Collaborative Learning Techniques Research Articles

PopFL: A scalable Federated Learning model in serverless edge computing integrating with dynamic pop-up network

With the rapid increase in the number of Internet-of-Things (IoT) devices, the massive volume of data creates significant challenges for traditional cloud-based solutions. These solutions often lead to high latency, increased operational costs, and limited scalability, making them unsuitable for real-time applications and resource-constrained environments. As a result, edge, and fog computing have emerged as viable alternatives, reducing latency and costs by processing data closer to its source. However, managing the flow of such vast and distributed data streams requires well-structured data pipelines to control the complete lifecycle—from data acquisition at the source to processing at the edge and fog layers, and finally storage and analytics in the cloud. To dynamically handle data analytics at varying distances from the source, often on heterogeneous hardware devices with collaborative learning techniques such as Federated Learning (FL). FL enables decentralized model training by leveraging the local data on Edge Devices (EDs), thereby preserving data privacy and reducing communication overhead with the cloud. However, FL faces critical challenges, including data heterogeneity, where the non-independent and identically distributed (non-IID) nature of data degrades model performance, and resource limitations on EDs, which lead to inefficiencies in training and biases in the aggregated models.To address these issues, we propose a novel FL solution, called Pop-Up Federated Learning (PopFL) in edge networks. This solution introduces hierarchical aggregation to reduce network congestion by distributing the aggregation tasks across multiple Fog Servers (FSs), rather than relying solely on centralized cloud aggregation. To further enhance participation and resource utilization at the edge, we incorporate the Stackelberg game model, which incentivizes EDs based on their contribution and resource availability. Additionally, PopFL employs a pop-up ad-hoc network for scalable and efficient communication between EDs and FSs, ensuring robust data transmission in dynamic network conditions. Extensive experiments conducted on three diverse datasets highlight the superior performance of PopFL compared to state-of-the-art FL techniques. The results show significant improvements in model accuracy, robustness, and fairness across various scenarios, effectively addressing the challenges of data heterogeneity and resource limitations. Through these innovations, PopFL paves the way for more reliable and efficient distributed learning systems, unlocking the full potential of FL in real-world applications where low latency and scalable solutions are critical.

Integration of UAVs and FANETs in Disaster Management: A Review on Applications, Challenges and Future Directions

ABSTRACTRapid growth and technological improvement in wireless communication, driven by engineers from various disciplines, have reached significant milestones. Unmanned Aerial vehicles (UAVs) and flying ad hoc networks (FANET) have undergone one of the biggest innovations. UAVs have drawn a lot of attention from research institutions. They are increasingly employed in various application fields, such as real‐time monitoring, precision farming, wireless coverage, military surveillance, climate monitoring, disaster surveillance, and monitoring and rescue operations. The primary characteristics of disasters are their unpredictability and the scarcity of resources in the affected areas. To reduce the loss of lives and livelihoods, disaster management has received much attention. Numerous methodologies and technologies have been developed to predict and handle disasters. UAVs are increasingly being used in disaster management. Additionally, artificial intelligence and collaborative machine learning techniques are gaining prominence among researchers, who are investigating the possibility of their use in disaster management tasks to better cope with the severe and frequently devastating effects of natural catastrophes. This paper provides a review of the relevant FANET research activities in disaster management and emerging artificial intelligence techniques, along with several observations and research challenges. The papers are categorized based on the disaster scenario‐related problems and their proposed solutions. FANET problems are receiving less attention from the research community, and the main challenges with FANETs are also highlighted. Finally, significant insights are presented that can aid in improving research related to the application of FANETs in disaster management.

Enhancing Analytical and Critical Thinking Skills through Reflective and Collaborative Learning: A Quasi-Experimental Study

In the context of contemporary higher education, cultivating students' analytical and critical thinking capabilities is essential for their achievement in both academic and professional arenas. Conventional pedagogical approaches frequently fail to adequately support the advancement of these sophisticated cognitive competencies. Recent studies suggest that reflective and collaborative learning methodologies can significantly strengthen these skills. Nevertheless, there remains a paucity of research concerning the overall effects of these educational strategies on university students. This initial investigation seeks to fill this void by assessing a blended instructional model that combines reflective and collaborative learning techniques to enhance the analytical and critical thinking abilities of students at Yunnan University of Traditional Chinese Medicine. The results indicate that this holistic approach not only improves cognitive skills but also fosters a more engaging and effective educational atmosphere. This research provides empirical validation for the incorporation of reflective and collaborative learning, offering valuable insights for curriculum design and instructional practices within higher education.

Sparse Personalized Federated Learning.

Federated learning (FL) is a collaborative machine learning technique to train a global model (GM) without obtaining clients' private data. The main challenges in FL are statistical diversity among clients, limited computing capability among clients' equipment, and the excessive communication overhead between the server and clients. To address these challenges, we propose a novel sparse personalized FL scheme via maximizing correlation (FedMac). By incorporating an approximated l1 -norm and the correlation between client models and GM into standard FL loss function, the performance on statistical diversity data is improved and the communicational and computational loads required in the network are reduced compared with nonsparse FL. Convergence analysis shows that the sparse constraints in FedMac do not affect the convergence rate of the GM, and theoretical results show that FedMac can achieve good sparse personalization, which is better than the personalized methods based on the l2 -norm. Experimentally, we demonstrate the benefits of this sparse personalization architecture compared with the state-of-the-art personalization methods (e.g., FedMac, respectively, achieves 98.95%, 99.37%, 90.90%, 89.06%, and 73.52% accuracy on the MNIST, FMNIST, CIFAR-100, Synthetic, and CINIC-10 datasets under non-independent and identically distributed (i.i.d.) variants).

Utilizing Integrated Think-Pair-Share and SSCS Techniques to Enhance Problem-Solving Skills in Mathematical Set among 10th Grade Students

This study aimed to investigate the impact of integrating Think-Pair-Share (TPS) and Search Solve Create Share (SSCS) techniques on the problem-solving skills of 10th-grade students within the framework of set theory, while also assessing participants' satisfaction with these methods. Conducted using a one-group experimental research design, the study involved 37 10th-grade students selected through cluster random sampling from a public school in Mahasarakham province, Thailand. Ethical considerations were meticulously observed throughout the study. Instruments utilized included a TPS and SSCS learning management plan, a Mathematics Set Problem Solving Ability Test, a Satisfaction Questionnaire, and Rubric Scoring. Data analysis employed percentage, mean score, standard deviation, and a one-sample t-test comparing to a predetermined criterion of 75% of the maximum score for each test. The results demonstrated that the integrated TPS and SSCS techniques were effective in developing students' problem-solving abilities and provided satisfying learning experiences. This underscores the efficacy of collaborative learning techniques, particularly in enhancing problem-solving skills. Additionally, the study highlights the potential of integrating various collaborative methods to achieve favorable outcomes in mathematics education.

A Collaborative Learning Technique for Improved Email Security

A Collaborative Learning Technique for Improved Email Security

Robust and privacy-preserving collaborative training: a comprehensive survey

Increasing numbers of artificial intelligence systems are employing collaborative machine learning techniques, such as federated learning, to build a shared powerful deep model among participants, while keeping their training data locally. However, concerns about integrity and privacy in such systems have significantly hindered the use of collaborative learning systems. Therefore, numerous efforts have been presented to preserve the model’s integrity and reduce the privacy leakage of training data throughout the training phase of various collaborative learning systems. This survey seeks to provide a systematic and comprehensive evaluation of security and privacy studies in collaborative training, in contrast to prior surveys that only focus on one single collaborative learning system. Our survey begins with an overview of collaborative learning systems from various perspectives. Then, we systematically summarize the integrity and privacy risks of collaborative learning systems. In particular, we describe state-of-the-art integrity attacks (e.g., Byzantine, backdoor, and adversarial attacks) and privacy attacks (e.g., membership, property, and sample inference attacks), as well as the associated countermeasures. We additionally provide an analysis of open problems to motivate possible future studies.

Embedding student-centred active learning environment with upside-down pedagogies in teaching practice: What works for psychology?

Student-Centred Active Learning Environment with Upside-down Pedagogies (SCALE-UP) is a set of collaborative learning techniques that are shown to reduce the continuation and awarding gaps between students of Black, Asian and other Minority Ethnicities (BAME) and White students when SCALE-UP is implemented throughout every module in an undergraduate course. The aim of this project was to evaluate the impact of SCALE-UP on a level-four Psychology module consisting of 140 students to inform considerations of wider implementation. Although the grades and gaps on this single module did not differ significantly from other modules, written feedback from students indicated that students appreciated the authenticity of interactions afforded by SCALE-UP. Staff recognised the growing confidence of their students when expressing their views in class, peer interactions and group activities. These are indicators of the potential benefits that large-scale implementation of SCALE-UP could contribute to closing or removing continuation and awarding gaps.

When and how do peers stimulate engaging in desirable difficulties: Student perspectives on the effectiveness of supplemental instruction

Supplemental instruction, also known as Peer Assisted Study Sessions (SI-PASS), is a well-established form of peer learning that has been implemented in higher education institutions across the globe and that coincides with learning gains for participants. While the effects on learning gains have been extensively studied with quasi-experiments, the underlying mechanisms that make SI-PASS effective are less well understood. This study explored what benefits students thought SI-PASS offered and through which mechanisms. We studied this by interviewing 14 students who participated in SI-PASS during a field experiment that reliably found a significant impact of SI-PASS on performance. The students were asked to expand on if and why they thought SI-PASS was effective. Thematic analysis and independent coding indicated an interplay of three main drivers. SI-PASS was experienced as effective because it stimulated the use of effective study techniques and social learning. These drivers were facilitated and enhanced by a pedagogical climate that lowered the threshold to engage in collaborative learning and effective study techniques. These findings could help pinpoint what elements should be highlighted during the preparation of SI-leaders and what aspects should be monitored and tested when implementing or studying SI-PASS.

Dynamic heterogeneous federated learning with multi-level prototypes

Federated learning shows promise as a privacy-preserving collaborative learning technique. Existing research mainly focuses on skewing the class distribution across clients. However, most approaches suffer from catastrophic forgetting and classifier shift, mainly when the global distribution of all classes is extremely unbalanced and the data distribution of the client dynamically evolves over time. In this paper, we study the Dynamic Heterogeneous Federated Learning, which addresses the practical scenario where heterogeneous data distributions exist among different clients and dynamic tasks within the client. Accordingly, we propose a novel federated learning framework named Federated Multi-Level Prototypes and design federated multi-level regularizations. To mitigate classifier shift, we construct semantic prototypes to provide fruitful generalization knowledge. To maintain the model stability and consistency convergence, three regularizations are introduced as training losses, i.e., prototype-based regularization, semantic prototype-based regularization, and federated inter-task regularization. Extensive experiments show that the proposed method achieves advanced performance in various settings.

ENHANCING BOTANICAL EDUCATION: EXPLORING INTERACTIVE METHODS IN TEACHING BOTANY

Botanical education plays a crucial role in fostering an understanding of plant life, its significance, and its intricate complexities. Traditional teaching methods often rely on passive learning approaches, which may not fully engage students or facilitate deeper comprehension. In recent years, there has been a growing emphasis on employing interactive teaching methods to enhance botanical education. This article reviews various interactive methods in teaching botany, including hands-on activities, digital simulations, field trips, and collaborative learning techniques. We discuss the benefits of incorporating interactive methods in botanical education, such as improved student engagement, enhanced retention of information, and the development of critical thinking skills. Additionally, we explore challenges associated with implementing interactive teaching methods and provide recommendations for overcoming these obstacles. By embracing interactive approaches, educators caninspire a greater appreciation for the fascinating world of plants and cultivate a new generation of botanists equipped with the knowledge and skills to address emerging environmental challenges.

SIM-FED: Secure IoT malware detection model with federated learning

Many IoT devices are presently in use without sufficient security measures. The vulnerability of these devices to malware highlights the necessity for effective methods to identify malicious activity while protecting data privacy. Federated Learning is a collaborative learning technique that can create a fair model while keeping the local data private. To this end, this paper presents the SIM-FED, a novel model for detecting malware in IoT devices. This model utilizes both deep learning and federated learning algorithms. The SIM-FED offers a privacy-preserving solution for IoT malware detection, without sharing data, enhancing security in distributed environments. The model utilizes a lightweight one-dimensional CNN with optimized hyperparameters, reducing preprocessing, and computational overhead. Various federated aggregation strategies are evaluated, and the FedAvg strategy is selected to integrate the results of local models in the proposed model. Moreover, the model's resilience against white-box and black box cyber-attacks is assessed, revealing minimal performance degradation and highlighting its robustness, which is often overlooked in previous studies. The performance of the SIM-FED is assessed by conducting a series of experiments using the IoT-23 dataset. The results show that the SIM-FED outperforms other deep learning and federated learning models in terms of all evaluation metrics, achieving a remarkable 99.52 % accuracy.

Integrating Collaborative Learning and Advanced Technology in Industry 5.0: A Systematic Mapping Study and Taxonomy

Learning evolves through human interactions and adapting to technological advancements. The fourth industrial revolution elevated the importance of machines in improving competitiveness, efficiency, and product quality. However, this emphasis on technology overshadowed the human-centric perspective. In contrast, Industry 5 aims to foster collaboration between humans, robots, and digital systems. Our study utilized systematic mapping to explore collaboration in I5. Specifically, we investigated how collaborative learning occurs in industrial workplaces. The main objective was to identify prevalent collaborative learning techniques, technologies, challenges, and data types in I5 environments. The results reveal various collaborative learning approaches, highlighting their potential to improve productivity, innovation, and worker participation in I5. The study uncovers significant trends, such as the increasing reliance on digital platforms and Artificial Intelligence (AI)-driven tools, which facilitate collaborative learning while posing unique challenges. Our taxonomy provides a structured framework for understanding these dynamics, serving as a valuable guide for practitioners and researchers. The findings underscore the necessity for adaptive learning strategies and the integration of advanced technologies to promote effective collaboration in industrial settings. This research contributes to the theoretical understanding of collaborative learning in I5 and offers practical insights for its implementation, thereby supporting the evolution of industry practices in this new technological era.

Improving Language Acquisition: Effective Numeracy Literacy Strategies in Early Childhood

This article explores various strategies aimed at improving language acquisition in children. Realizing the important role of language in human life can be stimulated from an early age through the application of numeracy literacy in children. This research explores innovative methodologies and approaches to cater to diverse learning styles. The authors emphasize the importance of creating an immersive linguistic environment that encourages speaking and writing proficiency. This research evaluates the impact of incorporating technology as a tool to enhance the language learning experience. Utilizing multimedia resources, online platforms, and interactive applications, this research examines how these digital elements can engage students and strengthen language skills. In addition, this article discusses the integration of cultural components into language lessons, emphasizing the role of cultural context in language comprehension and expression. These findings contribute to the ongoing discourse regarding optimizing language teaching methods to children in enriching vocabulary and language learning experiences through the application of literacy and numeracy in children's daily lives. insights that can inform more effective strategies for achieving sustainability goals. Next, the authors investigate the effectiveness of collaborative learning techniques in language acquisition. Children's learning activities, peer interactions, and communicative exercises are explored as means to encourage active participation and increase language retention in children. This study also examines the role of feedback mechanisms in shaping children's language development, emphasizing timely and constructive feedback as an important element in the learning process. In conclusion, this article provides valuable insights into effective child language acquisition strategies. By combining technological tools, cultural elements, collaborative activities, and feedback mechanisms, educators can create dynamic learning environments that meet the diverse needs of young children.

The Preservation and Literacy Transmission of the Mor Lam Khon Kaen Style by the Rattanasin Intathairat Group

This study focuses on the preservation and literacy transmission of the Mor Lam Khon Kaen style by the Rattanasin Intathairat Group and presents insightful findings in three knowledge-related aspects. In terms of knowledge definition, the Mor Lam Khon Kaen performance encompasses the preservation of its distinct identity, the collaborative exchange of knowledge among performers facilitated by Mrs. Chawala, the composer, and the meticulous memorization process of Klon Lam poems. Knowledge management within the performance involves the leadership of Mrs. Chawala, who guides the ensemble to ensure authentic Mor Lam Phuen style preservation over four evolving periods. This process entails systematic storage and retrieval through Klon Lam compilation, utilizing digital platforms for dissemination, and employing collaborative learning techniques. In the literacy transmission process, the Rattanasin Intathairat Group’s inclusive approach welcomes disciples, emphasizes Klon Lam memorization, and adapts the timeframe to individual progress. The study’s comprehensive insights shed light on the intricate interplay of preservation, literacy transfer, and community engagement within the Mor Lam tradition.

Implementing Universal Design through augmented-reality game-based learning

Technology integration in higher education (HE) provides educators with the opportunity to design stimulating learning environments, especially in design and engineering education (DEE) where it plays a unique role in nurturing creativity, problem-solving and innovation. This study investigates the integration of an augmented reality (AR) educational game in DEE, focusing on the teaching of Design for Manufacturing and Assembly (DfMA) principles. The primary aim of this research is to enhance traditional DfMA teaching and learning (T&L) practices by applying innovative T&L strategies. The resulting AR DfMA game, developed using digital game-based learning, microlearning and collaborative learning techniques, aligns with the Universal Design for Learning framework to create an inclusive learning environment that encourages participation from all students and supports several learning styles.The study tests the individual features designed for personal study opportunities to discover which elements are optimal in that environment, and then on the proposed in-class group components of the AR game, involving 68 participants from the appropriate program, though it is not currently implemented as part of a module. We utilize a mixed methods approach to examine the students’ experience and identify key design features that contribute to an inclusive educational experience. The findings highlight positive student experiences, and preferences for hands-on engagement, multimodal content, and collaborative activities. The AR DfMA game also has the potential to enhance intrinsic motivation and create an active and inclusive learning environment. Challenges and areas for improvement are also discussed.

Collaborative Learning: An Assessment of the Students’ Essay Writing Skills

The present experimental study aimed to assess the students’ essay-writing skills at the graduate level. Writing is highly pertinent among all four English language skills related to communication. The results of the students who were taught in the classroom through collaborative learning techniques were compared with the results of the students who were taught through traditional methodology. Female students at the graduation level from a government college in the Chakwal district of Punjab province were considered the population of the present study. A simple random sampling technique was used to select 30 students. The sample of 30 students was divided into experimental and control groups by dividing 15 students in each group. The traditional methodology was used to teach the students of the control group, whereas the collaborative learning technique was used to teach the treatment group students. Both groups were observed twice. The results were analyzed using the independent sample t-test. The findings revealed that the students of both groups performed almost the same in the pretest, whereas a significant difference was observed in the mean score of both groups in the post-test. It was recommended that the traditional teaching methodologies be replaced with collaborative learning techniques.

Enhancing English Language Acquisition: Strategies for Effective Classroom Implementation

This article explores various strategies aimed at enhancing English language acquisition in classroom settings. Recognizing the importance of effective language instruction, the study delves into innovative methodologies and approaches to cater to diverse learning styles. The authors emphasize the significance of creating an immersive linguistic environment that fosters both spoken and written proficiency.
 The research evaluates the impact of incorporating technology as a tool to augment language learning experiences. Utilizing multimedia resources, online platforms, and interactive applications, the study examines how these digital elements can engage students and reinforce language skills. Additionally, the article addresses the integration of cultural components into language lessons, emphasizing the role of cultural context in language comprehension and expression.
 Furthermore, the authors investigate the effectiveness of collaborative learning techniques in language acquisition. Group activities, peer interactions, and communicative exercises are explored as means to encourage active participation and enhance language retention. The study also scrutinizes the role of feedback mechanisms in shaping language development, emphasizing timely and constructive feedback as a pivotal element in the learning process.
 In conclusion, this article provides valuable insights into strategies for effective English language acquisition within classroom settings. By blending technological tools, cultural elements, collaborative activities, and feedback mechanisms, educators can create dynamic learning environments that cater to the diverse needs of students. The findings contribute to the ongoing discourse on optimizing language instruction methods and offer practical implications for educators seeking to enrich the English language learning experience

Teacher Skills in Leading Small Group Discussions to Class III Students of SD Inpres 10/73 Pandu

The purpose of this research is to investigate the abilities of conducting effective small group conversations in the setting of class III students, specifically at SD Inpres 10/73 Pandu. Small group discussions are a collaborative learning technique that can improve student active engagement while also developing critical thinking abilities. This study's research methodology is a qualitative approach using case study research. Interviews and classroom observations with students and instructors were used to obtain data. The study's findings indicate that there are various extremely successful strategies of conducting a small group conversation to students/I class III SD Inpres 10/73 Pandu, including providing discussion goals. As discussion guides, we must decide what we want to accomplish in the conversation. Prepare conversation themes and material, prepare discussion topics or material to be discussed, and gather appropriate material or resources. Provide chances for all kids and guarantee that all students may engage in the conversation. Pay close attention to the comments of numerous pupils who wish to present their points or ask questions. The last one employs tactics for leading the appropriate groups and assisting pupils in broadening their thoughts.

Federated Semi-Supervised Learning for Medical Image Segmentation via Pseudo-Label Denoising.

Distributed big data and digital healthcare technologies have great potential to promote medical services, but challenges arise when it comes to learning predictive model from diverse and complex e-health datasets. Federated Learning (FL), as a collaborative machine learning technique, aims to address the challenges by learning a joint predictive model across multi-site clients, especially for distributed medical institutions or hospitals. However, most existing FL methods assume that clients possess fully labeled data for training, which is often not the case in e-health datasets due to high labeling costs or expertise requirement. Therefore, this work proposes a novel and feasible approach to learn a Federated Semi-Supervised Learning (FSSL) model from distributed medical image domains, where a federated pseudo-labeling strategy for unlabeled clients is developed based on the embedded knowledge learned from labeled clients. This greatly mitigates the annotation deficiency at unlabeled clients and leads to a cost-effective and efficient medical image analysis tool. We demonstrated the effectiveness of our method by achieving significant improvements compared to the state-of-the-art in both fundus image and prostate MRI segmentation tasks, resulting in the highest Dice scores of 89.23% and 91.95% respectively even with only a few labeled clients participating in model training. This reveals the superiority of our method for practical deployment, ultimately facilitating the wider use of FL in healthcare and leading to better patient outcomes.