Supporting meaningful student engagement is an important goal of educators and educational researchers. However, there are many variations in definitions and measures of engagement, making it challenging to recognize and design for deep engagement. To address this challenge, the purpose of this work is to interrogate the ways that different theoretical and methodological choices impact how we describe student engagement. Methodology We applied two student engagement coding schemes to classroom video from two computer- supported collaborative learning (CSCL) contexts. We compare how each coding scheme made different types of engagement more or less visible and explore potential decision points and synergies. Findings The coding schemes differed in many ways, including their theoretical grounding, level of analysis, and inclusion of various dimensions of engagement. We argue that each of these methodological choices orient researchers to video data differently, which has implications for how researchers make sense of group work, next analytical steps, and claims. There are also substantial challenges that researchers may encounter when using engagement coding schemes outside of the contexts in which they were developed, including achieving reliability and the relevance of indicators of engagement to the novel context. Discussion This study contributes to our understanding of how to make sense of engagement in classroom video and the synergies between different coding choices and theoretical perspectives.

This study develops and validates a human-centered approach to measuring small-group, multidimensional engagement in a game-based Computer-Supported Collaborative Learning (CSCL) environment. Grounded in sociocultural perspectives, the proposed framework integrates multimodal data sources, including discourse, interaction logs, and linguistic indicators, to assess behavioral, collaborative, and disciplinary engagement through theory-informed human interpretation. Human-annotated engagement ratings serve as ground truth for evaluating whether supervised machine learning models can approximate multimodal interpretive judgments, establishing a pathway toward scalable assessment. Three supervised learning algorithms were compared to examine model performance across engagement dimensions, and multiple regression analysis was conducted to investigate associations between quest-level average engagement and group learning outcomes. Results indicate that average collaborative and disciplinary engagement ratings positively predict group performance, whereas behavioral engagement alone does not significantly relate to learning outcomes. Qualitative analysis of group responses further illustrates these associations. Methodologically and theoretically, the study advances multimodal learning analytics by proposing a generalizable, human-centered workflow that integrates multimodal interpretation with model-assisted scalability, emphasizing the essential role of human interpretation in producing valid and contextually grounded engagement assessments in collaborative learning environments.

Abstract When evaluating a two-phase intervention, the cumulative average treatment effect (ATE) is often the primary causal estimand of interest. However, some individuals who do not respond well to the Phase I treatment may subsequently display noncompliant behaviours. At the same time, exposure to the Phase I treatment is expected to directly influence an individual's potential outcomes, thereby violating the exclusion restriction. Building on an instrumental variable strategy for multisite trials, we clarify the conditions under which the cumulative ATE of a two-phase treatment can be identified by employing the random assignment of the Phase I treatment as the instrument. Our strategy relaxes both the conventional exclusion restriction and sequential ignorability assumptions. We assess the performance of the new strategy through simulation studies. Additionally, we reanalyse data from the Tennessee class size study, in which students and teachers were randomly assigned to either small or regular class types in kindergarten (Phase I) with noncompliance emerging in Grade 1 (Phase II). Applying our new strategy, we estimate the cumulative ATE of receiving two consecutive years of instruction in a small versus regular class.

Stealth assessment in game-based learning analyzes gameplay behaviors to measure student competencies unobtrusively. Grounded in the theoretical premise that in-game objectives shape learning outcomes, this article investigates how goal recognition can enhance stealth assessment by using predictions about immediate gameplay objectives as evidence for modeling learning. We evaluated this approach with 119 middle school students using an educational microbiology game, examining both overall posttest performance and mastery of individual science concepts. Our deep learning architecture combines gameplay interactions, written reflections, and goal recognition to assess different learning dimensions. Results demonstrate that goal recognition significantly improves concept-level assessment across all four concepts, achieving higher accuracy and earlier prediction convergence. Incorporating goal recognition makes stealth assessment more responsive to students' intentions during gameplay, enabling concept-level scaffolding to enhance student learning.

This study applies CMDA to analyze communication in a game-based CSCL setting.Examining two groups with differing outcomes, we explored participation, speech acts, and topic development.Findings show interaction density and balanced contributions impact performance, while communicative acts like "Inquire" and "Elaborate" shape collaboration.Topic coherence influences group effectiveness.Visualizations illustrate these dynamics.Despite limitations, CMDA provides insights into CSCL processes and informs adaptive support for collaborative learning.

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.

This study investigates the uses of a teacher dashboard in a technology-rich, problembased learning (PBL) environment incorporating collaborative game-based learning for middle school science inquiry.The dashboard, designed through participatory approaches with teachers, provides real-time data visualization of students' learning activities, aiming to reduce teachers' orchestration load and enhance PBL facilitation.Findings indicate the teacher actively orchestrated classroom activities using the dashboard, balancing between digital insights and personal observation.The study extends current models of dashboard interaction by integrating classroom observations and teachers' prior knowledge into the interpretation phase.It emphasizes the need for dashboards to offer strategic-level support, enabling teachers to tailor instruction effectively.This approach not only reduces the orchestration load but also promotes meaningful student engagement and collaborative inquiry in a PBL setting.

The study aims to examine the effectiveness of the coordinated computer-based scaffolds mediating students' collaborative problem-solving (CPS) practices and science learning.24 students in six groups participated in CPS activities related to middle school life science, conducted in a scaffolded collaborative game.We analyzed their CPS practices during 31 collaborative periods across three quests using latent profile analysis, chi-square tests, and interaction analysis.We found certain clusters associated with higher-performing groups and patterns of their CPS engagement.Furthermore, we found that these CPS engagement patterns linked to higher performance were synergistically mediated by the coordination of fixed and adaptive scaffolds.The study also discusses implications and outlines future research directions.

This paper explores student resources utilization and its impact on student learning outcomes in a game-based CSCL context from a sociocultural perspective.Analysis of grouplevel resource utilization revealed three distinct patterns that differentiate high-performing from low-performing groups.Qualitative analysis of group problem-solving responses provided further evidence of the association between group-level resource use and subsequent group performance.The study sheds light on the association between resource utilization and learning success in collaborative learning settings, offering valuable guidance for enhancing the pedagogical design of learning resources and teacher scaffolding to promote effective resource utilization.

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.