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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. 

Abstract: Given that pair programming has proved to be an effective pedagogical approach for teaching programming skills, it is now important to explore alternative collaborative configurations. One popular configuration is where dyads collaborate by sharing a single computer sitting side-by-side. However, prior research points to potential challenges for elementary students when sharing a single computer when collaborating. This prompted us to explore another configuration where dyads sit side by side but collaborate on a shared virtual platform with individual computers. We compared the discourse of students’ collaboration under these two settings. Results show that although there are no significant differences in the amount of collaborative talk between the two configurations, there is qualitative evidence of how differing affordances of two configurations shape collaborative elementary students’ practices. 

A key affordance of game-based learning environments is their potential to unobtrusively assess student learning without interfering with gameplay. In this paper, we introduce a temporal analytics framework for stealth assessment that analyzes students' problem-solving strategies. The strategy-based temporal analytic framework uses long short-term memory network-based evidence models and clusters sequences of students' problem-solving behaviors across consecutive tasks. We investigate this strategy based temporal analytics framework on a dataset of problem solving behaviors collected from student interactions with a game-based learning environment for middle school computational thinking. The results of an evaluation indicate that the strategy-based temporal analytics framework significantly outperforms competitive baseline models with respect to stealth assessment predictive accuracy.