Peng He is a Fixed-term assistant professor in the Department of Counseling, Educational Psychology, and Special Education at Michigan State University. His research focuses on developing and testing science learning systems consisting of curriculum, assessment, instruction, and professional learning with innovative technologies (e.g., AI). Before his work at MSU, He was an assistant professor of chemistry education at Northeast Normal University in China.
Current Projects
He is the PI of an NSF-funded project (Award ID: 2201068): Developing and Testing a Learning Progression for Middle School Physical Science incorporating Disciplinary Core Ideas, Science and Engineering Practices, and Crosscutting Concepts. (visit the project website: 3dlp.org).
Publications
Peer-reviewed journal articles
Li, T., He, P., & Peng, L. (2023). Measuring high school student engagement in science learning: An adaptation and validation study. International Journal of Science Education. 1-24. DOI: 10.1080/09500693.2023.2248668 [SSCI, IF = 2.3]
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. [SSCI, IF = 3.9]
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,1–5. https://doi.org/10.1002/tea.21867. [SSCI, IF = 4.6]
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. [SSCI, IF = 2.8]
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 [SSCI, IF = 4.6]
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.[SSCI, IF = 4.6]
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. [SSCI, IF = 2.2]
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. [SSCI, IF = 1.2]
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. [SSCI, IF = 3.0]
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. [SSCI, IF = 3.0]
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. [SSCI, IF = 2.2]
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. [SSCI, IF = 3.0]
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. [SSCI, IF = 4.0]
Peer-reviewed book chapters
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.
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.
He, P., Shin, N., & Krajcik, J. (in press). 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 for Science Learning Progression Research.
He, P. Shin, N. Kaldaras L., & Krajcik, J. (in press). 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 for Science Learning Progression Research.