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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 sci- ence 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’ conversa- tions 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. 

Game-based learning offers rich learning opportunities, but open-ended games make it difficult to identify struggling students. Prior work compares student paths to a single expert’s “golden path.” This effort focuses on efficiency, but additional pathways may be required for learning. We examine data from middle schoolers who played Crystal Island, a learning game for microbiology. Results show higher learning gains for students with exploratory behaviors, with interactions between prior knowledge and frustration. Results have implications for designing adaptive scaffolding for learning and affective regulation. 

Game-based learning offers rich learning opportunities, but open-ended games make it difficult to identify struggling students. Prior work compares student paths to a single expert’s “golden path.” This effort focuses on efficiency, but additional pathways may be required for learning. We examine data from middle schoolers who played Crystal Island, a learning game for microbiology. Results show higher learning gains for students with exploratory behaviors, with interactions between prior knowledge and frustration. Results have implications for designing adaptive scaffolding for learning and affective regulation.