Computation is now considered the third pillar of science and engineering disciplines, alongside theory and experimentation. Computing knowledge and skills provide the foundation for modern competency in a multitude of STEM (Science, Technology, Engineering, and Math)-related disciplines, prompting research in CS+X on how to best prepare students for the 21st century workforce and for lifelong learning. The Next Generation Science Standards (NGSS) have reinforced the importance of model-based STEM instruction as a means of engaging students in more authentic STEM practices. Technology-enhanced models can be used as a productive avenue for engaging students in computational model building, and then using these executable models to solve problems in the particular STEM domain. However, building computational models of scientific phenomena is a multifaceted process that requires a good grasp of STEM domain and CT knowledge, as well as higher-order thinking skills like abstraction and decomposition. Past research has shown that students face significant difficulties in the translation of their STEM knowledge into a computational representation.

My research targets a deeper understanding of how to address these difficulties through key technology and curricular scaffolds with the goal of integrating computation into K-12 STEM classrooms. To do so, we implement four key design principles, including: (1) evidence-centered assessment and curriculum design, (2) a domain-specific modeling language (DSML) implemented in a visual programming environment, (3) exploratory learning of dynamic processes, and (4) embedded (formative) and preparation for future learning (PFL) assessments to support and analyze student learning. In addition, I utilize data mining techniques such as differential sequence mining to better understand student learning strategies and methods for providing both the student and teacher feedback for improved learning.

Relevant Publications

1. Hutchins, N.M., Biswas, G., Zhang, N., Snyder, C., Ledeczi, A., & Maroti, M. (in review). Domain-Specific Modeling Languages in Computer-Based Learning Environments: A Systematic Approach to Scaffold Science Learning through Computational Modeling. International Journal of Artificial Intelligence in Education.
2. Hutchins, N.M., Biswas, G., Maroti, M., Ledeczi, A., Grover, S., Wolf, R., Blair, K.P., Chin, D., Conlin, L., Basu, S., and McElhaney, K. (2019). C2STEM: A System for Synergistic Learning of Physics and Computational Thinking. Journal of Science Education and Technology.
3. Hutchins, N., Biswas, G., Wolf, R., Chin, D., Grover, S., & Blair, K. (2020; in press). Computational thinking in support of learning and transfer. In Proceedings of the International Conference of the Learning Sciences (ICLS), Nashville, TN, USA.

Experience