Successfully engaging in collaborative problem solving is an essential constituent of professional and general life skills.Collaborative problem solving is also used to engage learners in collaborative processes relevant to the acquisition of knowledge and skills.Here, learners co-construct knowledge by transactively building on each other's contributions.In this symposium, we explore the factors leading to successful engagement in collaborative problem solving.The conceptualization of transactivity in collaborative problem solving is refined balancing the merits of both sharing new knowledge and referring to others' contributions.Empirical studies in this symposium show that it is important for successful collaborative problem solving that individuals elicit and share information with their learning partners who should evenly participate.Further, engaging in relevant collaborative processes can be facilitated by scaffolds such as sentence openers facilitating learners referring to each other.

This paper explores how digital traces within a CSCL classroom can provide evidence to better understand development of students' practices in scientific inquiry.With trace log data, we found several in-game patterns associated with inquiry practices, which implies that trace log data might provide be digital evidence accounting for student learning gains and trajectory of students' scientific inquiry practices within a CSCL classroom.

Because implementing and orchestrating collaborative problem-based learning (PBL) in K-12 classrooms requires teachers to manage multiple activities and access various teaching resources at the same time, this is an exceptionally complex task for designers to develop tools for orchestration support as well as for teachers to coordinate. The aim of the present study was to explore what classroom teachers perceived to be essential to support orchestration in PBL, but also to surface potential areas of tension between teacher and designer goals through a co-design approach. Thirteen K-12 classroom teachers were interviewed to find out what teachers consider integral to support orchestration in game-based PBL environments. We present the primary features that were identified as essential from the teacher interviews, which include the ability to (1) conduct assessment, (2) intervene and scaffold in different ways, and (3) visualize information. In addition, we discuss the design tensions between the goals of the teachers and designers as we work together on a shared view of what it means to design an intelligent assistant for classroom orchestration and present the design documents with implications.

Collaborative game-based learning environments offer the promise of combining the strengths of computer-supported collaborative learning and gamebased learning to enable students to work collectively towards achieving problemsolving goals in engaging storyworlds. Group chat plays an important role in such environments, enabling students to communicate with team members while exploring the learning environment and collaborating on problem solving. However, students may engage in chat behavior that negatively affects learning. To help address this problem, we introduce a multidimensional stealth assessment model for jointly predicting students' out-of-domain contributions to group chat as well as their learning outcomes with multi-task learning. Results from evaluating the model indicate that multi-task learning, which simultaneously performs the multidimensional stealth assessment, utilizing predictive features extracted from in-game actions and group chat data outperforms single-task variants and suggest that multi-task learning can effectively support stealth assessment in collaborative game-based learning environments.

Kyungjin Park, Hyunwoo Sohn, Wookhee Min, Bradford Mott, Krista Glazewski, Cindy E. Hmelo-Silver, James Lester. Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue. 2022.

This exploratory paper highlights how problem-based learning (PBL) provided the pedagogical framework used to design and interpret learning analytics from Crystal Island: EcoJourneys, a collaborative game-based learning environment centred on supporting science inquiry. In Crystal Island: EcoJourneys, students work in teams of four, investigate the problem individually and then utilize a brainstorming board, an in-game PBL whiteboard that structured the collaborative inquiry process. The paper addresses a central question: how can PBL support the interpretation of the observed patterns in individual actions and collaborative interactions in the collaborative game-based learning environment? Drawing on a mixed method approach, we first analyzed students' pre- and post-test results to determine if there were learning gains. We then used principal component analysis (PCA) to describe the patterns in game interaction data and clustered students based on the PCA. Based on the pre- and post-test results and PCA clusters, we used interaction analysis to understand how collaborative interactions unfolded across selected groups. Results showed that students learned the targeted content after engaging with the game-based learning environment. Clusters based on the PCA revealed four main ways of engaging in the game-based learning environment: students engaged in low to moderate self-directed actions with (1) high and (2) moderate collaborative sense-making actions, (3) low self-directed with low collaborative sense-making actions and (4) high self-directed actions with low collaborative sense-making actions. Qualitative interaction analysis revealed that a key difference among four groups in each cluster was the nature of verbal student discourse: students in the low to moderate self-directed and high collaborative sense-making cluster actively initiated discussions and integrated information they learned to the problem, whereas students in the other clusters required more support. These findings have implications for designing adaptive support that responds to students' interactions with in-game activities.

Children who are more curious learn more and perform better in school. Although there has been some work on how to induce curiosity in the moment, much less is known about how to promote curiosity-driven behaviors across contexts. In a preregistered experiment, 103 children ages 5-7-years-old participated in eight virtual science lessons over two weeks. Children were randomly assigned to a condition in which they were encouraged to ask questions, or to a condition in which they were asked to listen carefully. At the end of training, children in the question-asking condition valued new science information significantly more than children in the listening condition. They also learned marginally more than children in the listening condition. Children with less background knowledge benefited more from question-asking practice. Our results suggest that question-asking practice can boost some aspects of curiosity and learning.

Collaborative game-based learning environments offer significant promise for creating engaging group learning experiences. Online chat plays a pivotal role in these environments by providing students with a means to freely communicate during problem solving. These chat-based discussions and negotiations support the coordination of students' in-game learning activities. However, this freedom of expression comes with the possibility that some students might engage in undesirable communicative behavior. A key challenge posed by collaborative game-based learning environments is how to reliably detect disruptive talk that purposefully disrupt team dynamics and problem-solving interactions. Detecting disruptive talk during collaborative game-based learning is particularly important because if it is allowed to persist, it can generate frustration and significantly impede the learning process for students. This paper analyzes disruptive talk in a collaborative game-based learning environment for middle school science education to investigate how such behaviors influence students' learning outcomes and varies across gender and students' prior knowledge. We present a disruptive talk detection framework that automatically detects disruptive talk in chat-based group conversations. We further investigate both classic machine learning and deep learning models for the framework utilizing a range of dialogue representations as well as supplementary information such as student gender. Findings show that long short-term memory network (LSTM)-based disruptive talk detection models outperform competitive baseline models, indicating that the LSTM-based disruptive talk detection framework offers significant potential for supporting effective collaborative game-based learning through the identification of disruptive talk.

Collaborative game-based learning environments integrate gamebased learning and collaborative learning. These environments present students with a shared objective and provide them with a means to communicate, which allows them to share information, ask questions, construct explanations, and work together toward their shared goal. A key challenge in collaborative learning is that students may engage in unproductive discourse, which may affect learning activities and outcomes. Collaborative game-based learning environments that can detect this off-task behavior in real-time have the potential to enhance collaboration between students by redirecting the conversation back to more productive topics. This paper investigates the use of dialogue analysis to classify student conversational utterances as either off-task or on-task. Using classroom data collected from 13 groups of four students, we trained off-task dialogue models for text messages from a group chat feature integrated into Crystal Island: EcoJourneys, a collaborative game-based learning environment formiddle school ecosystem science. We evaluate the effectiveness of the off-task dialogue models, which use different word embeddings (i.e., word2vec, ELMo, and BERT), as well as predictive off-task dialogue models that capture varying amounts of contextual information from the chat log. Results indicate that predictive off-task dialogue models that incorporate a window of recent context and represent the sequential nature of the chat messages achieve higher predictive performance compared to models that do not leverage this information. These findings suggest that off-task dialogue models for collaborative game-based learning environments can reliably recognize and predict students' off-task behavior, which introduces the opportunity to adaptively scaffold collaborative dialogue.

Collaborative inquiry learning affords educators a context within which to support understanding of scientific practices, disciplinary core ideas, and crosscutting concepts. One approach to supporting collaborative science inquiry is through problem-based learning (PBL). However, there are two key challenges in scaffolding collaborative inquiry learning in technology rich environments. First, it is unclear how we might understand the impact of scaffolds that address multiple functions (e.g., to support inquiry and argumentation). Second, scaffolds take different forms, further complicating how to coordinate the forms and functions of scaffolds to support effective collaborative inquiry. To address these issues, we identify two functions that needed to be scaffolded, the PBL inquiry cycle and accountable talk. We then designed predefined hard scaffolds and just-in-time soft scaffolds that target the regulation of collaborative inquiry processes and accountable talk. Drawing on a mixed method approach, we examine how middle school students from a rural school engaged with Crystal Island: EcoJourneys for two weeks (N=45). Findings indicate that hard scaffolds targeting the PBL inquiry process and soft scaffolds that targeted accountable talk fostered engagement in these processes. Although the one-to-one mapping between form and function generated positive results, additional soft scaffolds were also needed for effective engagement in collaborative inquiry and that these soft scaffolds were often contingent on hard scaffolds. Our findings have implications for how we might design the form of scaffolds across multiple functions in game-based learning environments.