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Collaborative argumentation enables students to build disciplinary knowledge and to think in disciplinary ways. We use Large Language Models (LLMs) to improve existing methods for collaboration classification and argument identification. Results suggest that LLMs are effective for both tasks and should be considered as a strong baseline for future research. 

This paper presents the design and evaluation of an automated writing evaluation system that integrates natural language processing (NLP) and user interface design to support students in an important writing skill, namely, self-monitored revising. Results from a classroom deployment suggest that NLP can accurately analyze where and what kind of revisions students make across paper drafts, that students engage in self-monitored revising, and that the interfaces for visualizing the NLP results are perceived by students to be useful. 

Speakers build rapport in the process of aligning conversational behaviors with each other. Rapport engendered with a teachable agent while instructing domain material has been shown to promote learning. Past work on lexical alignment in the field of education suffers from limitations in both the measures used to quantify alignment and the types of interactions in which alignment with agents has been studied. In this paper, we apply alignment measures based on a data-driven notion of shared expressions (possibly composed of multiple words) and compare alignment in one-on-one human-robot (H-R) interactions with the H-R portions of collaborative human-human-robot (H-H-R) interactions. We find that students in the H-R setting align with a teachable robot more than in the H-H-R setting and that the relationship between lexical alignment and rapport is more complex than what is predicted by previous theoretical and empirical work. 

Working collaboratively in groups can positively impact performance and student engagement. Intelligent social agents can provide a source of personalized support for students, and their benefits likely extend to collaborative settings, but it is difficult to determine how these agents should interact with students. Reinforcement learning (RL) offers an opportunity for adapting the interactions between the social agent and the students to better support collaboration and learning. However, using RL in education with social agents typically involves training using real students. In this work, we train an RL agent in a high-quality simulated environment to learn how to improve students’ collaboration. Data was collected during a pilot study with dyads of students who worked together to tutor an intelligent teachable robot. We explore the process of building an environment from the data, training a policy, and the impact of the policy on different students, compared to various baselines.