Minds and Machines Unite: Deciphering Social and Cognitive Dynamics in Collaborative Problem Solving with AI

Abstract

We investigated the feasibility of automating the modeling of collaborative problem-solving skills encompassing both social and cognitive aspects. Leveraging a diverse array of cutting-edge techniques, including machine learning, deep learning, and large language models, we embarked on the classification of qualitatively coded interactions within groups. These groups were composed of four undergraduate students, each randomly assigned to tackle a decision-making challenge. Our dataset comprises contributions from 514 participants distributed across 129 groups. Employing a suite of prominent machine learning methods such as Random Forest, Support Vector Machines, Naive Bayes, Recurrent and Convolutional Neural Networks, BERT, and GPT-2 language models, we undertook the intricate task of classifying peer interactions. Notably, we introduced a novel task-based train-test split methodology, allowing us to assess classification performance independently of task-related context. This research carries significant implications for the learning analytics field by demonstrating the potential for automated modeling of collaborative problem-solving skills, offering new avenues for understanding and enhancing group learning dynamics.