemistry Education Research and Practice, 17, 381-393. http://dx.doi.org/10.1039/C6RP00004E 1. Zheng, C., Fu, L., & He, P. (2014). Development of an instrument for assessing the effectiveness of chemistry classroom teaching. Journal of Science Education and Technology, 23(2), 267-279. https://doi.org/10.1007/s10956-013-9459-3 |
Book Chapters & Proceedings
| 6. He, P., Shin, N., & Krajcik, J. (2024). Developing three-dimensional learning progressions of energy, interaction, and matter at middle school level: A design-based research. In Jin, H., Yan, D., & Krajcik, J. Handbook of Research in Science Learning Progressions. DOI: 10.4324/9781003170785-14. 5. He, P.Shin, N. Kaldaras L., & Krajcik, J. (2024). Integrating artificial intelligence into learning progression-based learning systems to support student knowledge-in-use: Opportunities and challenges. In Jin, H., Yan, D., & Krajcik, J. Handbook of Research in Science Learning Progressions. DOI: 10.4324/9781003170785-31. 4. He, P., Shin, N., Zhai, X., & Krajcik, J. (in press). A design framework for integrating artificial intelligence to support teachers’ timely use of knowledge-in-use assessments. In Zhai, X & Krajcik, J. Uses of Artificial Intelligence in STEM Education. Oxford University Press. 3. He, P., Zhai, X., Shin, N., Krajcik, J. (2023). Applying Rasch measurement to assess knowledge-in-use in science education. In: Liu, X., Boone, W.J. (eds) Advances in Applications of Rasch Measurement in Science Education. Contemporary Trends and Issues in Science Education, vol 57. Springer, Cham. https://doi.org/10.1007/978-3-031-28776-3_13. 2. Li, T., Miller, E. A., & He, P. (2024). Culturally and linguistically “Blind” or Biased? Challenges for AI Assessment of Models with Multiple Language Students. In Lindgren, R., Asino, T. I., Kyza, E. A., Looi, C. K., Keifert, D. T., & Suárez, E. (Eds.), Proceedings of the 18th International Conference of the Learning Sciences – ICLS 2024 (pp. 1323-1326). International Society of the Learning Sciences. https://doi.org/10.22318/icls2024.806499. 1. Zeng, M., He, P., Shin, N., & Krajcik, J. (2023). Characterizing students’ performances for interactive instructional decisions making to meet individual needs. In Blikstein, P., Van Aalst, J., Kizito, R., & Brennan, K. (Eds.), Proceedings of the 17th International Conference of the Learning Sciences – ICLS 2023 (pp. 1913-1914). International Society of the Learning Sciences. https://doi.org/10.22318/icls2023.267425. |
Grants and Awards
Grants
| 2022-2025 | PI, Developing and Testing a Learning Progression for Middle School Physical Science incorporating Disciplinary Core Ideas, Science and Engineering Practices, and Crosscutting Concepts. Award ID: 2201068 & 2446701, National Science Foundation (DRK-12), $449,960 |
Awards and Fellowships
| 2024 | Early Career Institute Scholar, NARST |
| 2022-2023 | Learning Analytics in STEM Education Research (LASER) Scholar, NC State University, NSF ECR: BCSER |
Service
Professional Organization Service
| 2024-2027 | Inaugural Council Committee, Chinese Academy for Science Education Research |
| 2023-2025 | Conference Program Co-Chair- Strand 10: Curriculum and Assessment, NARST Conference |
| 2023-2025 | Treasurer, NARST RIG: Asian and Pacific Islander Science Education Research (APISER) |
Editorial Board Member
| 2024-2025 | Special Issue Co-Editor, “Opportunities and Challenges of Using AI in Science Education”, Disciplinary and Interdisciplinary Science Education Research |
| 2024- present | Disciplinary and Interdisciplinary Science Education Research |
| 2024- present | International Journal of Science Education |
| 2023- present | Frontier in Education, STEM Education |
| 2022-2025 | Journal of Research in Science Teaching |
| 2021-2025 | Journal of Science Teacher Education |
University Service
| 2024-present | Member, Math and Science PhD Committee, Washington State University |