Collaborative Method Research Articles

Evaluating the effectiveness of flipped learning in an upper-division undergraduate electrical engineering course

This paper explores the implementation and effectiveness of flipped learning methodology, in an upper-division undergraduate electrical engineering course at a public research university. Given the limited research on flipped learning practices in engineering education, this study highlights the potential of flipped learning while addressing its implementation challenges. The objective of this study is to compare the learning outcomes between flipped and traditional classrooms taught between 2006 and 2023, using a quasi-experimental design. Additionally, student perceptions and their performance in various assessments are investigated for the Fall 2023 modified flipped classroom. Students’ feedback garnered at the end of the semester was combined with their grades to evaluate learning outcomes and perceptions of the flipped learning approach. The findings revealed an increase in the grades for the flipped classroom. The Fall 2023 students’ feedback indicated a preference for collaborative and active learning methods while revealing a reluctance to engage with pre-recorded videos, posing a major challenge in implementing flipped learning. The paper offers implications for future flipped courses, including methods to incentivize students to watch the pre-recorded videos and mitigate uneven learning outcomes in collaborative learning environments. Furthermore, it was observed that not all students prefer the flipped learning format. Hence, integrating mini-lectures may cater to students who prefer the traditional approach while maintaining the benefits of flipped learning.

Investigating STEM Secondary School Teachers’ CLIL Assessment Practices in EFL Contexts: the Repertory Grid Technique

Within the context of content and language integrated learning (CLIL), assessment assumes a pivotal role in fostering learning, given the dual emphasis on content and language acquisition. This dual focus engenders a heightened complexity in the assessment process within CLIL classrooms. Despite the escalating prevalence of CLIL classrooms, the aspect of CLIL assessment remains inadequately explored, constituting an unresolved issue in CLIL research and pedagogy. The enquiry into the assessment practices of CLIL teachers serves as a means to elucidate this research lacuna. This study aims to scrutinize the assessment practices employed by five Thai secondary school CLIL teachers, utilizing personal construct theory (PCT) as the foundational framework. Methodologically, data collection encompasses repertory grids and retrospective semi-structured interviews, with a pivotal application of focus grid analysis for data scrutiny and interpretation. The findings revealed that there was a strong preference for interactive and collaborative assessment methods that enhance both language skills and cognitive abilities. In particular, problem-solving tasks, lab experiments, and group discussions are effective in promoting higher-order thinking, while interactive projects and presentations boost language development. In contrast, traditional assessments such as multiple-choice questions are less effective. These findings highlight the need for professional development programmes and supportive educational policies to integrate dynamic, dual-focused assessments into CLIL curricula.

Exploring the Impact of Collaborative Learning on Student Motivation and Academic Achievement in Higher Education

Collaborative learning has emerged as an effective educational strategy that fosters student interaction and enhances learning outcomes. This paper examines how collaborative learning approaches, such as group projects, peer feedback, and cooperative tasks, influence student motivation and academic performance in higher education. The study reviews key theories and empirical evidence supporting the positive impact of collaborative learning on student engagement and achievement. It also highlights the challenges of implementing collaborative learning in diverse academic environments, such as issues related to group dynamics and individual accountability. The paper concludes by offering practical recommendations for educators seeking to integrate collaborative learning methods into their teaching practices to improve student outcomes.

Real-time economic following velocity and gear planning for commercial vehicles based on the bi-level optimization

ABSTRACT Current Predictive Adaptive Cruise Control (PACC) systems pose significant challenges. These include poor collaborative optimisation between economic velocity and the powertrain system, conservative energy-saving strategies and underutilisation of map data due to real-time constraints. A computationally efficient collaborative optimisation method is proposed for economic following velocity and gear shifting based on Bi-level optimisation theory. Based on the Bi-level theory, economic following velocity and gear shifting are decoupled and hierarchically planned. The lower-level subproblem constructs the objective function based on a dynamic-weight safety distance model within the Model Predictive Control (MPC) framework, solving the economic following velocity by the Continuous Generalized Minimal Residual Method (C/GMRES). The upper-level subproblem solves the economic gear shifting by the optimal control law based on the economic following velocity. Finally, comparative experiments are conducted between the proposed PACC algorithm, a standard Adaptive Cruise Control (ACC) algorithm, and a benchmark Dynamic Programming-Model Predictive Control (DP-MPC) PACC algorithm. The results show that the proposed PACC algorithm reduces fuel consumption by approximately 5.76% compared to the ACC. Compared to the benchmark algorithm, the proposed PACC algorithm also demonstrates significantly improved computational efficiency while achieving comparable energy savings.

Quality Assessment of MRI-Radiomics-Based Machine Learning Methods in Classification of Brain Tumors: Systematic Review.

Brain tumors present a complex challenge in clinical oncology, where precise diagnosis and classification are pivotal for effective treatment planning. Radiomics, a burgeoning field in neuro-oncology, involves extracting and analyzing numerous quantitative features from medical images. This approach captures subtle spatial and textural information imperceptible to the human eye. However, implementation in clinical practice is still distant, and concerns have been raised regarding the methodological quality of radiomic studies. A systematic literature search was performed to identify original articles focused on the use of radiomics for brain tumors from 2015 based on the inclusion and exclusion criteria. The radiomic features train machine learning models for glioma classification, and data are split into training and testing subsets to validate the model accuracy, reliability, and generalizability. The present study systematically reviews the status of radiomic studies concerning brain tumors, also using the radiomics quality score (RQS) to assess the quality of the methodology used in each study. A systematic search of PubMed identified 300 articles, with 18 studies meeting the inclusion criteria for qualitative synthesis. These studies collectively demonstrate the potential of radiomics-based machine learning models in accurately distinguishing between glioma subtypes and grades. Various imaging modalities, including MRI, PET/CT, and advanced techniques like ASL and DTI, were utilized to extract radiomic features for analysis. Machine learning algorithms such as deep learning networks, support vector machines, random forests, and logistic regression were applied to develop predictive models. The present study indicates high accuracies in glioma classification, outperforming traditional imaging methods and inexperienced radiologists in some cases. Further validation and standardization efforts are warranted to facilitate the clinical integration of radiomics into routine practice, ultimately enhancing glioma management and patient outcomes. Open science practices: Machine learning using MRI radiomic features provides a simple, noninvasive, and cost-effective method for glioma classification, enhancing transparency, reproducibility, and collaboration within the scientific community.

Carousel Brainstorming – An Innovative Small Group Teaching Method to Augment Anatomical Knowledge of MBBS Students

Introduction: This study investigated the impact of Carousel Brainstorming, a collaborative and interactive teaching method, on student learning in anatomy education. Emphasizing active participation and knowledge exchange, Carousel Brainstorming aims to enhance critical thinking, teamwork, and the comprehension of complex medical concepts. The research addresses the need for objective measures in real-world settings and explores the effectiveness of Carousel Brainstorming compared to traditional Tutorials. Methodology: Phase-1 MBBS students (n=100) in the academic year 2023-2024 participated in the study through simple randomization. The research spanned four months, and after a didactic lecture on Mammary Gland, students were divided into two groups: Group A (Tutorials) and Group B (Carousel Brainstorming). The latter engaged in a structured session involving pretests, subgroups, and rotating discussions. Post-tests were administered, and a questionnaire assessed students' perceptions. Results: Statistical analysis revealed significant differences in pre-test, post-test, and difference scores between Carousel Brainstorming and Tutorials groups (p < 0.001). Carousel Brainstorming exhibited higher post-test mean scores (7.6) compared to Tutorials (5.0). Students strongly agreed (56.8%) that Carousel Brainstorming was effective. Conclusion: Carousel Brainstorming emerges as a well-received and beneficial teaching method in anatomy education. Its collaborative and interactive nature positively influences knowledge acquisition and retention, fostering active learning. Future research may explore long-term impacts and diverse applications of Carousel Brainstorming in educational settings, offering a promising avenue for innovative pedagogy. KEYWORDS: Carousel Brainstorming, Anatomy education, Teaching-learning methods, Student perception, Learning gains.

Enhancing group interactions and story quality in primary school students: an investigation into the impact of collaborative storytelling methodologies

AbstractThis study examines the impact of collaborative storytelling methods on group interactions and story quality in primary school students. Participants (97 students, grades 1–3) engaged in tangible digital collaborative storytelling (TDST), collaborative digital storytelling (DST), and traditional storytelling (TST). The research explored three hypotheses: (1) TDST promotes more inclusive and prosocial interactions than DST, (2) collaborative stories show higher formal and content quality than individual stories, and (3) collaboration parameters influence story quality across methodologies. Employing a cross-sectional design, the findings demonstrated significant differences in group interactions and story characteristics In our study, students engaged in tangible digital collaborative storytelling (TDST) demonstrated reduced interruptions and enhanced task focus compared to traditional methods. Collaborative stories, particularly in TDST, were concise, with fewer off-task elements and higher narrative cohesion than individual stories. Notably, a significant correlation between story cohesion and inclusive space was observed. These findings highlight the significance of integrating tangible digital tools, providing valuable insights into enhancing collaborative storytelling for positive group dynamics and improved story quality among young students. This research holds implications for educational environments and therapeutic interventions, emphasizing the role of collaborative storytelling in fostering communication and group cohesion.

Collaborative Construction Method of Biomedical Knowledge Graph Based on Multi-Blockchain

The collaborative construction method of knowledge graph based on blockchain is crucial for the safe and efficient construction of biomedical knowledge graph (BioKG), and has received widespread attention from the academic and medical circles. At present, the data sources of BioKG are very wide. While using multi-source data to collaboratively build a knowledge graph, data redundancy problems often occur. Once data from a large number of sources is available, it becomes difficult for the system to ensure data consistency in a reliable way. In addition, the increase in inconsistent and redundant data also reduces the efficiency of collaboration in the system. The traditional blockchain-based collaborative construction method of BioKG is difficult to effectively solve these problems. Multi-blockchain technology provides a new solution for the construction of collaborative knowledge graphs in the biomedical field, which is characterized by good scalability and diversified applications. To this end, we propose a multi-person collaboration construction method for BioKG based on multi-blockchain, named collaborative construction method chain (CCMC). CCMC uses the multi-chain structure and clock cycle control method of the blockchain to separate user operations and system verification, and uses the read-write separation function to improve the consistency of the collaborative version. In addition, we propose a semantic fusion method that effectively reduces data redundancy in the collaborative process and improves the efficiency of BioKG collaborative construction. Performance testing and comparative evaluation confirmed that compared with previous methods, CCMC significantly improves the creation of collaborative knowledge graphs and meets the requirements of multi-person collaborative construction.

Development and Evaluation of 4 Short, Animated Videos for Women in Midlife Promoting Positive Health Behaviors: Survey Study

Background Health and health behaviors in midlife are important determinants of healthy aging. There is evidence of unmet needs for health-promoting information for women from culturally and linguistically diverse backgrounds and women with low literacy. Objective This study aimed to (1) develop accessible short, animated videos viewable and downloadable from YouTube aimed at promoting positive health behaviors in women in midlife and (2) evaluate their accessibility, acceptability, understanding, and usability and whether this was influenced by the level of education or socioeconomic disadvantage. Methods In collaboration with a video production company, a multidisciplinary team of academics and health professionals developed 2 short, animated videos on self-management of menopause health and 2 promoting joint health. Their accessibility, acceptability, understanding, and usability to women were evaluated in an anonymous web-based survey. Results A total of 490 women viewed the videos and responded to the survey. Of these, 353 (72%) completed all questions. Almost all (from 321/353, 91% to 334/363, 92%) agreed that the information in the videos was “very easy to understand.” The proportions reporting that all or some of the information in the video was new to them varied between videos from 36% (137/386) to 66% (233/353), the reported likelihood of using the practical tips offered in the videos varied from 70% (271/386) to 89% (331/373), and between 61% (235/386) and 70% (263/373) of respondents stated that they would recommend the videos to others. Education-level group comparisons revealed few differences in opinions about the videos, except that women with lower education were more likely than those with higher education to state that they would recommend the 2 joint health videos to others (36/45, 80% vs 208/318, 65%; P=.051 for video 3; and 36/44, 80% vs 197/309, 64%; P=.04 for video 4). There were no differences between women living in the least advantaged areas (Socioeconomic Indexes for Areas quintile areas 1 and 2) and those living in the most advantaged areas (Socioeconomic Indexes for Areas quintile areas 3, 4, and 5) in their responses to any of the questions about the 4 videos. Conclusions Most women found the videos easy to understand, learned something new from watching them, planned to use the practical tips they offered, and were likely to recommend them to other women. This suggests that short, animated videos about health self-management strategies in midlife to improve the chance of healthy aging are perceived as accessible, acceptable, easy to understand, and useful by women.

Peran Kolaborasi Pemerintah dan Masyarakat pada Proses Pengambilan Keputusan Sektor Pelayanan Publik

The importance of collaboration between the government and the community during the decision-making process in the public service sector using collaborative methods. The implementation of this collaboration is considered an effective strategy in order to improve public services as desired by the community. For this reason, the government needs to collaborate with the community in making public service policy decisions. By using the literature study method, this study obtained the result that collaboration is a form of innovation that has the same understanding between the parties involved. This same understanding can be in the form of vision, mission, and goals. Although there are still many challenges that need to be faced, most of them are that this government-community collaboration project has provided positive results and improved more effective public services.

Current status and development trends of research on collaborative control methods for unmanned cluster

Current status and development trends of research on collaborative control methods for unmanned cluster

Enhanced mechanical properties and mechanism of iron ore tailings-based geopolymers modified by municipal solid waste incineration fly ash

Iron ore tailings, coal fly ash and municipal solid waste incineration fly ash (MSWI FA), as solid waste, must be disposed of. Iron ore tailings, coal fly ash and MSWI FA were successfully prepared into high-performance geopolymer. The compressive strength of optimal geopolymer reached 57.89 MPa. Based on XRD, FTIR, SEM and chemical extraction analysis, C-(A)-S-H was the main gel product along with N-A-S-H. It was found that the dominant hydration process of geopolymer was changed from diffusion process (D) to phase boundary reaction process (I) after MSWI FA addition based on hydration reaction rate curves and dynamic simulation, releasing more hydration heat and generating more C(N)-A-S-H gels. Heavy metal content in the optimal geopolymer was below the limits of Class I screening value of development land (GB 36600-2018). Ef and Cf of the optimal geopolymer were 30.9 MJ/ton/MPa and 6.88 kg CO2/ton/MPa, affirming the viability of utilizing iron ore tailings, coal fly ash and MSWI FA as raw materials for geopolymer production, which provides a promising collaborative resource method of processing them.

Research on collaborative reconstruction method of missing data and abnormal data in air handling units (AHU)system

The Air Handling Units (AHU), as critical components in building energy management, demand rigorous performance monitoring to ensure indoor comfort and energy efficiency. However, prolonged sensor operation often leads to malfunctions and measurement drifts due to insufficient maintenance, resulting in data gaps and anomalies. These issues pose significant challenges to system operational efficiency, occupant comfort, and energy consumption regulation. To address this dilemma, this paper innovatively leverages Bayesian inference techniques, developing a sophisticated likelihood function distance model. This model achieves precise imputation of missing data and effective rectification of anomalous data, thereby mitigating the issue of error accumulation associated with frequent reliance on historical data.Furthermore, the study exploits the intricate coupling relationships among multiple physical variables within the AHU systems to construct a comprehensive analytical model. This approach reduces the dependence on historical data for mathematical fitting and enhances the reliability of predicted data. Consequently, this paper presents an efficient methodology for handling missing and anomalous sensor data in AHU systems. Experimental results demonstrate that the relative error for reconstructing single-variable missing data remains below 7 %, while the relative error for reconstructing multi-variable missing data is even lower, at 3 %. These findings affirm the practical applicability of the methodology in calibrating anomalous sensor data within the building energy management domain.

Optimizing E-commerce Recommender Systems: A Comprehensive Review of Techniques and Future Directions

Abstract. This paper examines recommender systems in e-commerce by reviewing technologies and real-world applications and identifying the importance of big data analytics in recommender systems. In the study, three kinds of recommendation algorithms are discussed: collaborative filtering, content-based recommendation, and hybrid models. Collaborative filtering methods did well in the case of large-scale user data, but had cold-start and sparsity problems. Based on the content, recommended methods have strong personalized suggestion functions, but the information cocoon phenomenon is a risk, which decreases the contents diversity. Hybrid models are a combination approach between the two techniques, providing a flexible and robustness solution, but only a more complex computationally. This article also looks at the technology trends that have emerged lately, for example, the use of deep learning models, as well as the privacy-preserving techniques utilized in recommender systems. By analyzing and summarizing the existing research, this paper provides a reference basis for future optimization and application of recommender systems and points out potential research directions.

Real-time sustainable cobotic disassembly planning using fuzzy reinforcement learning

Collaborative robots (cobots) play a vital role in smart manufacturing, particularly in disassembly processes. Human-robot collaboration (HRC) methods simultaneously leverage the complementary capabilities of humans and cobots, offering promising improvements in disassembly processes. A review of the literature reveals that most proposed HRC disassembly planning models do not incorporate sustainable factors, such as consumed energy, human safety, ergonomic risks, and circularity, in the decision-making process. Furthermore, uncertainties inherent in disassembly processes, such as the quality of recovered parts, are not well-addressed in the literature. This paper presents a novel multi-agent fuzzy reinforcement learning (RL) planning model for sustainable HRC disassembly. In addition to cost elements, the developed model involves social and environmental considerations in the real time planning process. By developing a fuzzy-based environment in the RL architecture, the proposed approach aims to effectively model the probabilistic uncertain parameters involved in the problem. Experimental analysis shows that the model presented in this research outperforms a baseline model, applied to the same case study, in terms of convergence time. Furthermore, in terms of qualitative analysis, the proposed model integrates a more extensive set of features into the planning process compared to recent literature.

Focus on user micro multi-behavioral states: Time-sensitive User Behavior Conversion Prediction and Multi-view Reinforcement Learning Based Recommendation Approach

In recommender systems, user behavior conversion implies user interest drifts and behavior patterns. However, current research has paid little attention to the correlation between target behavior conversion rate and user behavior patterns, and the impact of highly time-sensitive multi-behavior analysis on target behavior conversion rate is neglected. Meanwhile, compared to normal behavior conversions, user deviant behavior conversions are seldom studied. The behavior conversion rate that balances normal behavior patterns and deviant behavior patterns can more accurately reflect user interest drifts and real-time needs, thereby improving recommendation performance. Based on the above motivations, we propose a Time-sensitive Behavior Conversion Prediction and Multi-view Reinforcement Learning Based Recommendation Approach (TCMR), aiming to achieve more accurate and adaptive recommendations by analyzing user interest drifts, demand timings and behavior stability. First, we construct a hyper-behavior spatial model of highly collaborative temporal signals, and propose a subnet collaborative method to obtain normal behavior patterns, in which, core subnet, similarity subnet and behavior subnet are extracted from the hyper-behavior spatial model. Subsequently, we design a multi-level user behavior trajectory tree to perceive potential user deviant behaviors by comparing behavior conversions within the single behavior modality and across different behavior modality. By integrating normal behaviors and deviant behaviors, we evaluate user interest drifts, demand timings, and behavior stability, and ultimately obtain a prediction of behavior conversion rate. Finally, a multi-perspective asynchronous reinforcement learning is proposed, enabling TCMR to provide recommendations by considering multiple user perspectives and purposes. Experimental results demonstrate that TCMR exhibits superior recommendation performance and effectiveness.

Non-local design method of metasurface by the grating diffraction theory enabling efficient wave control

Currently, the metasurface design of flexural wave is primarily based on the generalized Snell's law (GSL). However, due to GSL only considering phase gradient, high-order parasitic diffraction leads to poor performance of larger angle anomalous refraction. In this paper, to suppress parasitic diffraction, a collaborative optimal method is proposed for metasurface design. Firstly, the diffraction formula considering both phase gradient and periodicity of grating structures is derived based on the grating diffraction theory (GDT). Then, the reconfigurable metasurface constructed of ‘screw-nut’ substructures are designed in conjunction with the genetic algorithm (GA). For the vertical incident flexural wave, the anomalous refractions of medium angle 51° and large angle 77° for 6.4 kHz, as well as 90° for 6.066 kHz are considered. Finally, oblique incidence is considered, in which the improvements of collaborative optimization design methods to 1 and −1 orders anomalous refractions are analyzed separately. The results are compared with the local ones obtained by GSL. It indicates that the proposed non-local design method opens a new perspective for parasitic diffraction suppression and efficient flexural wave manipulation.

A robust optimization approach for steeling-continuous casting charge batch planning with uncertain slab weight

The volatility of slab weight in steelmaking-continuous casting (SCC) production, attributed to factors such as flexible order demand, is addressed in this paper. A robust optimization mathematical model for charge batch planning (CBP) with uncertain slab weight is established, and a collaborative optimization method using the surrogate Lagrangian relaxation (SLR) framework and improved objective feasibility pump (IOFP) is developed to solve the problem. In the SLR method, new step-size updating conditions are developed, eliminating the need for pre-estimating the optimal dual value. Additionally, only a subset of subproblems that satisfy the optimality conditions of the surrogate needs to be solved to overcome the low optimization efficiency resulting from oscillations in the feasible domain during internal searches in traditional Lagrangian relaxation (LR) methods. The IOFP method is employed to match the structure of the subproblem model of 0–1 mixed integer programming (MIP). During the search for integer solutions, a weighted objective function is added to the auxiliary model to improve the quality of solutions. Furthermore, it combines a variable neighborhood branching method to prevent the algorithm from entering into cycles. Finally, the effectiveness of the proposed model and the performance of the algorithm are validated through simulation experiments.

A Multi-Mode Pressure Stabilization Control Method for Pump–Valve Cooperation in Liquid Supply System

In order to solve the problems of frequent pressure fluctuations caused by frequent action of the unloading valve of the pump station and serious hydraulic shock due to the variable amount of fluid used in the hydraulic support system of the coal mining face and the irregularity of the load suffered by the system, a pump–valve cooperative multi-mode stabilizing control method based on a digital unloading valve was proposed. Firstly, a prototype of a digital unloading valve under high-pressure and high water-based conditions was developed, and a digital control scheme was proposed to control the pilot valve by a servo motor to adjust the system pressure in real time. Then, an experimental platform for simulating the hydraulic bracket and a co-simulation model was constructed, and the validity of the co-simulation model was verified through experiments. Secondly, a collaborative multi-mode pressure stabilization control method for the pump valve based on a GRNN (General Regression Neural Network) was established to control the flow and pressure output of the emulsion pumping station according to the actual working conditions. Finally, numerical research and experimental verification were carried out for different working conditions to prove the effectiveness of this method. The results showed that the proposed pressure stabilization control method could adaptively adjust the working state of the digital unloading valve and the liquid supply flow of the emulsion pump station according to the working condition of the hydraulic support, effectively reducing the frequency and amplitude of the system pressure fluctuations and making the system pressure more stable.

The Reform and Practice of College English Writing Teaching Mode by Integrating Information Technology

The integration of information technology (IT) into college English writing instruction has emerged as a transformational strategy in response to the limitations of traditional teaching methods. This paper explores the modifications and applications of a pedagogical approach that uses ICT to improve the effectiveness of college English writing instruction. Traditional approaches to teaching English writing often suffer from limitations such as lack of individual feedback, monolithic teaching methods, and inadequate engagement with digital tools that define modern communication. The study examines how the integration of ICTs into the curriculum can lead to a variety of instructional strategies, including digital tools, collaborative methods, and multimedia materials, that address different learning styles. The main focus areas are the challenges associated with traditional teaching methods, the specific benefits of ICT in teaching English writing, and practical strategies for better integration The paper builds on case studies and data-supported outcomes to demonstrate the success of ICT-enabled learning practices for improving students’ writing skills, critical thinking skills and engagement. The findings suggest that ICT not only modernizes English education but also integrates the demands of a digitally connected world. This integration opens up a more sustainable, inclusive, and competency-oriented approach to English writing education in colleges.