Collaborative Method Research Articles

Teaching science, technology and society in blended learning large classes: a qualitative study of the Normale lecture model

Purpose This study aims to investigate teaching methodologies, best practices, and challenges encountered in delivering a science, technology, and society (STS) course using the Normale Lecture model at Y University. Design/methodology/approach Using a qualitative descriptive approach, data was collected through surveys distributed to 202 first-year students enrolled in an STS course. Ethical clearance was secured prior to data collection. Findings Effective strategies in large-class STS course included a combination of lectures, collaborative group activities, asynchronous assignments, and digital tools, such as Quizizz, to enhance engagement and provide structured, interactive learning experiences. Students perceive best practices, such as collaborative and interdisciplinary projects and active learning methods. Students face challenges such as limited interaction with instructors, fast-paced content delivery, technological issues, lack of hands-on activities, and increased stress and anxiety in large class settings. Practical implications Recommendations include reducing class size, enhancing interactive and practical activities, providing clear communication and feedback, and optimizing technology use to foster engagement and improve learning outcomes in large classes. Originality/value This study contributes to the growing body of research on large-class pedagogy in the Philippine higher education context, offering insights into adapting blended learning for diverse and large class sizes. This study focuses on a single institution and course, which limits its generalizability.

A Generative Artificial Intelligence (AI)-Based Human-Computer Collaborative Programming Learning Method to Improve Computational Thinking, Learning Attitudes, and Learning Achievement

A Generative Artificial Intelligence (AI)-Based Human-Computer Collaborative Programming Learning Method to Improve Computational Thinking, Learning Attitudes, and Learning Achievement

Mechanism–Data Collaboration for Characterizing Sea Clutter Properties and Training Sample Selection

Multi-feature-based maritime radar target detection algorithms often rely on statistical models to accurately characterize sea clutter variations. However, it is a big challenge for these models to accurately characterize sea clutter due to the complexity of the marine environment. Moreover, the distribution of training samples captured from dynamic observation conditions is imbalanced. These multi-features extracted from inaccurate models and imbalanced data lead to overfitting or underfitting and degrade detection performance. To tackle these challenges, this paper proposes a mechanism–data collaborative method using the scattering coefficient as a representative feature. By establishing a mapping relationship between measured data and empirical values, the classical model is piecewise fitted to the measured data. A fusion strategy is then used to compensate for interval discontinuities, enabling accurate characterization of clutter properties in the current maritime environment. Based on the characterized clutter properties, a hybrid feature selection strategy is further proposed to construct a diverse and compact training sample set by integrating global density distribution with local gradient variation. The experiments based on field data are included to evaluate the effectiveness of the proposed method including sea clutter characterization accuracy and training sample selection across various scenarios. Experimental results demonstrate that the proposed method provides a more accurate representation of sea clutter characteristics. Moreover, the detectors trained with the proposed training samples exhibit strong generalization capability across diverse maritime environments under the condition of identical features and classifiers. These achievements highlight the importance of accurate sea clutter modeling and optimal training sample selection in improving target detection performance and ensuring the reliability of radar-based maritime surveillance.

Collaborative Hunting Method of Multi-AUV in 3-D IoUT: Searching, Tracking, and Encirclement Keeping

Collaborative Hunting Method of Multi-AUV in 3-D IoUT: Searching, Tracking, and Encirclement Keeping

Efficacy and Safety of Phosphate-Lowering Agents for Adult Patients with CKD Requiring Dialysis: A Network Meta-Analysis.

It is necessary to update the evidence of each phosphate-lowering agent on dialysis patients. From the CENTRAL, MEDLINE, EMBASE, and ClinicalTrial.gov databases, randomized controlled trials (RCTs) using oral phosphate-lowering agents on adult patients requiring maintenance dialysis were extracted. The treatment period was required for eight or more weeks, and the risk of bias was assessed according to the Cochrane Collaboration method. The outcomes were all-cause mortality, cardiovascular mortality, gastrointestinal (GI) events, fracture, coronary artery calcium score (CACS), serum calcium, phosphate, intact parathyroid hormone (iPTH), and bicarbonate levels. A network meta-analyses using multivariate random-effects models were performed for assessing the comparative effectiveness. The ranking of the phosphate-lowering agents was assessed using a surface under the cumulative ranking curve (SUCRA). A total of 70 RCTs involving 15,551 participants were included. Eleven phosphate-lowering agents including calcium-based agents, sevelamer, bixalomer, lanthanum, sucroferric oxyhydroxide, ferric citrate, tenapanor, magnesium, nicotinamide, aluminum, and sucralfate were assessed. Sevelamer was significantly associated with lower all-cause mortality compared with calcium-based agents [risk ratio (95% Confidence Interval {CI}): 0.59 (0.37-0.94)], and sucroferric oxyhydroxide and tenapanor were estimated to rank high in terms of lowering all-cause mortality based on the SUCRA. The risk of GI events was the highest with nicotinamide, followed by sucroferric oxyhydroxide. Compared with calcium-based agents, CACS were significantly lower among those on lanthanum and sucroferric oxyhydroxide [standardized mean difference (95% CI): -0.26 (-0.52 to -0.01) and -0.50 (-0.95 to -0.06), respectively]. Serum calcium levels were higher and serum iPTH levels were lower in patients treated with calcium-based agents. Except for sevelamer, serum bicarbonate levels for all other agents were higher compared with placebo. Compared with calcium-based agents, sevelamer was associated with lower all-cause mortality, and sucroferric oxyhydroxide and lanthanum were associated with slower progression of CACS. Potential benefits and harms should be considered when selecting phosphate-lowering agents (PROSPERO: CRD42022328388).

DSRC: Learning Density-Insensitive and Semantic-Aware Collaborative Representation Against Corruptions

As a potential application of Vehicle-to-Everything (V2X) communication, multi-agent collaborative perception has achieved significant success in 3D object detection. While these methods have demonstrated impressive results on standard benchmarks, the robustness of such approaches in the face of complex real-world environments requires additional verification. To bridge this gap, we introduce the first comprehensive benchmark designed to evaluate the robustness of collaborative perception methods in the presence of natural corruptions typical of real-world environments. Furthermore, we propose DSRC, a robustness-enhanced collaborative perception method aiming to learn Density-insensitive and Semantic-aware collaborative Representation against Corruptions. DSRC consists of two key designs: i) a semantic-guided sparse-to-dense distillation framework, which constructs multi-view dense objects painted by ground truth bounding boxes to effectively learn density-insensitive and semantic-aware collaborative representation; ii) a feature-to-point cloud reconstruction approach to better fuse critical collaborative representation across agents. To thoroughly evaluate DSRC, we conduct extensive experiments on real-world and simulated datasets. The results demonstrate that our method outperforms state-of-the-art collaborative perception methods in both clean and corrupted conditions.

FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning

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.

Pre-Trained Vision-Language Models as Noisy Partial Annotators

In noisy partial label learning, each training sample is associated with a set of candidate labels, and the ground-truth label may be contained within this set. With the emergence of powerful pre-trained vision-language models, e.g. CLIP, it is natural to consider using these models to automatically label training samples instead of relying on laborious manual annotation. In this paper, we investigate the pipeline of learning with CLIP annotated noisy partial labels and propose a novel collaborative consistency regularization method, in which we simultaneously train two neural networks, which collaboratively purify training labels for each other, called Co-Pseudo-Labeling, and perform consistency regularization between label and representation levels. For instance-dependent noise that embodies the underlying patterns of the pre-trained model, our method employs multiple mechanisms to avoid overfitting to noisy annotations, effectively mines information from potentially noisy sample set while iteratively optimizing both representations and pseudo-labels during the training process. Comparison experiments with various kinds of annotations and weakly supervised methods, as well as other pre-trained model application methods demonstrates the effectiveness of method and the feasibility of incorporating weakly supervised learning into the distillation of pre-trained models.

Balanced Adaptive Subspace Collaboration for Mixed Pareto-Lexicographic Multi-Objective Problems with Priority Levels

Multi-objective Optimization Problems (MOPs) where objectives have different levels of importance in decision-making, known as Mixed Pareto-Lexicographic MOPs with Priority Levels (PL-MPL-MOPs), are increasingly prevalent in real-world applications. General-purpose Multi-Objective Evolutionary Algorithms (MOEAs) that treat all objectives equally not only increase the workload of decision-making but also suffer from computational inefficiencies due to the necessity of generating many additional solutions. Conversely, strictly adhering to Priority Levels (PLs) during optimization can easily result in premature convergence within some PLs. To address this issue, we suggest an effective Balanced Adaptive Subspace Collaboration (BASC) method in this paper. Specifically, this method decomposes the search space into sub-fronts based on PLs and utilizes a sampling mechanism that operates exclusively within subspaces formed by sub-fronts at the same PL to generate new solutions, thereby emphasizing the exploitation of individual PLs. Furthermore, a set of parameters is employed to control the strictness of adherence to each PL, with these parameters adaptively adjusted to balance exploration across different PLs. The two mechanisms are then collaboratively integrated into MOEAs. Comprehensive experimental studies on benchmark problems and a set of complex job-shop scheduling problems in semiconductor manufacturing demonstrate the competitiveness of the proposed method over existing methods.

CoDTS: Enhancing Sparsely Supervised Collaborative Perception with a Dual Teacher-Student Framework

Current collaborative perception methods often rely on fully annotated datasets, which can be expensive to obtain in practical situations. To reduce annotation costs, some works adopt sparsely supervised learning techniques and generate pseudo labels for the missing instances. However, these methods fail to achieve an optimal confidence threshold that harmonizes the quality and quantity of pseudo labels. To address this issue, we propose an end-to-end Collaborative perception Dual Teacher-Student framework (CoDTS), which employs adaptive complementary learning to produce both high-quality and high-quantity pseudo labels. Specifically, the Main Foreground Mining (MFM) module generates high-quality pseudo labels based on the prediction of the static teacher. Subsequently, the Supplement Foreground Mining (SFM) module ensures a balance between the quality and quantity of pseudo labels by adaptively identifying missing instances based on the prediction of the dynamic teacher. Additionally, the Neighbor Anchor Sampling (NAS) module is incorporated to enhance the representation of pseudo labels. To promote the adaptive complementary learning, we implement a staged training strategy that trains the student and dynamic teacher in a mutually beneficial manner. Extensive experiments demonstrate that the CoDTS effectively ensures an optimal balance of pseudo labels in both quality and quantity, establishing a new state-of-the-art in sparsely supervised collaborative perception.

A Cross-Regional Cost Collaborative Optimization Model Based on a BIM Lightweight Engine and Mobile Edge Computing

With the rapid development of building information modeling (BIM) technology, its application in construction projects has become widespread, particularly in the collaborative work during the design, construction, and operation phases. However, traditional BIM models, due to their large data volume and complex structure, present significant challenges in data transmission and real-time processing during cross-regional collaboration. Meanwhile, mobile edge computing (MEC), as an emerging computational paradigm, reduces data transmission latency and enhances processing efficiency by deploying computing and storage resources at the network edge. The integration of a BIM lightweight engine and MEC offers a more efficient solution for cross-regional cost collaboration. While previous studies have explored the combination of BIM with cloud and edge computing, most have focused on single-region scenarios, with the lightweight methods for BIM data and MEC deployment for cross-regional collaboration still lacking efficiency and practicality. Furthermore, existing optimization algorithms often fail to fully address the unique demands of cross-regional collaboration, leading to suboptimal overall performance. To address these issues, a cross-regional cost collaborative optimization model based on the BIM lightweight engine and MEC was proposed in this study. The main content of this study includes a) the design of a BIM lightweight engine based on point cloud compression, which improves data transmission efficiency through efficient compression techniques; b) the investigation of the MEC server deployment strategy in cross-regional cost collaborative scenarios, optimizing the distribution and scheduling of computational resources; and c) the development of a cross-regional cost collaborative optimization method based on an improved genetic algorithm to enhance the intelligence and efficiency of resource scheduling. This research provides not only a new theoretical framework for the integration of BIM and MEC but also technical support for cross-regional collaborative optimization in practical engineering.

A Methodology for Situation Assessing of Space-Based Information Networks

This paper proposes a cloud-edge collaborative method for operational situation assessment to ensure the efficient and reliable operation of space-based information networks. By analyzing time-varying network topology characteristics, we establish a 14-dimensional assessment factor system that can characterize the operational situation of space-based information networks. Considering the resource constraints of satellites, traditional on-orbit assessment methods often lead to high latency and excessive resource consumption. A cloud-edge collaborative situation assessment method is introduced to enhance assessment efficiency. The proposed method first applies principal component analysis for dimensionality reduction, followed by pre-labeling situational factor data using an improved K-means clustering algorithm. The on-orbit assessment of individual satellites is then performed using a particle swarm optimization-support vector machine algorithm. Finally, a fusion assessment of the space-based information networks is conducted at the ground cloud center, incorporating situation weighting factors. Experimental results demonstrate that the proposed cloud-edge collaborative method improves assessment accuracy by 13% compared to baseline methods, significantly reduces average completion time, and maintains stable performance in large-scale satellite constellations.

Multi-view collaborative ensemble classification for EEG signals based on 3D second-order difference plot and CSP.

EEG signal analysis methods based on electrical source imaging (ESI) technique have significantly improved classification accuracy and response time. However, for the refined and informative source signals, the current studies have not fully considered their dynamic variability in feature extraction and lacked an effective integration of their dynamic variability and spatial characteristics. Additionally, the adaptability and complementarity of classifiers have not been considered comprehensively. These two aspects lead to the issue of insufficient decoding of source signals, which still limits the application of brain-computer interface (BCI). To address these challenges, this paper proposes a multi-view collaborative ensemble classification method for EEG signals based on three-dimensional second-order difference plot (3D SODP) and common spatial pattern (CSP). First, EEG signals are mapped to the source domain using the ESI technique, and then the source signals in the region of interest (ROI) are obtained. Next, features from three viewpoints of the source signals are extracted, including 3D SODP features, spatial features, and the weighted fusion of both. Finally, the extracted multi-view features are integrated with subject-specific sub-classifier combination, and a voting mechanism is used to determine the final classification. The results show that the proposed method achieves classification accuracy of 81.3% and 82.6% respectively in two sessions of the OpenBMI dataset, which is nearly 5% higher than the state-of-the-art method, and maintains the analysis response time required for online BCI. This paper employs multi-view feature extraction to fully capture the characteristics of the source signals and enhances feature utilization through collaborative ensemble classification. The results demonstrate high accuracy and robust performance, providing a novel approach for online BCI.

Signal Control Method for Urban Road Networks Based on Dynamic Identification of Critical Nodes

Ensuring the reliable operation of all nodes in a road network is often challenging due to the influence of managed resources and other dynamic factors. This study proposes a method for identifying critical nodes based on multi-attribute decision-making, aimed at enhancing traffic efficiency and reliability. By utilizing dynamic traffic flow data and real-time ranking of node criticality, an adaptive signal optimization approach was developed to establish a collaborative control method for road network signals. First, a quantitative analysis was conducted to evaluate the impact of road network topology, traffic volume, and travel time reliability, enabling a comprehensive ranking of critical nodes. Subsequently, based on real-time traffic flow and critical node rankings, a signal collaborative control method was established to optimize travel time reliability while mitigating congestion and resource inefficiencies. Case analysis revealed that nodes with higher OD (origin–destination) pairs do not necessarily exhibit high traffic flow or criticality, underscoring the importance of targeted signal control strategies. The results demonstrate that the proposed optimization method effectively improves the dynamic reliability and operational efficiency of road networks while contributing to sustainable transportation by enhancing adaptability to traffic fluctuations. This study provides theoretical and practical references for advancing sustainable traffic management and supporting the transition to smarter transportation systems.

LINGKUNGAN BELAJAR ABAD 21 PADA PEMBELAJARAN PENDIDIKAN KEWARGANEGARAAN (PKN) DI SEKOLAH DASAR NEGERI (SDN) 3 TAPA, KABUPATEN BONE BOLANGO

This study elaborates the concept of 21st century learning environment applied in Civics learning in Elementary Schools, learning strategies used in 21st century learning environment in Civics learning in Elementary Schools, and the impact of implementing 21st century learning environment on students’ motivation, learning outcomes, and character in Civics learning? This study aims to analyze the implementation of 21st century learning environment culture in Civics learning in Elementary School 3 Tapa, Bone Bolango Regency. This study focuses on three main aspects: the concept of 21st century learning environment applied, learning strategies used, and its impact on students’ motivation, learning outcomes, and character. The research method used is qualitative with data collection techniques in the form of observation, in-depth interviews, and documentation. The results of the study indicate that the implementation of educational technology, such as the use of applications and social media, significantly increases student engagement in the learning process. Various learning strategies, such as project-based learning and collaborative methods, successfully create an interactive and enjoyable learning atmosphere, which encourages students to actively participate. The positive impact of implementing this 21st century learning environment is seen in the increase in students’ learning motivation and academic outcomes, as well as the development of better civic character. This study recommends the need for additional training for teachers to utilize technology and innovative learning methods in improving the quality of education in the modern era.

The effect of gamification in entrepreneurship and business education on pharmacy students’ self-efficacy and learning outcomes

PurposeThe current study aimed to evaluate the effect of an entrepreneurship and business education program using gamification on enhancing self-efficacy and the learning of entrepreneurial and business skills among pharmacy students.MethodThis quasi-experimental study was carried out at Shahid Sadoughi University of Medical Sciences. The participants, including 5th and 6th-year pharmacy students (N = 86), were entered. The objective of the intervention was to improve entrepreneurial and business skills and develop business models in the pharmacy field. During the intervention, students collaboratively engage in a puzzle of a business plan as a project, integrating elements including risk, rewards, completion, multiplayer gameplay, and scenario-based challenges across various pharmaceutical fields. Students were tasked with completing canvas business plan components for their proposals, which were subsequently evaluated by an expert. The students’ self-efficacy was assessed using a 23-item questionnaire, and their learning was measured through a project-based examination focused on the business plan. The collected data were analyzed using descriptive statistics, T-tests, Pearson’s ANOVA, and regression analysis, with a significance level considered p-value < 0.05.ResultsThe students’ learning scores were reported as 89.93 ± 5.66. The scores of students’ self-efficacy domains before (46.11 ± 2.59) and after the educational program (69.58 ± 2.04) had a significant difference. (p-value = 0.0001). The results of the regression test showed that students’ learning scores have a substantial relationship with initiating investor relationships (p = 0.005) and coping with unexpected challenges (p = 0.03). After controlling the demographic information, the domains of initiating investor relationships (p = 0.004) and developing new products and market opportunities (p = 0.038) significantly correlate with a student’s entrepreneurship score.ConclusionThe educational program using the gamification method with a focus on business models significantly enhances students’ self-efficacy in pharmaceutical entrepreneurship. This improvement was observed across all domains assessed, indicating a comprehensive impact on students’ self-perception of their ability to succeed in entrepreneurship. Further study of entrepreneurship development and business education using a game-based learning method as a collaborative and active method in pharmacy education is recommended.Clinical trial numberNot applicable.

Point of Interest Recommendation using Implicit Trust based on Combining Ratings and Check-ins of Smartphone Users

This paper introduces a hybrid model called Implicit Trust based on Combining point-of-interest Ratings and user Check-ins (ITCRC) to address the cold-start challenges commonly associated with trust-based collaborative filtering methods. The model combines Point of Interest (POI) ratings and user check-ins to estimate implicit trust, facilitating location recommendations in a Location-Based Social Network (LBSN). In the Yelp dataset, the ITCRC model's trust and prediction matrices are calculated using Trust based on Ratings (TR), Trust derived from Check-ins (TC), and Trust based on the Hybridization of ratings and check-ins (TH), as well as three approaches derived by adapting O'Donovan's trust formula to the LBSN context. These six approaches are then compared using sparsity metrics and evaluation parameters such as RMSE, precision, and recall. The comparisons revealed that the TH approach significantly reduces the data sparsity of the prediction matrix by 36.08%, the TR and TC approaches improve the relevance of the recommendations (0.77% of precision and 0.99% of recall), and the OR, OC, and OH approaches improve the prediction accuracy by 0.2% in terms of RMSE.

Enhancing Recommender Systems through Imputation and Social-Aware Graph Convolutional Neural Network.

Recommendation systems are vital tools for helping users discover content that suits their interests. Collaborative filtering methods are one of the techniques employed for analyzing interactions between users and items, which are typically stored in a sparse matrix. This inherent sparsity poses a challenge because it necessitates accurately and effectively filling in these gaps to provide users with meaningful and personalized recommendations. Our solution addresses sparsity in recommendations by incorporating diverse data sources, including trust statements and an imputation graph. The trust graph captures user relationships and trust levels, working in conjunction with an imputation graph, which is constructed by estimating the missing rates of each user based on the user-item matrix using the average rates of the most similar users. Combined with the user-item rating graph, an attention mechanism fine tunes the influence of these graphs, resulting in more personalized and effective recommendations. Our method consistently outperforms state-of-the-art recommenders in real-world dataset evaluations, underscoring its potential to strengthen recommendation systems and mitigate sparsity challenges.

An online simulation-based collaborative optimization control method for solar thermal energy, heat pumps and building operations

An online simulation-based collaborative optimization control method for solar thermal energy, heat pumps and building operations

A heterogeneous robot collaborative search method for radioactive sources

A heterogeneous robot collaborative search method for radioactive sources