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1. FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning

   https://doi.org/10.1609/aaai.v39i17.33975  

   Kosolwattana, Tanapol; Wang, Huazheng; Al_Kontar, Raed; Lin, Ying (April 2025, Proceedings of the AAAI Conference on Artificial Intelligence)

   Monitoring a large population of dynamic processes with limited resources presents a significant challenge across various industrial sectors. This is due to 1) the inherent disparity between the available monitoring resources and the extensive number of processes to be monitored and 2) the unpredictable and heterogeneous dynamics inherent in the progression of these processes. Online learning approaches, commonly referred to as bandit methods, have demonstrated notable potential in addressing this issue by dynamically allocating resources and effectively balancing the exploitation of high-reward processes and the exploration of uncertain ones. However, most online learning algorithms are designed for 1) a centralized setting that requires data sharing across processes for accurate predictions or 2) a homogeneity assumption that estimates a single global model from decentralized data. To overcome these limitations and enable online learning in a heterogeneous population under a decentralized setting, we propose a federated collaborative online monitoring method. Our approach utilizes representation learning to capture the latent representative models within the population and introduces a novel federated collaborative UCB algorithm to estimate these models from sequentially observed decentralized data. This strategy facilitates informed monitoring of resource allocation. The efficacy of our method is demonstrated through theoretical analysis, simulation studies, and its application to decentralized cognitive degradation monitoring in Alzheimer’s disease. 

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2. NTK-DFL: Enhancing decentralized federated learning in heterogeneous settings via neural tangent kernel

   Thompson, Gabriel; Yue, Kai; Wong, Chau-Wai; Dai, Huaiyu (July 2025, International Conference on Machine Learning)

   Decentralized federated learning (DFL) is a collaborative machine learning framework for training a model across participants without a central server or raw data exchange. DFL faces challenges due to statistical heterogeneity, as participants often possess data of different distributions reflecting local environments and user behaviors. Recent work has shown that the neural tangent kernel (NTK) approach, when applied to federated learning in a centralized framework, can lead to improved performance. We propose an approach leveraging the NTK to train client models in the decentralized setting, while introducing a synergy between NTK-based evolution and model averaging. This synergy exploits inter-client model deviation and improves both accuracy and convergence in heterogeneous settings. Empirical results demonstrate that our approach consistently achieves higher accuracy than baselines in highly heterogeneous settings, where other approaches often underperform. Additionally, it reaches target performance in 4.6 times fewer communication rounds. We validate our approach across multiple datasets, network topologies, and heterogeneity settings to ensure robustness and generalization. 

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3. Using pair programming as a collaborative learning approach to support students with learning disabilities via Zoom breakout rooms

   Li, L.; Xu, L.; He, Y.; He, W.; Watson, S.; Pribesh, S.; Major, D. (March 2021, Society for Information Technology & Teacher Education International Conference)

   SITE (Ed.)

   Peer learning through pair programming is a type of collaborative learning that involves students working in pairs to discuss computer programming concepts or develop codes to solve problems. The Zoom breakout room method is applied to teach pair programming in a virtual classroom during the COVID-19 environment. By facilitating pair programming in a virtual learning environment, we gained valuable experience in promoting collaborative learning, active learning, and problem-based learning activities in a cloud setting. 

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4. Understanding Collaborative Learning Processes and Outcomes Through Student Discourse Dynamics

   https://doi.org/10.1145/3706468.3706547  

   Park, Seehee; Nixon, Nia; D'Mello, Sidney; Shariff, Danielle; Choi, Jaeyoon (March 2025, ACM)

   This study explores the relation between students’ discourse dynamics and performance during collaborative problem-solving activities utilizing Linguistic Inquiry Word Count (LIWC). We analyzed linguistic variables from students’ communications to explore social and cognitive behavior. Participants include 279 undergraduate students from two U.S. universities engaged in a controlled lab setting using the physics related educational game named Physics Playground. Findings highlight the relationship between social and cognitive linguistic variables and student’s physics performance outcome in a virtual collaborative learning context. This study contributes to a deeper understanding of how these discourse dynamics are related to learning outcomes in collaborative learning. It provides insights for optimizing educational strategies in collaborative remote learning environments. We further discuss the potential for conducting computational linguistic modeling on learner discourse and the role of natural language processing in deriving insights on learning behavior to support collaborative learning. 

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5. Multi-Party Mixed Reality Interaction for Earth Sciences Education

   https://doi.org/10.1145/3294109.3302933  

   Chiou, Yan-Ming (March 2019, Thirteenth International Conference on Tangible, Embedded, and Embodied Interaction (TEI ’19).)

   Collaborative learning has been shown to be beneficial for children's learning performance, increasing the curiosity and intensity of the ability of cooperation. Mixed-Reality with collaborative learning is the trending research topic in the Human-Computer Interaction (HCI) area. Additionally, with the rise of attention to global warming which brings in more extreme weather and climate conditions, the earth science education would be one of the crucial topics for the next generation. Moreover, there are few augmented reality and mixed reality applications on earth science subject. In this paper, we propose a Mixed Reality Tornado Simulator which offers an earth science education in a collaborative setting. Students and the instructor can cooperate on learning the knowledge of the formation and its damage cause on human-built structures, farming, and vegetation by using our mixed reality application with the Microsoft HoloLens. Also, for evaluating the learning performance in this mixed reality setting, we propose to study the cognitive load while the student is learning the abstract knowledge in Earth Science. We will separate the student into a control group and experimental groups and use different teaching instruments to test the difference of cognitive load. 

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