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1. Convergent Group Understanding: Indicator of Knowledge Integration?

   Cang, X; Zeng, L; Tissenbaum, M; Gnesdilow, D; Puntambekar, S (June 2024, International Society for the Learning Sciences)

   Clarke_Midura, J; Kollar, I; Gu, X; DAngelo, C (Ed.)

   This study investigates small group collaborative learning with a technologysupported environment. We aim to reveal key aspects of collaborative learning by examining variations in interaction, the influence of small group collaboration on science knowledge integration, and the implications for individual knowledge mastery. Results underscore the importance of high-quality science discourse and user-friendly tools. The study also highlights that group-level negotiations may not always affect individual understanding. Overall, this research offers insights into the complexities of collaboration and its impact on science learning. 

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2. Multimodal Learning Analytics for Predicting Student Collaboration Satisfaction in Collaborative Game-Based Learning

   https://doi.org/10.5281/zenodo.12729802  

   Acosta, Halim; Lee, Seung; Mott, Bradford; Bae, Haesol; Glazewski, Krista; Hmelo-Silver, Cindy; Lester, James (January 2024, International Educational Data Mining Society)

   Benjamin, Paaßen; Carrie, Demmans Epp (Ed.)

   Collaborative game-based learning offers opportunities for students to participate in small group learning experiences that foster knowledge sharing, problem solving, and engagement. Student satisfaction with their collaborative experiences plays a pivotal role in shaping positive learning outcomes and is a critical factor in group success during learning. Gauging students申f satisfaction within collaborative learning contexts can offer insights into student engagement and participation levels while affording practitioners the ability to provide targeted interventions or scaffolding. In this paper,we propose a framework for inferring student collaboration satisfaction with multimodal learning analytics from collaborative interactions. Utilizing multimodal data collected from 50 middle school students engaged in collaborative game-based learning, we predict student collaboration satisfaction. We first evaluate the performance of baseline models on individual modalities for insight into which modalities are most informative. We then devise a multimodal deep learning model that leverages a cross-attention mechanism to attend to salient information across modalities to enhance collaboration satisfaction prediction. Finally,we conduct ablation and feature importance analysis to understand which combination of modalities and features is most effective. Findings indicate that various combinations of data sources are highly beneficial for student collaboration satisfaction prediction. 

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3. Characterising Individual-Level Collaborative Learning Behaviours Using Ordered Network Analysis and Wearable Sensors

   Yan, L; Swiecki, Z; Gašević, D; Zhao; L; Li, X; Martinez-Maldonado; R; Tan; Y; et al (October 2023, Springer, Cham)

   Irgens, G; Knight, S (Ed.)

   Wearable positioning sensors are enabling unprecedented opportunities to model students’ procedural and social behaviours during collaborative learning tasks in physical learning spaces. Emerging work in this area has mainly focused on modelling group-level interactions from low-level x-y positioning data. Yet, little work has utilised such data to automatically identify individual-level differences among students working in co-located groups in terms of procedural and social aspects such as task prioritisation and collaboration dynamics, respectively. To address this gap, this study characterised key differences among 124 students’ procedural and social behaviours according to their perceived stress, collaboration, and task satisfaction during a complex group task using wearable positioning sensors and ordered networked analysis. The results revealed that students who demonstrated more collaborative behaviours were associated with lower stress and higher collaboration satisfaction. Interestingly, students who worked individually on the primary and secondary learning tasks reported lower and higher task satisfaction, respectively. These findings can deepen our understanding of students’ individual-level behaviours and experiences while learning in groups. 

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4. Increasing students’ social engagement during COVID-19 with Net.Create: collaborative social network analysis to map historical pandemics during a pandemic

   https://doi.org/10.1108/ILS-04-2020-0105  

   Craig, Kalani; Humburg, Megan; Danish, Joshua A.; Szostalo, Maksymilian; Hmelo-Silver, Cindy E.; McCranie, Ann (July 2020, Information and Learning Sciences)

   Purpose The authors explored shifts in social interactions, content engagement and history learning as students who were studying one pandemic simultaneously experienced another. This paper aims to understand how the Net.Create network visualization tool would support students as they tried to understand the many complex interactions in a historical text in a remote learning environment and how sustained knowledge building using Net.Create would shape student attitudes toward remote learning, collaboration and engagement. Design/methodology/approach This paper explores changes in engagement and learning in a survey-level history course on the black death after a shift to remote learning during the COVID-19 pandemic. The authors used activity theory to focus the adaptation of Net.Create, a web-based collaborative social-network-analysis tool and to understand how it supported group-based remote learning. The authors describe how the redesigned activities sustained engagement with historical content and report coded student network entries, reading responses and surveys to illustrate changes in engagement and learning. Findings The results suggest that students benefit from personal connections to historical content and their peers. Net.Create supported both through collaborative knowledge-building activities and reflection on how their quarantine experiences compared to the historical content they read. It is possible to avoid student frustrations with traditional “group work” even in a remote environment by supporting collaborative learning using Net.Create and a mix of individual and group contributions. Originality/value This is the first use of a collaborative network visualization tool to support large classroom interaction and engagement with history content at the undergraduate level. 

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5. Collaborative (in)decision: A Preliminary Investigation of the Differences in Undergraduate Engineering Capstone Students’ Collaborative Behaviors

   Gerhard, M; Madboly, M; Pitterson, N; Dringenberg, E; Ahn, B (April 2025, International journal of engineering education)

   This study investigates differences in collaborative behaviors among undergraduate engineering capstone students through a behavioral sorting methodology. Using the Comprehensive Assessment of Team Member Effectiveness Behaviorally Anchored Rating Scale (CATME-B), 25 students from a senior-level interdisciplinary engineering capstone course sorted collaborative behaviors according to their observed frequency in collaborative experiences. The sorting revealed patterns worth further investigation across technical/task-oriented, process-oriented, and interpersonal/social dimensions of collaboration, with variations emerging between demographic groups. Technical behaviors showed consistent observation across the sample, while process-oriented and interpersonal behaviors exhibited notable variability. The initial results suggest that collaborative behaviors may be influenced by sociocultural dynamics, with students adapting their engagement strategies in response to identity-related and culturally situated contexts. This preliminary investigation indicates the need for further research to examine how students’ perceptions and attitudes toward collaborative behaviors influence their engagement in engineering group work; particularly focusing on the relationships between individual beliefs, group contexts, and behavioral choices. Such understanding could inform theoretical models of engineering collaboration and guide the development of evidence-based approaches to collaborative learning. 

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