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.
- 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).