Brian Riordan

Citations of DRK-12 or Related Work (DRK-12 work is denoted by *)
  • Riordan, B., Wiley, K., Chen, J. K., Bradford, A., Mulholland, M., & Gerard, L. (2020). Automated scoring of science explanations for multiple NGSS dimensions and knowledge integration. Annual Meeting of the American Educational Research Association (AERA).*
  • Riordan, B., Cahill, A., Chen, J. K., Wiley, K., Bradford, A., Gerard, L., & Linn, M. C. (2020). Identifying NGSS-Aligned Ideas in Student Science Explanations. Workshop on Artificial Intelligence for Education (AI4EDU@AAAI).*
  • Burstein, J., Riordan, B., & McCaffrey, D. (2020). Expanding Automated Writing Evaluation to Serve Broader Education Needs. In Duanli Yan, André A. Rupp, & Peter W. Foltz (Eds.), Handbook of Automated Scoring: Theory into Practice. Boca Raton: Chapman and Hall/CRC.
  • Steimel, K., & Riordan, B. (2020). Towards Instance-Based Content Scoring with Pre-Trained Transformer Models. Workshop on Artificial Intelligence for Education (AI4EDU@AAAI).
  • Riordan, B., Flor, M., & Pugh, R. (2019). How to account for mispellings: Quantifying the benefit of character representations in neural content scoring models. Proceedings of the 14th Workshop on Innovative Use of NLP for Building Educational Applications (BEA@ACL).
Educational Testing Service (ETS)

This project takes advantage of advanced technologies to support science teachers to rapidly respond to diverse student ideas in their classrooms. Students will use web-based curriculum units to engage with models, simulations, and virtual experiments to write multiple explanations for standards-based science topics. The project will also design planning tools for teachers that will make suggestions relevant research-proven instructional strategies based on the real-time analysis of student responses.

Educational Testing Service (ETS)

This project takes advantage of language to help students form their own ideas and pursue deeper understanding in the science classroom. The project will conduct a comprehensive research program to develop and test technology that will empower students to use their ideas as a starting point for deepening science understanding. Researchers will use a technology that detects student ideas that go beyond a student's general knowledge level to adapt to a student's cultural and linguistic understandings of a science topic.