Understanding students' multi-party epistemic and topic based-dialogue contributions, or how students present knowledge in group-based chat interactions during collaborative game-based learning, offers valuable insights into group dynamics and learning processes. However, manually annotating these contributions is labor-intensive and challenging. To address this, we develop an automated method for recognizing dialogue acts from text chat data of small groups of middle school students interacting in a collaborative game-based learning environment. Our approach utilizes dual contrastive learning and label-aware data augmentation to fine-tune large language models' underlying embedding representations within a supervised learning framework for epistemic and topic-based dialogue act classification. Results show that our method achieves a performance improvement of 4% to 8% over baseline methods in two key classification scenarios. These findings highlight the potential for automated dialogue act recognition to support understanding of how meaning-making occurs by focusing on the development and evolution of knowledge in group discourse, ultimately providing teachers with actionable insights to better support student learning.

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.

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.

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.

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.

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 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.

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.

Narrative and collaboration are two core features of rich interactive learning. Narrative-centered learning environments offer significant potential for supporting student learning. By contextualizing learning within interactive narratives, these environments leverage students' innate facilities for developing understandings through stories. Computer-supported collaborative learning environments offer students rich, collaborative learning experiences in which small groups of students engage in constructing artifacts, addressing disciplinary challenges, and solving problems. Narrative and collaboration have distinct affordances for learning, but combining them poses significant challenges. In this paper, we present initial work on solving this problem by introducing collaborative narrative-centered learning environments. These environments will enable small groups of students to collaboratively solve problems in rich multi-participant storyworlds. We propose a novel framework for designing and developing these environments, which we are using to create a collaborative narrative-centered learning environment for middle school ecosystems education. In the learning environment, students work on problem-solving scenarios centered on how to support optimal fish health in aquatic environments. Results from pilot testing the learning environment with 45 students suggest it supports the creation of engaging and effective collaborative narrative-centered learning experiences.

Purpose - This paper aims to lay out the goals and challenges in using information for ambitious learning practices. Design/methodology/approach - Through a review of the literature, the authors integrate across learning, information sciences and instructional design to identify challenges and possibilities for information searching and sense-making in ambitious learning practices (ALPs). Findings - Learners face a number of challenges in using information in ALPs such as a problem-based learning. These include searching and sourcing, selecting information and sense-making. Although ALPs can be effective, providing appropriate scaffolding, supports and resources is essential. Originality/value - To make complex ALPs available to a wide range of learners requires considering the information literacy demands and how these can be supported. This requires deep understanding and integration across different research literature areas to move toward solutions.