Automatic Detection of Negotiation in Collaborative Complex Problem Solving Interactions

Abstract

When learners collaborate on complex problems and open-ended tasks, the mechanism of negotiation plays a crucial role in establishing a common understanding and achieving a shared goal among them. Research has shown that negotiation improves problem-solving processes, making it an essential skill to be developed among learners. In this study, we propose a method for automating the identification of negotiation in learners' discourse during collaboration. We leverage language models like BERT, RoBERTa, and GPT2 along with traditional machine learning models like logistic regression to detect utterances of negotiation in learners' discourse while they collaboratively solve engineering estimation problem in an Open-Ended Learning Environment (OELE) called Modeling Based Estimation Learning Environment (MEttLE). Our findings suggest that our approach can accurately identify negotiation utterances with a high accuracy of 0.924 and 0.781 kappa value with a relatively smaller training set. Our method is the first step in real-time detection of negotiation, thereby enabling educators to design scaffolds and environments to help learners engage in effective negotiations.