Applying Rasch Measurement to Assess Knowledge-in-Use in Science Education
This study applied the many-facet Rasch measurement (MFRM) to assess students' knowledge-in-use in middle school physical science. 240 students completed three knowledge-in-use classroom assessment tasks on an online platform. We developed transformable scoring rubrics to score students’ responses, including a task-generic polytomous rubric (applicable to the three tasks), a task-specific polytomous rubric (for each task), and a task-specific dichotomous rubric (for each task). Three qualified raters scored 240 students’ responses to the three tasks. MFRM reported student ability, item difficulty, rater severity, and interaction effects, which helped improve the assessment tasks and rubrics.
Cite this article:
He, P., Zhai, X., Shin, N., & Krajcik, J. (2023). Using Rasch measurement to assess knowledge-in-use in science education. In Liu, X & Boone, W. Advances in Applications of Rasch Measurement in Science Education. Springer. https://doi.org/10.1007/978-3-031-28776-3_13
Developing three-dimensional learning progressions of energy, interaction, and matter at middle school level: A design-based research
Using a design-based research approach, this study put efforts into developing a 3DLP of matter, interaction, and energy at the middle school level and presents the essential design principles for developing 3DLPs. Our chapter offers an illuminating exploration of our design-based research, shedding light on the fundamental design principles and systematic processes involved in the development of 3DLPs. By identifying design principles and establishing a systematic process, we unlock new perspectives and approaches that will shape the future of designing and implementing 3DLPs to support student knowledge-in-use development.
Cite this article:
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 of Research in Science Learning Progressions, Routledge.
Integrating Artificial Intelligence into Learning Progression to Support Student Knowledge-in-Use: Opportunities and Challenges
In science education, a knowledge-in-use LP describes students’ development of core ideas and practices over time so that knowledge becomes more sophisticated, allowing learners to apply their knowledge in new and compelling situations. Researchers put efforts into developing and using LPs in learning systems to support student knowledge-in-use. With the rapid development of digital technologies, artificial intelligence (AI) can serve as a partner to provide potential solutions to support LP-based learning systems further. This chapter addresses this essential research agenda: 1) positioning theoretical perspectives and a conceptual model for integrating AI into LP in learning systems; 2) discussing our current work on integrating AI into knowledge-in-use LPs with automatic scoring information, personalized feedback, and instructional support in learning systems; and 3) envisioning the future research work with recommendations for AI-empowered LP-based learning systems.
Cite this article:
He, P., Shin, N., Kaldaras, L., & Krajcik, J. (in press). Integrating artificial intelligence into learning progression to support student knowledge-in-use: Opportunities and challenges. In Jin, H., Yan, D., & Krajcik, J. Handbook of Research in Science Learning Progressions, Routledge.
