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1. Why do capstone students choose to perform behaviors? Differing prevalence in collaborative choices.

   Gerhardt, Mitchell; Pitterson, Nicole; Dringenberg, Emily; Ahn, Benjamin (June 2024, Capstone Design Conference 2024)

   This project explores the collaborative skills occurring within engineering education and practice. While technical competence is crucial, collaborative skills are paramount in engineering enterprises, and current evidence suggests working in teams does not ensure the development of effective collaboration behaviors among engineers. Yet, lifelong learning requires engineers to navigate complex interactions within diverse design teams, emphasizing the need for a nuanced understanding of collaboration. To address this gap, our study aims to identify the least-performed effective collaboration behaviors in engineering capstone teams and explore the reasons behind this occurrence. This investigation is part of a larger study that employs the Reasoned Action Approach1 where we seek to uncover individual beliefs and factors influencing the performance of target behaviors. These insights serve as tools for engineers, students, educators, and managers to assess and enhance collaboration skills, fostering effective teamwork in engineering settings. Ultimately, this overarching goal of advancing professional formation in engineering distills into the key question: Why do individuals exhibit variations in performing effective collaboration behaviors in engineering teams? 

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2. Why do capstone students choose to perform behaviors? Differing prevalence in collaborative choices

   Gerhardt, M; Pitterson, N; Dringenberg, E; Ahn, B (June 2024, Capstone Design Conference)

   This project explores the collaborative skills occurring within engineering education and practice. While technical competence is crucial, collaborative skills are paramount in engineering enterprises, and current evidence suggests working in teams does not ensure the development of effective collaboration behaviors among engineers. Yet, lifelong learning requires engineers to navigate complex interactions within diverse design teams, emphasizing the need for a nuanced understanding of collaboration. To address this gap, our study aims to identify the least-performed effective collaboration behaviors in engineering capstone teams and explore the reasons behind this occurrence. This investigation is part of a larger study that employs the Reasoned Action Approach where we seek to uncover individual beliefs and factors influencing the performance of target behaviors. These insights serve as tools for engineers, students, educators, and managers to assess and enhance collaboration skills, fostering effective teamwork in engineering settings. Ultimately, this overarching goal of advancing professional formation in engineering distills into the key question: Why do individuals exhibit variations in performing effective collaboration behaviors in engineering teams? 

   more » « less
3. Why do capstone students choose to perform behaviors? Differing prevalence in collaborative choices

   Gerhardt, M; Pitterson, N; Dringenberg, E; Ahn, B (June 2024, 2024 Capstone Design Conference)

   This project explores the collaborative skills occurring within engineering education and practice. While technical competence is crucial, collaborative skills are paramount in engineering enterprises, and current evidence suggests working in teams does not ensure the development of effective collaboration behaviors among engineers. Yet, lifelong learning requires engineers to navigate complex interactions within diverse design teams, emphasizing the need for a nuanced understanding of collaboration. To address this gap, our study aims to identify the least-performed effective collaboration behaviors in engineering capstone teams and explore the reasons behind this occurrence. This investigation is part of a larger study that employs the Reasoned Action Approach1 where we seek to uncover individual beliefs and factors influencing the performance of target behaviors. These insights serve as tools for engineers, students, educators, and managers to assess and enhance collaboration skills, fostering effective teamwork in engineering settings. Ultimately, this overarching goal of advancing professional formation in engineering distills into the key question: Why do individuals exhibit variations in performing effective collaboration behaviors in engineering teams? 

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4. 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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5. Noticing, Understanding, and Encouraging Positive Engagement with Collaborative History Learning

   Humburg, Megan; Craig, Kalani; Szostalo, Maksymilian; Danish, Joshua; Hmelo-Silver, Cindy; Mccranie, Ann (January 2021, Reflecting the Past and Embracing the Future: ISLS Annual Meeting 2021 (Online Event))

   de Vries, Erica; Hod, Yotam; Ahn, June (Ed.)

   This study analyzes the implementation of Net.Create, a collaborative network analysis tool (Craig & Danish, 2018), in the context of a digital humanities classroom. Undergraduate students used network analysis to investigate historical objects gathered from the local community in a History Harvest. This paper focuses on the collaborative engagement of groups as they co-constructed conceptual frameworks in Net.Create to explain the individual, social, and cultural histories attached to these objects. Findings suggest that positive social engagement and metacognitive behaviors can support students’ sustained engagement with historical and network analysis ideas. Interwoven personal-oriented and class-oriented social engagement in the data suggest that these forms of engagement can productively sustain engagement with cognitively demanding activities. Having built a supportive environment for collaboration amongst themselves, students were able to smoothly and effectively build on each other’s ideas to generate an understanding of historical and network analysis patterns. 

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