Brian Riordan

Organization/Institution
Educational Testing Service (ETS)
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).

Supporting Teachers in Responsive Instruction for Developing Expertise in Science (Collaborative Research: Riordan)

Educational Testing Service (ETS)
09/01/2018

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.

Using Natural Language Processing to Inform Science Instruction (Collaborative Research: Liu)

Educational Testing Service (ETS)
07/01/2021

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.