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Successful problem-based learning (PBL) often requires students to collectively regulate their learning processes as a group and engage in socially shared regulation of learning (SSRL). This paper focuses on how facilitators supported SSRL in the context of middle-school game-based PBL. Using conversation analysis, this study analyzed text-based chat messages of facilitators and students collected during gameplay. The analysis revealed direct modeling strategies such as performing regulative processes, promoting group awareness, and dealing with contingency as well as indirect strategies including prompting questions and acknowledgment of regulation, and the patterns of how facilitation faded to yield responsibilities to students to regulate their own learning. The findings will inform researchers and practitioners to design prompts and develop technological tools such as adaptive scaffolding to support SSRL in PBL or other collaborative inquiry processes. 

rior research has shown that digital games can enhance STEM education by providing learners with immersive and authentic scientific experiences. However, optimizing the learning outcomes of students engaged in game-based environments requires aligning the game design with diverse student needs. Therefore, an in-depth understanding of player behavior is crucial for identifying students who need additional support or modifications to the game design. This study applies an Ordered Network Analysis (ONA)—a specific kind of Epistemic Network Analysis (ENA)—to examine the game trace log data of student interactions, to gain insights into how learning gains relate to the different ways that students move through an open-ended virtual world for learning microbiology. Our findings reveal that differences between students with high and low learning gains are mediated by their prior knowledge. Specifically, level of prior knowledge is related to behaviors that resemble wheel-spinning, which warrant the development of future interventions. Results also have implications for discovery with modeling approaches and for enhancing in-game support for learners and improving game design. 

Prior research has shown that digital games can enhance STEM education by providing learners with immersive and authentic scientific experiences. However, optimizing the learning outcomes of students engaged in game-based environments requires aligning the game design with diverse student needs. Therefore, an in-depth understanding of player behavior is crucial for identifying students who need additional support or modifications to the game design. This study applies an Ordered Network Analysis (ONA)—a specific kind of Epistemic Network Analysis (ENA)—to examine the game trace log data of student interactions, to gain insights into how learning gains relate to the different ways that students move through an open-ended virtual world for learning microbiology. Our findings reveal that differences between students with high and low learning gains are mediated by their prior knowledge. Specifically, level of prior knowledge is related to behaviors that resemble wheel-spinning, which warrant the development of future interventions. Results also have implications for discovery with modeling approaches and for enhancing in-game support for learners and improving game design. 

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.