More Like this

1. 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. 

   more » « less
2. Working Together Apart through Embodiment: Engaging in Everyday Collaborative Activities in Social Virtual Reality

   https://doi.org/10.1145/3492836  

   Freeman, Guo ; Acena, Dane ; McNeese, Nathan J. ; Schulenberg, Kelsea ( January 2022 , Proceedings of the ACM on Human-Computer Interaction)

   Computer-mediated collaboration has long been a core research interest in CSCW and HCI. As online social spaces continue to evolve towards more immersive and higher fidelity experiences, more research is still needed to investigate how emerging novel technology may foster and support new and more nuanced forms and experiences of collaboration in virtual environments. Using 30 interviews, this paper focuses on what people may collaborate on and how they collaborate in social Virtual Reality (VR). We broaden current studies on computer-mediated collaboration by highlighting the importance of embodiment for co-presence and communication, replicating offline collaborative activities, and supporting the seamless interplay of work, play, and mundane experiences in everyday lives for experiencing and conceptualizing collaboration in emerging virtual environments. We also propose potential design implications that could further support everyday collaborative activities in social VR 

   more » « less
3. A social network analysis of collaborative governance for the food-energy-water nexus in Phoenix, AZ, USA

   https://doi.org/10.1007/s13412-021-00676-3  

   Jones, J. Leah ; White, Dave D. ( March 2021 , Journal of Environmental Studies and Sciences)

   null (Ed.)

   Despite the known benefits of integrated policy and planning, traditional governance decisions in the food-energy-water (FEW) nexus are often made without cross-sector collaboration, potentially leading to unintended consequences and decreased resource security. Applying collaborative governance approaches to the FEW nexus provides an opportunity to shift towards integrated policy of food, energy, and water governance; doing so first requires an understanding of the limitations of current governance structures and the opportunities for change. We conduct a social network analysis of stakeholders in Phoenix, AZ using secondary data sources to construct the social network of collaboration and to analyze the ability of the governance landscape to facilitate or hinder collaborative governance. The social network measures indicate potential challenges to collaborative governance of FEW nexus stakeholders, such as limited trust between actors. However, leveraging bridging actors provides opportunities to increase collaborative governance between sectors. This research is important for implementing collaborative FEW nexus governance in practice. 

   more » « less
4. Building a reinforcement learning environment from limited data to optimize teachable robot interventions.

   Maidment, T. ; Yu, M. ; Lobczowski, N. G ; Kovashka, A. ; Walker, E. ; Litman, D. ; & Nokes-Malach, T. ( January 2022 , Proceedings of the 15th International Conference on Educational Data Mining)

   Mitrovic, A. ; & Bosch, N. (Ed.)

   Working collaboratively in groups can positively impact performance and student engagement. Intelligent social agents can provide a source of personalized support for students, and their benefits likely extend to collaborative settings, but it is difficult to determine how these agents should interact with students. Reinforcement learning (RL) offers an opportunity for adapting the interactions between the social agent and the students to better support collaboration and learning. However, using RL in education with social agents typically involves training using real students. In this work, we train an RL agent in a high-quality simulated environment to learn how to improve students’ collaboration. Data was collected during a pilot study with dyads of students who worked together to tutor an intelligent teachable robot. We explore the process of building an environment from the data, training a policy, and the impact of the policy on different students, compared to various baselines. 

   more » « less
5. Measuring and Comparing Collaborative Visualization Behaviors in Desktop and Augmented Reality Environments

   https://doi.org/10.1145/3611659.3615691  

   Kintscher, Michael ; Huang, Jinbin ; Arunkumar, Anjana ; Amresh, Ashish ; Bryan, Chris ( October 2023 , 29th ACM Symposium on Virtual Reality Software and Technology)

   Augmented reality (AR) provides a significant opportunity to improve collaboration between co-located team members jointly analyzing data visualizations, but existing rigorous studies are lacking. We present a novel method for qualitatively encoding the positions of co-located users collaborating with head-mounted displays (HMDs) to assist in reliably analyzing collaboration styles and behaviors. We then perform a user study on the collaborative behaviors of multiple, co-located synchronously collaborating users in AR to demonstrate this method in practice and contribute to the shortfall of such studies in the existing literature. Pairs of users performed analysis tasks on several data visualizations using both AR and traditional desktop displays. To provide a robust evaluation, we collected several types of data, including software logging of participant positioning, qualitative analysis of video recordings of participant sessions, and pre- and post-study questionnaires including the NASA TLX survey. Our results suggest that the independent viewports of AR headsets reduce the need to verbally communicate about navigating around the visualization and encourage face-to-face and non-verbal communication. Our novel positional encoding method also revealed the overlap of task and communication spaces vary based on the needs of the collaborators. 

   more » « less