Automated analysis of student writing has the potential to provide alternatives to selected-response questions such as multiple choice, and to enable teachers and instructors to assess students’ reasoning skills based on their long-form writing. Further, automated support to assess both short answers and long passages could provide students with a smoother trajectory towards mastery of written communication. Our methods focus on the specific ideas students express to support formative assessment through different kinds of feedback, which aims to scaffold their abilities to reason and communicate. In this talk I review our work in the PSU NLP lab on methods for automated assessment of different forms of student writing, from younger and older students. I will briefly illustrate highly curated datasets created in collaboration with researchers in STEM education, results from deployment of an older content analysis tool on middle school physics essays, and very preliminary results on assessment of college students’ physics lab reports. I will also present our current work on short answer assessment using a novel recurrent relation network that incorporates contrastive learning.
Becky Passonneau has been a Professor in the Department of Computer Science and Engineering at Penn State University since 2016, when she joined as the first NLP researcher. Since that time the NLP faculty has grown to include Rui Zhang and Wenpeng Yin. Becky’s research in natural language processing addresses computational pragmatics, meaning the investigation of language as a system of interactive behavior that serves a wide range of purposes. She received her PhD in Linguistics from the University of Chicago in 1985, and worked at several academic and industry research labs before joining Penn State. Her work is reported in over 140 publications in journals and refereed conference proceedings, and has been funded through 27 sponsored projects from 16 sources, including government agencies, corporate sponsors, corporate gifts, and foundations..