Collaborative Method Research Articles

Nuestras Historias: Latino* youth stories from upstate South Carolina

ABSTRACT Photo elicitation is a collaborative visual method that uses photography to facilitate conversations between participants and researchers (Loeffler, 2004; Shaw, 2021). Latino youth are an understudied population and face many challenges and adversities, including stressors surrounding immigration and family illegality (Ellis, 2021; Camarillo et al., 2023). The purpose of this study is to explore the lived experiences of Latino youth in the South Carolina Upstate as they develop coping strategies caused by status-related stressors surrounding their family's illegality. Eight Latino youth (ages 16–20) participated in this study using photo elicitation and the PHOTO method, capturing images of their everyday lives (Breny & McMorrow, 2020; Shaw, 2021). Guided by the Chicano/Latino Ethnic Identity Model and the Cycles of Deportability Framework, data analysis was conducted and identified three themes (Ellis, 2021; Ruiz, 1990). (a) Latino youths ethnic identity connected to their family and Latino communities, (b) Status-related stressors resulting in fears linked to macro socio-political environments and family illegality and (c) Development of coping strategies, specifically outdoor recreation. Findings from this project will help communities and organizations understand the importance of leisure for Latino youth facing status-related stressors.

Behavioral Patterns in Micro-lending: Enhancing Credit Risk Assessment with Collaborative Filtering and Federated Learning

Credit risk assessment uses finance-based behavioural patterns for micro-lending purposes and organizations. The repayment behaviour and credit stability patterns are analyzed across varying repayment tenures and financed amounts. Due to limited borrower data and fluctuating financial patterns, micro-lending platforms have substantial hurdles when it comes to effectively evaluating credit risk. This article introduces a Collaborative Filtering Method using Lending Pattern Analysis (CFM-LPA). The proposed method is enhanced through collaborative federated learning, enabling the analysis of these patterns. This approach evaluates the return rate, credit limit, and consumer response behaviours. Federated learning processes one or more of these factors to assess diverse lending patterns. Based on these evaluations, the behavioural factor is updated for each return period, influencing the credit risk for subsequent return periods and supporting the financial stability of micro-lending operations. The model is trained individually on the identified factors, allowing the behavioural factor to be filtered. New credit risks are identified using this filtered factor from the previous return period. These insights help define new behavioural patterns for the specified credit limit. The proposed method enhances risk detection accuracy by 14.03% and improves return rate analysis by 13.28% across financed amounts. The above abstract is also graphically presented.Graphical

Multi-objective collaborative optimization of active distribution network operation based on improved particle swarm optimization algorithm

ADN (Active distribution network) is easily disturbed during its operation, resulting in problems such as power supply quality degradation and operation safety deterioration. Therefore, the research and simulation of multi-objective collaborative optimization of ADN operation based on improved particle swarm optimization algorithm are proposed. An objective function of multi-objective collaborative optimization configuration for ADN operation is constructed. According to this objective function, the improved particle swarm optimization algorithm is used to optimize the collaborative optimization configuration, and the population particles are mutated, and the obtained result is the optimal energy storage capacity configuration result of power system. The architecture of the simulation platform for cooperative operation of ADN is constructed, and the load grades of distribution system are divided. Based on the hierarchical management of loads in distributed systems, multi-objective collaborative optimization of ADN operating voltage in both frequency and time domains has been achieved. The experimental results show that during peak periods, the system’s load capacity is only twice that of before optimization or other situations, achieving stable power supply for peak power demand. Multi-objective collaborative optimization in frequency domain and time domain has the best effect. Under the conditions of reactive power and active power, the multi-objective collaborative optimization method of ADN operation has good results.

Efficacy and Safety of Phosphate-Lowering Agents for Adult Patients with Chronic Kidney Disease 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).

Lesson Study som metode for utvikling av det profesjonelle læringsfellesskapet i videregående skole

The article examines a study conducted at an upper-secondary school in Southern Norway, focusing on the experiences of teachers and school leaders using Lesson Study (LS) to enhance their professional community. LS is a collaborative method where teachers jointly plan, implement, and analyze lessons. Data for this study was collected through five focus group interviews with 17 participants, including one interview with school leaders and four with teachers. The data underwent a collective, qualitative analysis alternating between inductive and deductive coding. The findings suggest that participants view the professional community as a collaborative professional effort. Teachers emphasize that adequate time is essential for LS to positively impact professional development. Despite time constraints, LS fostered professional collaboration, increased openness among colleagues, provided insights into teaching practices, and demonstrated direct applicability to teaching. The teachers stress the importance of voluntary participation in LS. School leaders echoed the need for sufficient time, noting that shortages reduce motivation and obscure LS's benefits. They described their school’s implementation of LS as a local adaptation and acknowledged challenges in enabling teachers with enough time to engage with research literature. The lack of time, they reported, diminishes motivation, limits the use of research literature, and reduces experience-sharing, ultimately hindering LS's potential as a tool for professional development. The discussion explores factors influencing time availability for planning, reflection, experience-sharing, research engagement, and balancing autonomy with control—key components of a professional community.

Building collaborative infrastructures for an interdisciplinary higher education master’s program

This paper examines the practices and importance of building a collaborative infrastructure in interdisciplinary education, using the context of the master’s program developed by the Interdisciplinary Consortium for Applied Research in Ecology and Evolution (ICARE) as a case study. The study focuses on two levels of collaborative infrastructure: The project organization and project practice of the ICARE program and the specific use of CoNavigator, a physical tool for interdisciplinary teaching, learning, and collaboration. The analysis explores the educational aspects of the ICARE program and investigates how the training teams (each consisting of a master’s student, supervisors, and mentors) within the project organized themselves and developed their collaboration methods. By examining the challenges faced by ICARE and the implications for its Trainees and stakeholders, this paper emphasizes the significance of prioritizing and developing robust and explicit collaborative infrastructures both at the program and institutional level, as the challenges identified in ICARE mirror those at higher institutional levels, where interdisciplinary activities are not sustained unless they are fully embedded in the visible and physical structures. The findings provide valuable insights for future interdisciplinary study programs and underscore the necessity of proactive infrastructure planning and implementation to support successful interdisciplinary teaching and learning practices.

Understanding degenerative cervical myelopathy in musculoskeletal practice

ABSTRACT Background Degenerative cervical myelopathy (DCM) is a clinical syndrome characterized by a progressive compression of the spinal cord. DCM often looks like common symptoms of aging or bilateral carpal tunnel syndrome in its early stages, requiring careful differential diagnosis. Identifying DCM is a real challenge as no validated screening tools are available for making the DCM diagnosis. Potentially, individuals with DCM may experience misdiagnosis or substantial diagnostic delays, with an enhanced risk of irreversible neurological consequences if not promptly addressed. Despite the increasing prevalence, there is a lack of awareness about DCM among both the public and healthcare professionals. However, patients may seek physiotherapy to obtain a diagnosis or access treatment. Methods A comprehensive (non-systematic) review of the literature about DCM epidemiology, pathophysiology, clinical presentation, diagnostic methods, and management was conducted. Results A guide and essential knowledge to facilitate clinicians to understand DCM and to enhance clinical reasoning skills, performance and interpretation of the examination are provided. Interdisciplinary collaboration and optimal referral methods are also handled. Conclusion The aim of this article is to summarize and enhance physiotherapists’ essential knowledge of the differential diagnosis and management of patients with DCM.

Collaborative monitoring method for cutter anomaly detection and RUL prediction based on multi-task learning

Collaborative monitoring method for cutter anomaly detection and RUL prediction based on multi-task learning

Teachers’ capabilities to implement virtue education in classroom practice in Latvia: implications from feasibility narratives

ABSTRACT In Latvia, as in many other countries, teachers’ capabilities to implement statutory moral education remain understudied. Based on teachers’ feedback during the piloting of a new curriculum for virtue education called ‘e-TAP’, which provided 118 lesson plans and learning materials from preschool to Year 9, this research addresses the question ‘What are teachers’ fulfilled and unfulfilled capabilities for delivering a virtue education curriculum which appear explicitly or implicitly in their narratives about their classroom practice in Latvia?’ Mixed-methods were used for data collection: 259 teachers completed 1305 feedback questionnaires and 25 teachers participated in focus-group discussions. The results provided insights (1) into teachers’ capabilities for shifting the focus of education to virtue education, being aware of the larger educational ecosystem, and demonstrating digital literacy when implementing the virtue education curriculum; and (2) into their difficulties for planning virtue learning in larger timespans, using collaborative methods, integrating virtue education across subjects, and practising virtues in real-life situations. Teacher training implications for strengthening teachers’ capabilities to implement virtue education in classroom practice are put forward.

The Effectiveness of Supervision Programs on Improving Teaching Skills and Teacher Performance

This study aims to evaluate the effectiveness of the academic supervision program in improving teaching skills and teacher performance at SMK Muhammadiyah 1 Sangatta Utara. Using a quantitative approach and experimental design, the study involved 30 teachers as respondents, where 60% had less than 5 years of teaching experience, and 40% had more than 5 years of experience. Data were collected through standardized observation instruments and analyzed using t-test and regression analysis. The results showed a significant increase in teachers' teaching skills by 23.08% (from a score of 65 to 80) and teacher performance by 21.43% (from a score of 70 to 85) after the implementation of the supervision program. The statistical analysis produced a t-value of 5.67 with a p-value <0.05 and a coefficient of determination (R²) of 0.62, indicating that 62% of the variation in teacher performance can be explained by the supervision program. The majority of teachers expressed high satisfaction with the supervision program in providing an understanding of learning objectives (85%), improving teaching skills (90%), providing constructive feedback (88%), encouraging creativity (87%), and increasing collaboration between teachers (80%). Factors supporting the effectiveness of supervision include the frequency of periodic supervision, collaborative supervision methods, school management support, and teacher self-reflection. This study has important implications for the development of an adaptive supervision model for vocational education and can be an empirical reference for similar educational institutions in designing effective and sustainable teacher professionalism improvement programs

Collaborative Learning Groupings Incorporating Deep Knowledge Tracing Optimization Strategies

Effective grouping in collaborative learning is crucial for enhancing the efficiency of collaborative learning. A well-structured collaborative learning group can significantly enhance the learning effectiveness of both individuals and group members. However, the current approaches to collaborative learning grouping often lack a thorough examination of students’ knowledge-level characteristics, thereby failing to ensure that the knowledge structures of group members complement each other. Therefore, a collaborative learning grouping method incorporating the optimization strategy of deep knowledge tracking is proposed. Firstly, the optimized deep knowledge tracking (DKVMN-EKC) model is used to model the knowledge state of learners to obtain the degree of knowledge mastery of learners, and then the K-means method is used to similarly cluster all learners, and finally, the learners of different clusters are assigned to suitable learning groups according to the principle of heterogeneity of grouping. Extensive experiments have demonstrated that DKVMN-EKC can precisely model students’ knowledge mastery levels and that the proposed approach facilitates effective grouping at the level of students’ knowledge structures, thereby ensuring fairer and more heterogeneous grouping results. This approach fosters positive interactions among students, enabling them to learn from one another and effectively improve their understanding of various knowledge points.

The Effectiveness of Group Discussion Methods and Learning Media in Enhancing Students' Understanding in the Qur'anic Studies Course at STIT Palapa Nusantara

This study explored the effectiveness of the group discussion method and learning media in enhancing students' understanding of the Qur'anic Studies course at STIT Palapa Nusantara. A quasi-experimental design was applied, with students divided into two groups: an experimental group that participated in group discussions supported by learning media, and a control group that followed traditional lecture-based instruction. The data were collected through pre- and post-tests. The data were analyzed using both descriptive and inferential statistics, with paired sample t-tests applied to compare pre- and post-test scores. Qualitative data were analyzed through thematic analysis, identifying key themes related to student engagement, learning collaboration, and retention. The results showed that the experimental group experienced significant improvement in their understanding, with mean scores increasing by 25 points, compared to a 7-point increase in the control group. Qualitative findings highlighted several benefits, including active student engagement, collaborative learning, and enhanced retention, all facilitated by the group discussion method and learning media. The study demonstrated that integrating these approaches fostered a deeper understanding of the material, promoted critical thinking, and improved retention. These findings provide valuable insights into how teaching strategies in Islamic education can be enhanced, emphasizing the importance of interactive and collaborative learning methods. This research contributes to the development of more effective teaching practices and provides evidence that combining group discussions with media-based learning is a promising approach for improving student outcomes in Qur'anic Studies.

A text-based recommender system for recommending relevant news articles

A text-based recommender system for recommending relevant news articles

Heterogeneous Federated Learning: Client-Side Collaborative Update Interdomain Generalization Method for Intelligent Fault Diagnosis

Heterogeneous Federated Learning: Client-Side Collaborative Update Interdomain Generalization Method for Intelligent Fault Diagnosis

The synergy of digital knowledge and skills in forming digital citizenship: a comprehensive assessment on nursing students

Nurses play a pivotal role in improving the quality of healthcare services through innovation in the nursing care delivery system by using technological advancement. Digital knowledge and mindset are useful for further developing digital skills and literacy among future nurses to form digital citizenship. This study aimed to analyze the correlation between digital knowledge and mindset with digital skills, literacy, and citizenship among nursing students. The mind sponge theory was used in study conceptualization and results interpretation. This quantitative study utilized a cross-sectional design. The population was all nursing students in two private nursing institutions located in Bangkok, Thailand (n=476) and Surabaya, Indonesia (n=179). Samples were the total population. Self-developed, valid, and reliable instrument was used in data collection. Descriptive statistics and correlation test were used in data analysis (α<.05). Findings showed that digital knowledge did not correlate with digital mindset (p=.94), but it was weakly correlated with digital skills, literacy, and citizenship (ρ=.12-.16; p<.05). Digital mindset was strongly correlated with digital skills, literacy, and citizenship (ρ=.56-.60; p<.05). Digital skills had a strong correlation with digital literacy and citizenship (ρ=.58-.67; p<.05), while digital literacy was strongly correlated with digital citizenship (ρ=.59; p=<.05). The synergy of digital mindset, skills, and literacy are recommended to support the digital citizenship formation. We strongly recommend the implementation of collaborative learning method in nursing education. This method promotes the information filtering and exchanging behaviors among students important for digital nursing.

Knowledge&Social-based collaborative method with contrastive graph structure learning for explainable recommendation

Knowledge&Social-based collaborative method with contrastive graph structure learning for explainable recommendation

Trust-aware privacy-preserving QoS prediction with graph neural collaborative filtering for internet of things services

The booming development of the Internet of Things (IoT) has led to an explosion of web services, making it more inconvenient for users to choose satisfactory services among numerous options. Therefore, ensuring quality of service (QoS) in a service-oriented IoT environment is crucial, highlighting QoS prediction as a prominent research focus. However, issues related to information credibility, user data privacy, and prediction accuracy in QoS prediction for IoT services have become significant challenges in current research. To tackle these issues, we propose TPP-GNCF, a trust-aware privacy-preserving QoS prediction framework that integrates graph neural networks with collaborative filtering methods. In TPP-GNCF, we filter out untrustworthy QoS values provided by users for certain services to select credible QoS values. Then, a message-passing graph neural network (MP-GNN) is utilized to effectively capture information transmission and relationships in the graph structure, while differential privacy is used to protect user node information. In addition, we use a similarity calculation method based on weight function in collaborative filtering to mine implicit embedded features that graph neural networks cannot directly utilize. Finally, the final missing QoS values are achieved by fusing graph neural predicted QoS and feature collaborative filtering predicted QoS. We conducted extensive experiments on the well-known WS-DREAM dataset. The results demonstrate that the TPP-GNCF framework not only surpasses existing schemes in performance but also effectively addresses issues of information credibility and user privacy.

Persepsi Mahasiswa dalam Faktor Mempelajari Kanji

This study aims to determine the problems of kanji learning difficulties and how to overcome them at three private universities in Bandung, namely Universitas Nasional Pasim, Universitas Kristen Maranatha, and Universitas Komputer Indonesia involving 125 students. The method used in this study is a quantitative descriptive method by distributing questionnaires to students as a research instrument via Google Forms. Then the data is processed in the form of a percentage to find students' responses or perceptions of the difficulties in learning kanji and efforts to overcome them. From the results of the study, it was found that third-semester students of the Japanese Literature Study Program from three private universities in Bandung had difficulty mastering basic kanji. The difficulties experienced by students in sequence were difficulty in understanding the meaning of kanji, difficulty in writing kanji, difficulty in understanding the number of kanji strokes, difficulty in understanding bushu, and difficulty in reading kanji. To overcome these difficulties, students prefer to ask lecturers outside of class hours and use the internet to learn kanji. However, not many students learn kanji independently. Therefore, to improve students' ability to master kanji, the use of collaborative learning methods or project-based learning is required.

개인중심접근에 근거한 IEP 운영을 위한 중학교 개별화교육지원팀의 협력적 실행

Purpose: The purpose of this study is to examine the practical knowledge gained by the middle school individualized education support team in the process of operating an person-centered individualized education plan. Method: To this end, a person-centered IEP was operated for a total of 10 months from April 2023 to February 2024 by applying the collaborative action research method with the individualized education support team. The data collected in the implementation process consisted of 3 upper categories, 10 subcategories, and 28 semantic units using a comprehensive analysis procedure. Results: 1) person-centered IEP operation began by listening to the voices of the three subjects of student, guardian, and teacher education 2) person-centered IEP can be operated most excellently through the cooperation of the individualized education support team; cooperation by the individualized education support team was achieved throughout the establishment, implementation, and evaluation of IEP 3) cooperation by the individualized education support team on the operation of person-centered IEP was a process to grow students, guardians, and teachers. Conclusion: Based on these findings, basic information and implications for implementation that can be used when applying person-centered IEPs in an integrated education environment are presented.

유아교사 양성과정에서 생성형 AI 활용 교수설계 방안 탐색

This study aims to explore the instructional design approach utilizing generative AI (such as ChatGPT) in early childhood teacher training programs and examine its feasibility and applicability. The research was conducted through literature review, two rounds of expert focus group interviews (FGI), and the development of an instructional design model. The findings indicate that AI-based instructional design should be systematically structured, incorporating adaptive learning, AI-driven feedback and assessment, and intelligent learning assistants (chatbots). AI can serve as a tool to complement the role of instructors and support personalized learning. However, limitations such as the reliability of AI feedback and disparities in AI utilization skills also exist. To address these issues, it was concluded that maintaining the traditional role of instructors and adopting a hybrid assessment model that combines AI evaluation, peer assessment, and instructor evaluation is necessary. Based on the ADDIE instructional design model, a four-week AI-integrated instructional design framework was proposed, detailing AI-assisted collaborative learning and assessment methods. The study concludes that AI-based instructional design has the potential to improve teaching quality and enhance teachers' instructional planning skills in early childhood teacher training programs.