Enhancing Engagement Modeling in Game-Based Learning Environments with Student-Agent Discourse Analysis

Pedagogical agents offer significant promise for engaging students in learning. In this paper, we investigate students' conversational interactions with a pedagogical agent in a game-based learning environment for middle school science education. We utilize word embeddings of student-agent conversations along with features distilled from students' in-game actions to induce predictive models of student engagement. An evaluation of the models' accuracy and early prediction performance indicates that features derived from students' conversations with the pedagogical agent yield the highest accuracy for predicting student engagement. Results also show that combining student problem-solving features and conversation features yields higher performance than a problem solving-only feature set. Overall, the findings suggest that student-agent conversations can greatly enhance student models for game-based learning environments.