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1. A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students’ Formative Assessment Responses in Science

   Cohn, Clayton ; Hutchins, Nicole ; Biswas, Gautam ( September 2023 , AAAI Press)

   Martin Fred ; Norouzi, Narges ; Rosenthal, Stephanie (Ed.)

   This paper examines the use of LLMs to support the grading and explanation of short-answer formative assessments in K12 science topics. While significant work has been done on programmatically scoring well-structured student assessments in math and computer science, many of these approaches produce a numerical score and stop short of providing teachers and students with explanations for the assigned scores. In this paper, we investigate few-shot, in-context learning with chain-of-thought reasoning and active learning using GPT-4 for automated assessment of students’ answers in a middle school Earth Science curriculum. Our findings from this human-in-the-loop approach demonstrate success in scoring formative assessment responses and in providing meaningful explanations for the assigned score. We then perform a systematic analysis of the advantages and limitations of our approach. This research provides insight into how we can use human-in-the-loop methods for the continual improvement of automated grading for open-ended science assessments. 

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2. Automated scoring of science explanations for multiple NGSS dimensions and knowledge integration

   Riordan, B. ; Wiley, K. ; Chen, J. K. ; Bradford, A. ; Bichler, S. ; Mulholland, M. ; Gerard, L. F. ( April 2020 , Annual Meeting of the American Educational Research Association (AERA))

   The Next Generation Science Standards (NGSS) emphasize integrating three dimensions of science learning: disciplinary core ideas, cross-cutting concepts, and science and engineering practices. In this study, we develop formative assessments that measure student understanding of the integration of these three dimensions along with automated scoring methods that distinguish among them. The formative assessments allow students to express their emerging ideas while also capturing progress in integrating core ideas, cross-cutting concepts, and practices. We describe how item and rubric design can work in concert with an automated scoring system to independently score science explanations from multiple perspectives. We describe item design considerations and provide validity evidence for the automated scores. 

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3. Towards a formative feedback generation agent: Leveraging a human-in-the-loop, chain-of-thought prompting approach with LLMs to evaluate formative assessment responses in K-12 science.

   Cohn, Clayton ; Hutchins, Nicole ; Biswas, Gautam ( October 2023 , dl.acm.org)

   This research explores a novel human-in-the-loop approach that goes beyond traditional prompt engineering approaches to harness Large Language Models (LLMs) with chain-of-thought prompting for grading middle school students’ short answer formative assessments in science and generating useful feedback. While recent efforts have successfully applied LLMs and generative AI to automatically grade assignments in secondary classrooms, the focus has primarily been on providing scores for mathematical and programming problems with little work targeting the generation of actionable insight from the student responses. This paper addresses these limitations by exploring a human-in-the-loop approach to make the process more intuitive and more effective. By incorporating the expertise of educators, this approach seeks to bridge the gap between automated assessment and meaningful educational support in the context of science education for middle school students. We have conducted a preliminary user study, which suggests that (1) co-created models improve the performance of formative feedback generation, and (2) educator insight can be integrated at multiple steps in the process to inform what goes into the model and what comes out. Our findings suggest that in-context learning and human-in-the-loop approaches may provide a scalable approach to automated grading, where the performance of the automated LLM-based grader continually improves over time, while also providing actionable feedback that can support students’ open-ended science learning. 

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4. Examining the effect of automated assessments and feedback on students’ written science explanations

   Puntambekar, Sadhana ; Dey, Indrani ; Gnesdilow, Dana ; Passonneau, Rebecca J ; Kim, ChanMin ( July 2023 , Computer Supported Collaborative Learning Conference)

   Writing scientific explanations is a core practice in science. However, students find it difficult to write coherent scientific explanations. Additionally, teachers find it challenging to provide real-time feedback on students’ essays. In this study, we discuss how PyrEval, an NLP technology, was used to automatically assess students’ essays and provide feedback. We found that students explained more key ideas in their essays after the automated assessment and feedback. However, there were issues with the automated assessments as well as students’ understanding of the feedback and revising their essays. 

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5. Analyzing saliency in neural models for scoring content in science explanations

   Riordan, Brian ( November 2020 , BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP)

   null (Ed.)

   Models for automated scoring of content in educational applications continue to demonstrate improvements in human-machine agreement, but it remains to be demonstrated that the models achieve gains for the “right” reasons. For providing reliable scoring and feedback, both high accuracy and connecting scoring decisions to scoring rubrics are crucial. We provide a quantitative and qualitative analysis of automated scoring models for science explanations of middle school students in an online learning environment that leverages saliency maps to explore the reasons for individual model score predictions. Our analysis reveals that top-performing models can arrive at the same predictions for very different reasons, and that current model architectures have difficulty detecting ideas in student responses beyond keywords. 

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