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This study explored how researcher–coach dyads collaborated to create research-based briefs for classroom use. Data from a two-day workshop and dyads’ final products were analyzed using interaction analysis. The dyad’s work illuminated how products and relationships evolved together, fostering ownership and collaboration. These findings inform future researcher–practitioner partnerships and guide facilitation of effective collaborations by highlighting relationship building, agency, and ownership to shape joint work. 

This study examines researcher-practitioner collaborations in educational research, using Cultural-Historical Activity Theory (CHAT) to analyze interactions between STEM coaches and researchers. It explores a two-day workshop focused on practitioner-identified challenges. The research highlighted the need to shift from researcher-centric approaches to balanced, collaborative methods. This study provides insights for developing bidirectional learning models that center practitioners' perspectives, aiming to bridge the gap between research and educational practice through more equitable and transformative partnerships. 

Effective researcher-coach relationships need reciprocal learning, which allows practitioners to share valuable contextual knowledge while researchers share evidence-based ideas. Nevertheless, these collaborations encounter obstacles due to power imbalances, which frequently establish researchers as authorities and reduce the role of practitioners as co-creators. Therefore, this study examines power dynamics in researcher-coach partnerships within educational contexts, emphasizing equitable collaboration strategies. Using Cultural-Historical Activity Theory (CHAT) as a framework, this study analyzes video data from a writing intensive to explore interactions between two participants, Ashley and Russell. Findings reveal that initial tensions foster deeper understanding through negotiated power exchanges. The study underscores that openness, mutual trust, and reflective dialogue are essential for sustainable partnerships, advancing the understanding of power dynamics in researcher-coach collaborations. 

Abstract 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. 

Abstract: The objective of this research was to explore how educators and researchers can collaborate to create research-to-practice briefs that make research accessible for classroom implementation. Specifically, we examined the benefits, opportunities, and difficulties of working together. While there were challenges, our findings suggest that both researchers and teachers can benefit from collaborating. This pilot research is shaping future work to further bridge the gap between research and practice. 

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. 

This poster discusses a promising collaboration platform to encourage students in co- constructing historical knowledge through a network visualization tool. The tool uniquely mediated collaboration at both the small and large group level in a big lecture format undergraduate history class. The findings demonstrated the tool mediated a specific sequence of collaborating processes at both levels and students’ ability to see the historical relationships.