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In this paper, we adopt a multistakeholder governance perspective to study how people collectively respond to a grand challenge. Specifically, we show how working through governance obstacles—that is, coordinating and collaborating challenges arising from a multistakeholder governance approach to responding to grand challenges—can erode actors' ability to mitigate these wicked problems. We illustrate this process through an in-depth case study of WaterHealthOrg, a multistakeholder initiative established to address degrading water health in Australia's critical Great Barrier Reef region. Our findings reveal how, in an effort to avoid group paralysis or dissolution, actors employ specific practices to address governance obstacles. By doing so, actors set off a cumulative self-reinforcing process, driving them to consolidate rather than critically reflect on and adapt their collective response. Drawing on these insights, we develop a conceptual process model of how efforts to manage multistakeholder governance obstacles can generate governance traps that shape participants' ability to collectively respond and, ultimately, mitigate grand challenges. [ABSTRACT FROM AUTHOR]

Currently, there are increasing attempts to better involve citizens in political decision processes. A successful approach in that regard has been participatory budgeting (PB), which allows citizens to propose projects and then decide how to distribute a given budget over them. Meanwhile, the literature on collective intelligence (CI) has also shown the ability of groups to solve complex problems. Thus, by combining CI and PB, it should be possible for citizens to identify problems and create their own solutions. In this article, we study this possibility by using agent-based models. Specifically, we first show that a system combining CI and PB produces solutions that strongly penalize minorities if the solution quality depends on group size. Then, we introduce an approach that can overcome this issue. Indeed, by using a common knowledge base for the storage of partial solutions, the quality of solutions of minorities can benefit from the work of the majority, thereby promoting fairness. Interestingly, this approach also benefits majorities, as the quality of their solutions is further improved by the work of the minorities, thus reaching better solutions for everyone. This stresses the potential and importance of an open innovation approach, which is committed to information sharing. This article is part of the theme issue 'Co-creating the future: participatory cities and digital governance'. [ABSTRACT FROM AUTHOR]

Purpose: Many groups in organisations are unsuccessful in problem solving. However, the principle of continuous improvement necessitates that organisations refine their employees' problem-solving skills. In this mixed-method, field-based lab experiment, we explored the impact of a treatment to enhance the quality of group problem-solving processes. Design/methodology/approach: We focused on the structured problem-solving process in Kaizen Events by differentiating six consecutive phases. About 16 Kaizen Event groups (101 members) participated in a field-based lab experiment that used a lean simulation game to establish a group problem-solving context. Data were collected via video, surveys and group interviews. We examined if a high-quality process is strengthened through group members' elevated awareness of problem-solving preferences. About 11 groups received a treatment of tailor-made individual feedback to increase awareness of their problem-solving preferences. Additionally, we repeated the experiment in five control groups, where member preferences were not shared. Findings: In the treatment groups, where problem-solving preferences had been shared, we observed a clear improvement in Kaizen Event process quality and higher problem-solving self-efficacy levels. Moreover, their self-reported Kaizen Event behaviour had changed. Within the control groups, the participants also reported that their problem-solving self-efficacy had improved, but this did not have a positive impact on the quality of the objectively measured Kaizen Event process. Originality/value: By combining insights from operations management and organisational behaviour, we demonstrate that the structured Kaizen Event problem-solving process improves when group members' individual problem-solving preferences are shared. We thus add an individual-level variable to the extant models of Kaizen Event success factors. Our results provide fresh insights into how to improve the continuous improvement process within organisations. Kaizen Event stakeholders and their facilitators are offered guidance on how to increase one's awareness of own and others' problem-solving preferences in group-based problem-solving events. [ABSTRACT FROM AUTHOR]

Community-based courses, driven by connectivist learning theory, provide learners with a social environment that supports collaborative problem solving (CPS). The purpose of this study is to investigate the characteristics of potential collaborators before they engage in CPS. This study analyzed a community-based course, a cMOOC with a total of 145 learners participated in the thematic learning stage and 52 of the 145 chose to participate in the CPS. The interaction pattern of the collaborators and the indirect effects of individual attribute-based (exogenous) effects and network structure-based (endogenous) effects on the interaction pattern were examined using the quadratic assignment procedure (QAP) and the stochastic actor-oriented model (SAOM). Results suggest that substantial interaction activeness and interaction attractiveness are key patterns of the collaborators' social interactions before CPS, and there is some interaction homogeneity among the collaborators. Some individual attribute-based effects, such as age, gender, and occupation, will reduce the early interaction attractiveness of the collaborators. The reciprocity, transitivity, and cumulative advantages of the network structure-based effects will gradually increase collaborators' interaction attractiveness; in contrast, the network structure-based effects simultaneously limit collaborators' interaction activeness. This study provides a theoretical foundation and practical experience regarding encouraging learners' deep collaboration in community-based courses. [ABSTRACT FROM AUTHOR]

When solving a complex problem in a group, should group members always choose the best available solution that they are aware of? In this paper, I build simulation models to show that, perhaps surprisingly, a group of agents who individually randomly follow a better available solution than their own can end up outperforming a group of agents who individually always follow the best available solution. This result has implications for the feminist philosophy of science and social epistemology. [ABSTRACT FROM AUTHOR]

The article discusses the concept of flipping a science course to promote equity and inclusion. The author flipped a mid-level majors and pre-medical course, BIOL151: Biological Chemistry, in Spring 2020 to increase student engagement and understanding. The flipped format allowed for the adoption of inclusive pedagogy strategies and provided opportunities for diverse student populations to succeed. The author emphasizes transparency with students, interventions to support students with varying levels of prior knowledge, and the importance of engaging students' strengths and affective domain goals in the flipped course. [Extracted from the article]

Cognitively diverse teams outperform high intelligence (IQ) teams in problem solving, while businesses with at least one woman in their board of directors financially outperform ones with all men boards. These well‐known facts are lost in most academic science enterprises. Herein, we make the case for looking at sources, approaches, and opportunities in expanding cognitive diversity of research teams for high productivity and efficiency. [ABSTRACT FROM AUTHOR]

For half a century, artificial intelligence research has attempted to reproduce the human qualities of abstraction and reasoning - creating computer systems that can learn new concepts from a minimal set of examples, in settings where humans find this easy. While specific neural networks are able to solve an impressive range of problems, broad generalisation to situations outside their training data has proved elusive. In this work, we look at several novel approaches for solving the Abstraction & Reasoning Corpus (ARC). This is a dataset of abstract visual reasoning tasks introduced to test algorithms on broad generalization. Despite three international competitions with $100,000 in prizes, the best algorithms still fail to solve a majority of ARC tasks. The best solvers today rely on complex hand-crafted rules, without using machine learning at all. We revisit whether recent advances in neural networks allow progress on this task, or whether an entirely different class of models are required. First, we adapt the DreamCoder neurosymbolic reasoning solver to ARC. DreamCoder automatically writes programs in a bespoke domain-specific language to perform reasoning, using a neural network to mimic human intuition. We present the Perceptual Abstraction and Reasoning Language (PeARL) language, which allows DreamCoder to solve ARC tasks, and propose a new recognition model that allows us to significantly improve on the previous best implementation. We also propose a new encoding and augmentation scheme that allows large language models (LLMs) to solve ARC tasks, and find that the largest models can solve some ARC tasks. LLMs are able to solve a different group of problems to state-of-the-art solvers, and provide an interesting way to complement other approaches. We perform an ensemble analysis, combining systems to achieve better results than any system alone and analysing individual strengths. However, it is sobering to see that approaches based on neural networks still lag behind existing hand-crafted solvers, and we suggest avenues for future improvements. Our findings with the ensemble model may indicate that a diversity of methods might be necessary to solve problems in ARC. Humans likely employ diverse strategies to solve ARC. Studies involving human participants to identify the strategies they employ to solve ARC could provide valuable insights for future AI approaches. Finally, we publish the arckit Python library to make future research on ARC easier. [ABSTRACT FROM AUTHOR]

The article discusses a lab project that integrates principles of exercise physiology and physics by having students design and construct a bicycle-powered centrifuge. This project aims to engage kinesiology students who may have low interest in physics by providing a hands-on, interdisciplinary experience. Students are given the freedom to design the device using readily available materials, and the lab allows for measurements and calculations related to efficiency, angular speed, power analysis, and metabolic equivalents. The project fosters problem-solving skills, group collaboration, and deeper understanding of physics and kinesiology concepts among students. [Extracted from the article]

The decisions that teachers make in transforming the curriculum into specific lesson plans determine the real enactment or otherwise of curricular ideals. These decisions are shaped by the resources available and by each teacher's goals and orientations. This exploratory study employs Schoenfeld's decision-making model to examine how resources, goals and orientations influence lesson planning for mathematics problem solving, for different profiles of primary teachers in Chile. To this purpose, a survey was conducted among 40 teachers of varying degrees of ability and experience: some were beginning teachers, others were experienced but had no further training in teaching problem solving and a third group was composed of experienced teachers with specific training in this question. Interviews with two teachers from each profile revealed important differences between the three groups. Beginning teachers relied more heavily on official resources such as the official curriculum and standard textbooks, aligning themselves with school requirements. Experienced teachers with problem solving training demonstrated a strong inclination towards teaching through a problem solving approach. While beginning teachers acknowledged the importance of promoting problem solving strategies, they did not usually adapt problems to the mathematical content or to the age/competence of their students. Interestingly, all three groups under-utilised sections of curricular resources that emphasise the present curricular focus on problem solving. Finally, the study found that experience alone is not enough to develop a problem solving approach and that focused professional development programmes are needed to equip teachers with the necessary skills. In addition, a diagnostic teaching approach should be incorporated into initial teacher training. [ABSTRACT FROM AUTHOR]

As online social networks (OSNs) become more prevalent, a new paradigm for problem-solving through crowd-sourcing has emerged. By leveraging the OSN platforms, users can post a problem to be solved and then form a team to collaborate and solve the problem. A common concern in OSNs is how to form effective collaborative teams, as various tasks are completed through online collaborative networks. A team's diversity in expertise has received high attention to producing high team performance in developing team formation (TF) algorithms. However, the effect of team diversity on performance under different types of tasks has not been extensively studied. Another important issue is how to balance the need to preserve individuals' privacy with the need to maximize performance through active collaboration, as these two goals may conflict with each other. This research has not been actively studied in the literature. In this work, we develop a TF algorithm in the context of OSNs that can maximize team performance and preserve team members' privacy under different types of tasks. Our proposed PRivAcy-Diversity-Aware TF framework, called PRADA-TF, is based on trust relationships between users in OSNs where trust is measured based on a user's expertise and privacy preference levels. The PRADA-TF algorithm considers the team members' domain expertise, privacy preferences, and the team's expertise diversity in the process of TF. Our approach employs game-theoretic principles Mechanism Design to motivate self-interested individuals within a TF context, positioning the mechanism designer as the pivotal team leader responsible for assembling the team. We use two real-world datasets (i.e., Netscience and IMDb) to generate different semi-synthetic datasets for constructing trust networks using a belief model (i.e., Subjective Logic) and identifying trustworthy users as candidate team members. We evaluate the effectiveness of our proposed PRADA-TF scheme in four variants against three baseline methods in the literature. Our analysis focuses on three performance metrics for studying OSNs: social welfare, privacy loss, and team diversity. [ABSTRACT FROM AUTHOR]

The article defines Quality 4.0 as the "future of quality within the context of the exponential growth of technological advancement. Topics include the integration of smart sensors and interconnected devices for real-time data collection, the importance of establishing strong foundational practices like total quality management (TQM), lean, and Six Sigma to support Quality 4.0 initiatives, and the need for collaboration among continuous improvement teams.

ABSTRACT: Background and Aim: Team-based learning (TBL) is an instructional strategy that has gained popularity in various educational settings, including anatomy courses. It is an active learning approach that encourages students to work collaboratively in small teams to solve problems, discuss concepts, and apply their knowledge. Therefore, the purpose of this study was to assess how well students engaged with and learned about anatomy through the medium of TBL. Materials and Methods: The current study was carried out for phase I MBBS students in the Department of Anatomy at KAHER's J N Medical College, Belagavi. TBL sessions included 65 students. One week prior, students were provided pertinent reading materials for the gross anatomy topics that were chosen. The Pre-TBL test, which was composed of five MCQs based on case scenarios, was administered before the start of the session. Students completed the Individual Readiness Assurance Test (IRAT), which had ten multiple-choice questions (MCQs) at the start of the class. The students were then put into groups where they collaborated on the same MCQs, talked about the topics, and provided identical answers. This is called the Group Readiness Assurance Test (GRAT). A post-TBL test was given after the case scenario discussion to wrap up the session. This test's questions were the same as those on the Pre-TBL. Results: The effect sizes provide insights into the magnitude of change observed between pre-TBL and post-TBL test scores. In this study, significant statistical differences were found between pre and post-TBL tests across all sessions [ Table 1 , P < 0.001], indicating larger effect sizes associated with more significant improvements in learning outcomes attributed to TBL. This aids in interpreting the statistical significance of the results. Conclusion: TBL can help students achieve academic success through teamwork. Study revealed that TBL is one of the most effective ways to teach anatomy and can complement traditional didactic lectures. [ABSTRACT FROM AUTHOR]

Global warming, mainly caused by human activities, demands urgent reduction of greenhouse gas emissions. Establishing a carbon market central to this effort involves the allocation of carbon emission quotas. The uncertainty of consensus cost in the carbon market will bring the risk of loss to the whole consensus process. In this paper, we focus on solving the problem of group consensus decision with risk averse decision maker. First, three new distributionally robust two-stage minimum asymmetric adjustment cost consensus models based on conditional value at risk (CVaR) are proposed. Considering that it is difficult to obtain historical decision-making data with risk in the carbon market, a novel box ambiguous set and a polyhedron ambiguous set are constructed, respectively. The risk expectation cost of group consensus decision-making problem under the worst-case condition is measured. Then, a computable linear equivalent form of the proposed model is derived in order to facilitate calculation. Finally, numerical cases based on carbon emission quotas are carried out. The numerical results show that the consensus cost of this method is better than the results under the stochastic programming method, and it brings new solutions to the group decision-making progress in the allocation of carbon emission quotas. [ABSTRACT FROM AUTHOR]

Economic dispatch is an important issue in the management of power systems and is the current focus of specialists. In this paper, a new metaheuristic optimization algorithm is proposed, named Social Small Group Optimization (SSGO), inspired by the psychosocial processes that occur between members of small groups to solve real-life problems. The starting point of the SSGO algorithm is a philosophical conception similar to that of the social group optimization (SGO) algorithm. The novelty lies in the introduction of the small group concept and the modeling of individuals' evolution based on the social influence between two or more members of the small group. This conceptual framework has been mathematically mapped through a set of heuristics that are used to update the solutions, and the best solutions are retained by employing a greedy selection strategy. SSGO has been applied to solve the economic dispatch problem by considering some practical aspects, such as valve-point loading effects, sources with multiple fuel options, prohibited operating zones, and transmission line losses. The efficiency of the SSGO algorithm was tested on several mathematical functions (unimodal, multimodal, expanded, and composition functions) and on power systems of varying sizes (ranging from 10-units to 1280-units). The SSGO algorithm was compared with SGO and other algorithms belonging to various categories (such as: evolution-based, swarm-based, human behavior-based, hybrid algorithms, etc.), and the results indicated that SSGO outperforms other algorithms applied to solve the economic dispatch problem in terms of quality and stability of the solutions, as well as computation time. [ABSTRACT FROM AUTHOR]

This document emphasizes the importance of collaboration and teaming among early intervention professionals in supporting infants and toddlers with delays/disabilities and their families. It highlights the need for professionals to engage in joint decision-making, trust one another, and respect differences in values or culture. The document provides resources and strategies to support effective collaboration, including online training modules, checklists, and podcasts. It also emphasizes the importance of preparing for collaboration, finding time and space to collaborate, using evidence-based practices, documenting plans, establishing communication protocols, and monitoring progress as a team. The authors stress the complexity of collaboration in early intervention and the need for consistent attention to collaboration as part of service delivery. [Extracted from the article]

Background: Globalization has significantly impacted the field of medical education. Medical students need to be more competent to adapt to diverse patient populations and medical practices, understanding the cultural differences, and overcoming the language barriers. While the cognitive domain is important, it cannot fulfill the other domains of learning (affective, psychomotor). Therefore, medical education has to be enhanced to produce well-rounded health-care professionals. Integrating traditional teaching with interactive learning methods can bridge the gap between theoretical knowledge and practical application. Flipping the classroom is one such interactive teaching method that prioritizes the needs, interests, and autonomy of students. By organizing the students into small teams to solve problems and discuss cases, it additionally encourages accountability within the team, which further enhances enthusiasm and commitment to their studies. Aim and Objective: This team-based learning (TBL) in the settings of the flipped classroom (FCR) model can promote self-directed learning (SDL), critical thinking, and pursuing lifelong learning, which was the objective of the present study. Materials and Methods: The study was conducted on 200 undergraduate 1st-year MBBS students, 100 each in the study and control groups. The study group was subjected to flipped classroom (FCR) and the control group to traditional large group teaching. A pre-class assessment (pre-test) using Google Forms, including MCQ, open-ended questions, and case-based physiology, was administered to both the study and control groups of students. In the study group, the team test was conducted in the class. Both groups were subjected to a post-test to evaluate the efficacy of the flipped classroom over the traditional lecture. Qualitative feedback and a focus group interview were taken from the study groups to get the insight of the students regarding the flipped classroom. The incorporation of seminars in the study group further enriched the contents with lucidity of concepts. Results: In the comparison of the pre-test and post-test assessment scores, there was a weighty improvement after the post-FCR session among the study group. Furthermore, the comparison of the pre-test and team-test scores revealed the effectiveness of TBL among the study group students. Thus, the amalgamated approach of FCR with TBL enhances student engagement and comprehension of course material. It also boosts SDL, empowering the students to take ownership of their learning journey. Discussion: Thus, employing a combination of teaching and learning techniques can be more promising than didactic teaching for cultivating medical students with SDL abilities, and certainly flipped classroom and TBL can improve learning outcomes in medical education, creating a more engaging and stimulating learning environment. [ABSTRACT FROM AUTHOR]

In the future of higher education, student learning will become more virtual and group-oriented, and this new reality of academic learning comes with challenges. Positive social interactions in virtual synchronous student learning groups are not self-evident but need extra support. To successfully support positive social interactions, the underlying group processes, such as collaborative group engagement, need to be understood in detail, and the important question arises: How can collaborative group engagement be assessed in virtual group learning settings? A promising methodological approach is the observation of students' non-verbal behavior, for example, in videoconferences. In an exploratory field study, we observed the non-verbal behavior of psychology students in small virtual synchronous learning groups solving a complex problem via videoconferencing. The groups were videorecorded to analyze possible relations between their non-verbal behaviors and to rate the quality of collaborative group engagement (QCGE). A rating scheme consisting of four QCGE dimensions (Behavioral, Social, Cognitive, and Conceptual-to-consequential QCGE) was applied, and non-verbal behaviors during the task were coded based on related research literature. We quantitatively and qualitatively analyzed non-verbal behaviors as indicators of QCGE. The results show that groups use a wide range of nonverbal behaviors. Furthermore, certain non-verbal behaviors are significantly related to specific dimensions of QCGE. These results help to identify relevant indicators of QCGE in virtual synchronous learning settings and thus promote the development of advanced methods for assessing QCGE. Furthermore, the indicators can be discussed as possible anchors for supporting collaborative learning in virtual synchronous groups. [ABSTRACT FROM AUTHOR]

Background: A collaborative approach is critical in inclusive research and should incorporate taking time to build relationships with co-researchers based on trust and shared understanding. Involvement can often be seen as tokenistic and in order to avoid this, it is important to provide opportunities for people to exercise choice throughout the research process. Main body: The current paper outlines learnings from a co-researcher training process for young people with disabilities to identify the ways in which meaningful choice can be facilitated with this group. While conducting training of co-researchers in topics such as research methods, we were continuously led by the group with regards to the directions that the sessions took and promoted problem solving with the group to accommodate the unique needs of all members. The overall aim of a wider project was to develop research capacity in a group of young people with disabilities through co-researcher training and this paper will report on learnings from this work with regards to how we sought to provide opportunities for the co-researchers to exercise choice within research projects. Feedback from the group of young people highlighted the variety of needs and expectations that must be accommodated in such a process and therefore, allowing them to dictate the extent and manner of their engagement is key. Young people with disabilities are a heterogeneous group and therefore, some methodologies and ways of working required adaptation in order to facilitate meaningful choice and engagement for all. Conclusion: Providing meaningful opportunities for demonstrating their choices, in relation to elements of research projects, is a critical component of facilitating a rights-based approach when conducting co-research and requires researchers to cede some level of control over the research process to co-researchers. This can be difficult to achieve in practice and researchers must continuously reflect on their own practice and be willing to change and adapt throughout the process. Plain English summary: It is very important that all members of society can be part of research teams so that researchers can develop projects that will provide good outcomes, however, not all people with disabilities have opportunities to take part in research projects. This project supported a group of young people with disabilities to develop research skills through a set of workshops and this paper describes what the researchers learned from these workshops. This paper will help researchers to understand how best to capture the voices of this population for future research. Young people with disabilities are all different and must be supported to give their opinion in the way that suits them best. Researchers must always think about the ways that they work and change how they work if it is not good for supporting young people with disabilities to work with them on research projects. [ABSTRACT FROM AUTHOR]

This study's objective was to create a hybrid learning-integrated remote laboratory model with validity and practicality. This model has four learning spaces, namely live synchronous, virtual synchronous, self-paced asynchronous, and collaborative asynchronous, so it can support flexible learning. Besides that, this learning model is also based on cognitivism, connectivism, constructivism, behaviourism learning theories and Bloom's digital taxonomy. The hybrid learning integrated remote laboratory model consists of six syntaxes: 1) issue; 2) investigation; 3) team discussion to solve problems; 4) experiment using a remote laboratory; 5) analysis and evaluation; and 6) explore new solutions. Focus group discussions (FGD) were used to collect high-quality data by seven experts in learning models, vocational education, language and technology. The hybrid learning-integrated remote laboratory model quality analysis used Aiken's V. The result showed that the hybrid learning integratedremote laboratory model content is valid, with a validity value of 0.87. The practicality analysis result showed that the average percentage of the assessments from lecturers and students was 88.16%, so it can be concluded that it has a high validity value and is very practical. [ABSTRACT FROM AUTHOR]

This article discusses the implementation of a clinical reasoning blueprint in health professions education. The blueprint was developed to guide educators in aligning teaching tools with individual learner's needs. The article describes the process of engaging educators in a professional development series to address barriers and develop knowledge translation resources. The blueprint divides the learner continuum into four performance levels and provides detailed descriptions of each level. The article also provides guidance on how to use the blueprint, including assessing the learner's clinical reasoning abilities and connecting assessments with teaching strategies. The research team developed resources to help educators understand and apply the blueprint, emphasizing the importance of ongoing support for educators. The resources are intended to empower clinicians to enhance their role as educators and improve clinical reasoning abilities in future healthcare providers. [Extracted from the article]

The article presents a seven-step method to enhance customer relationships through Area Activity Analysis (AAA). Topics discussed include the development of aligned mission statements for work teams, defining and documenting performance standards and feedback mechanisms, and creating strong internal and external supplier/customer relationships.

This article provides an overview of the advancements in immersive technologies, such as mixed reality, virtual reality, and augmented reality, and their potential for enhancing skills development and education. It discusses the concept of multimodal and immersive learning systems, which combine different modes of interaction with artificial intelligence to create personalized learning experiences. The article also introduces a special section that showcases research on multimodal and immersive systems in various domains, including collaboration, mathematics, reading comprehension, art appreciation, and skills development. The papers in the special section highlight the diversity of applications and the impact of these technologies on different skills and learning scenarios, while also identifying gaps and challenges for future research. Overall, the findings contribute to our understanding of the potential for enhanced learning experiences and personalized skill development through multimodal and immersive technologies. [Extracted from the article]

This descriptive study focuses on using voice activity detection (VAD) algorithms to extract student speech data in order to better understand the collaboration of small group work and the impact of teaching assistant (TA) interventions in undergraduate engineering discussion sections. Audio data were recorded from individual students wearing head‐mounted noise‐cancelling microphones. Video data of each student group were manually coded for collaborative behaviours (eg, group task relatedness, group verbal interaction and group talk content) of students and TA–student interactions. The analysis includes information about the turn taking, overall speech duration patterns and amounts of overlapping speech observed both when TAs were intervening with groups and when they were not. We found that TAs very rarely provided explicit support regarding collaboration. Key speech metrics, such as amount of turn overlap and maximum turn duration, revealed important information about the nature of student small group discussions and TA interventions. TA interactions during small group collaboration are complex and require nuanced treatments when considering the design of supportive tools. Practitioner notesWhat is already known about this topic Student turn taking can provide information about the nature of student discussions and collaboration.Real classroom audio data of small groups typically have lots of background noise and present challenges for audio analysis.TAs have little training in how to productively intervene with students about collaborative skills.What this paper adds TA interaction with groups primarily focused on task progress and understanding of concepts with negligible explicit support on building collaborative skills.TAs intervened with the groups often which gave the students little time for uptake of their suggestions or deeper discussion.Student turn overlap was higher without the presence of TAs.Maximum turn duration can be an important real‐time turn metric to identify the least verbally active student participant in a group.Implications for practice and/or policy TA training should include information about how to monitor groups for collaborative behaviours and when and how they should intervene to provide feedback and support.TA feedback systems should keep track of previous interventions by TAs (especially in contexts where there are multiple TAs facilitating) and the duration since previous intervention to ensure that TAs do not intervene with a group too frequently with little time for student uptake.Maximum turn duration could be used as a real‐time metric to identify the least verbally active student in a group so that support could be provided to them by the TAs. [ABSTRACT FROM AUTHOR]

Computational thinking is recognized as a critical competency in contemporary education, preparing individuals to tackle complex challenges in a digitally pervasive world. In this quasi-experimental design study with pretest and post-test measures, the possibility of developing computational thinking from the field of didactics of mathematics in higher education students was investigated. This was performed via a problem-based learning (PBL) methodology using problem solving in the experimental group or, alternatively, focused on the analysis of solved problems in the control group. After the intervention, the control group experienced a statistically significant improvement in the scores obtained in the post-test measure. Thus, PBL and problem solving did not lead to an improvement in the students' computational thinking, whereas the analysis of solved problems approach did. Therefore, the results suggested the potential benefits of this latter methodology for teaching computational thinking. [ABSTRACT FROM AUTHOR]

With the rapid development of the fashion industry and the increasing diversification of consumer needs, accurately predicting the fashion trends of clothing elements has become an urgent problem in the field of art design. In order to solve these problems, this paper uses deep learning technology for fashion trend prediction and optimization of prediction models. Firstly, the EfficientNet-b7 model is constructed as an attribute predictor to accurately extract the attributes of clothing image elements. Then, based on user information, popular elements are grouped and counted to solve the problem of different populations having different opinions on popular trends. The prediction model is constructed based on the bidirectional long short-term memory network encoder decoder framework, which trains trend information as a whole and utilizes element coexistence relationships to assist in trend prediction. Meanwhile, the study uses random sampling method for clothing original adoption, and the experimental results show that the model considering coexistence relationship performs the best in terms of mean absolute error and mean absolute percentage error, which are 0.0132 and 14.68%, respectively. In addition, the model that introduces attention mechanism based on trend similarity has improved by 5.09% and 4.54% on two indicators compared to the latest model. The experimental results indicate that coexistence relationships can help improve the performance of prediction models. The attention mechanism based on trend similarity can further improve model performance by similarity comparison between historical information and changing trend, and selecting similar historical information as an important influencing factor for future trends. [ABSTRACT FROM AUTHOR]

Background: Despite positive outcomes in controlled trials, organizations have been slow to adopt health literacy practices. The purpose of the Communicate to CARE (Clear Communication, Achieve Understanding with Teach-Back, Receptive to our patient family needs, Empathetic care delivery) study was to use theories and strategies from implementation science to scale up health literacy practices in a pediatric Ears, Nose, and Throat (ENT) clinic. Brief Description of Activity: Expanding on previous efforts that simply reflected on barriers, the CARE team identified barriers within the local context pre-implementation to select strategies to directly address barriers during health literacy implementation. The RE-AIM framework was used to evaluate the reach, effectiveness, adoption, implementation, and maintenance of health literacy practices. Implementation: Over 18 months, the CARE team delivered multiple implementation strategies, including external facilitator, microlessons, preparing champions, audit and feedback, local consensus discussions, and small test of change. We tailored health literacy practices to clinic team roles to accommodate the clinic workflow. Results: ENT team mean ratings on acceptability, appropriateness, and feasibility remained >4 indicating a high likelihood of successful implementation. Caregiver always ratings significantly increased from baseline to 12 months for easy-to-understand medication instructions (74%–96%), test results (54%–96%), know what to do if had questions (89%–96%), and encouraged to talk about health problems (76%–90%). Caregiver ratings dropped slightly at 18 months, indicating a need for booster training. While one third of caregivers reported Teach-Back practice across all time periods, the ENT team reported increased practice from baseline (42%), 6 (61%) and 12 months (70%). Lessons Learned: Over the first 12 months, the external facilitator delivered implementation strategies with weekly contact, tapering contact over the final 6 months. The local champion became engaged in the CARE study through a quality improvement project with meaningful outcomes for the clinic and an incentivization program for scholarly endeavors. Lunch and learn sessions helped build relationships between the CARE and ENT team to discuss and problem solve issues. The 5-item CAHPS health literacy composite proved to be sensitive to changes during implementation of health literacy practices. Integrating these items into standard follow up surveys with patients and families would help realize the return on investment for health literacy implementation. [HLRP: Health Literacy Research and Practice. 2024;8(3):e166–e174.] [ABSTRACT FROM AUTHOR]

Despite the fact that several technologies have been developed to assist healthcare workers in reducing errors and improving accuracy in illness diagnosis, there is still substantial ambiguity regarding the accurate disease diagnosis based on symptoms. The goal of this work is to establish a group decision-making problem in an uncertain situation to assist medical practitioners in generating accurate illness predictions based on symptoms. This study proposes a novel distance measure for Pythagorean fuzzy sets that incorporates the inherent uncertainty of complex, uncertain data by incorporating indeterminacy in the computation. First, we establish the proposed Mabala distance measure by describing it's properties. Then, the suggested distance measure is applied to solve group decision-making problems in uncertain situations. A case study of disease analysis based on symptoms is presented to illustrate the decision-making procedure involving four medical professionals, five symptoms, and five probable diseases. Furthermore, We have presented two cases of disease analysis using non-standard and standard Pythagorean fuzzy soft matrices. The results suggest that the proposed Mabala's distance measure has great potential for improving disease analysis. The proposed Mabala distance measure is compared to five existing distance measures using an identical data set of prospective disease symptoms. The comparative analysis indicates that the suggested Mabala distance measure's result almost coincides with the results of the other distance measurements. A set of sensitivity analysis is provided to analyze the durability and consistency of the proposed distance measurements across different input scenarios. [ABSTRACT FROM AUTHOR]

Cloud applications are built from a set of components often deployed as containers, which can be deployed individually on separate Virtual Machines (VMs) or grouped on a smaller set of VMs. Additionally, the application owner may have inhibition constraints regarding the co-location of components. Finding the best way to deploy an application means finding the best groups of components and the best VMs, and it is not trivial because of the complexity coming from the number of possible options. The problem can be mapped onto may known combinatorial problems as binpacking and knapsack formulations. However, these approaches often assume homogeneus resources and fail to incorporate the inhibition constraints. The main contribution of this paper are firstly a novel formulation of the grouping problem as constrained Coalition Structure Generation (CSG) problem, including the specification of the value function which fulfills the criteria of a Characteristic Function Game (CFG). The CSG problem aims to determine stable and disjoint groups of players collaborating to optimize the joint outcome of the game, and a CFG is a common representation of a CSG, where each group is assigned a value and where the value of the game is the sum of the groups' contributions. Secondly, the Integer-Partition (IP) CSG algorithm has been modified and extended to handle constraints. The proposed approach is evaluated with the extended IP algorithm, and a novel exhaustive search algorithm establishing the optimum grouping for comparison. The evaluation shows that our approach with the modified algorithm evaluates on average significantly less combinations than the CSG state-of-the-art algorithm. The proposed approach is promising for optimized constrained Cloud application management as the modified IP algorithm can optimally solve constrained grouping problems of attainable sizes. [ABSTRACT FROM AUTHOR]

The faculty at Massachusetts College of Pharmacy and Health Sciences (MCPHS) recognized the potential benefits for collaborative teaching and learning across disciplines. Research and development of an Interprofessional Case Learning Project (ICLP) laid the foundation for the creation and implementation of a problem-based learning course. This course, Interdisciplinary Perspectives of an Infectious Disease—Malaria, introduced innovative interdisciplinary pedagogy into the curriculum of the MCPHS School of Arts & Sciences. Designed by biologists, chemists, historians, and public health faculty, this course introduced senior pre-medicine majors to varied methods and theories as they worked in groups to solve problems related to the incidence, prevention, and treatment of an infectious disease. Students' deliverables included journal-quality research papers and posters presented at the MCPHS School of Arts & Sciences Undergraduate Research Conference. [ABSTRACT FROM AUTHOR]

This article explores the value of consulting religious traditions in liberal democratic states. It argues that religious traditions can provide valuable resources for problem-solving and offers four nonreligious, epistemic reasons for consulting them. These reasons include the continuity of transmission, the test of time, the intergenerational learning process, and the result of moral inquiry. The article emphasizes that while there are epistemic reasons for consulting religious traditions, other factors such as unjust discrimination may outweigh them. The analysis has implications for the role of religious traditions in political or ethical problem-solving in liberal democratic states. [Extracted from the article]

When solving problems, mathematically gifted individuals tend to internalize intuitive ideas and approaches, and to shorten their reasoning. Consequently, for teachers it is difficult to observe gifted students' mathematical reasoning in the context of problem solving. In this paper we investigate nine gifted Swedish 9th grade students' mathematical reasoning during problem solving in small groups at vertical whiteboards. The data consists of 5 filmed group-activities, that were analysed according to a framework of collaborative problem-solving (Roschelle & Teasley, 1995). The analysis shows that every group solved proposed problems successfully within different socially negotiated Joint Problem Spaces (JPS) and, importantly, that students were able to verbalize and display their mathematical reasoning. Additionally, it is indicated that using vertical whiteboards facilitated considerably the exhibition of students' mathematical reasoning. [ABSTRACT FROM AUTHOR]

The dual probabilistic linguistic term sets (DPLTSs) are more effective than PLTSs in solving the problem of multi-attribute group decision-making (MAGDM). In this paper, an improved TOPSIS method is developed combining the TOPSIS method and projection measure of DPLTS to supplement the existing research. Firstly, considering the mathematical characteristics of DPLTS, this paper defines the concepts of the module, cosine function, and projection of DPLTS, and then proves the mathematical properties of the cosine function. Secondly, considering the uncertainty of decision-making problems, the weight-solving models are established respectively under the condition that the weight information is completely unknown and partially known. Furthermore, a novel DPLPrj-TOPSIS approach is established based on the projection measure proposed. It involves integrating experts' DPLTS evaluations, normalizing different DPLTSs, calculating alternatives' relative closeness and score, etc. Secondly, the proposed method's feasibility is demonstrated through a case study that entails selecting network promotion plans for food manufacturers. Finally, the proposed method's effectiveness and validity are verified by comparing and analyzing it with the traditional TOPSIS method based on a distance measure and other existing decision methods. [ABSTRACT FROM AUTHOR]

To clarify the effect of team state on complex problem solving performance, drawing from the complex problem solving interaction theory, a two-factor mixed experimental design was adopted to explore the impact of collective psychological capital (team state), problem situation complexity on team and individual problem-solving performance through two experiments. Data were gathered through a sample of 390 members of 130 teams, with 66 teams in the experimental group and 64 teams in the control group. The results showed that the main effects of collective psychological capital and problem situation complexity on both team and individual member problem-solving performance were significant, and the interaction effects were also significant. Further simple effects analysis revealed that teams with higher collective psychological capital had less negative effects of problem situation complexity on problem-solving performance. This finding indicates that collective psychological capital has a positive effect on the teams and individual members of complex problem-solving performance, and that the negative effects of problem situation complexity are buffered in teams with higher collective psychological capital. This finding is also indicates that the team itself, as a microenvironment, has a multifaceted impact on the psychology and behavior of the team and individual members. At the same time, the formation of an effective team allows investment in terms of collective psychological capital, which is one way to increase and maintain competitiveness and a cost-saving optimization option. [ABSTRACT FROM AUTHOR]

Computer-Assisted Instruction (CAI) is a type of teaching strategy or remediation using the computer. CAI is interactive and can illustrate a concept through attractive animation, sound, and demonstration. It engages the students to learn at their own pace and work individually or problem solve in a group. CAI can provide immediate feedback and letting students know whether their answer is correct. This study determined the effectiveness of Computer-Assisted Instruction (CAI) in teaching Grade 9 Mathematics, Talamban National High School (TNHS), Cebu City, SY 2018-2019. The findings served as basis of the proposed action plan. Specifically, the study looked into the pretest performances of the control and experimental groups; the posttest performances of the control and experimental groups; the significance of the differences between the pretest and posttest performances of the control and experimental groups; there significance of the difference in the mean gain performances of the control and experimental groups. The study utilized the quasi-experimental design with the use of a teacher-made test to gather data for analysis and interpretation. This study was conducted in Talamban National High School (TNHS) situated in Talamban, Cebu City. The research subjects of this study were the grade 9 students of Talamban National High School. Two sections were chosen as control and experimental groups of the study. This study utilized a teachermade test which was validated by the co-teachers and math coordinators. The test covered the topics on quadrilaterals. The test was comprised of a 40-item multiple choice type of test items. The researcher-made test was used in gathering data on the pretest and posttest performances of the research subjects. The study used frequency count, per cent, t-test for correlated samples, t-test for independent samples in summarizing, analyzing and interpreting the data. [ABSTRACT FROM AUTHOR]

The study examined the implementation of mixed augmented and virtual reality in an animated flipped classroom for low-achieving writers in rural primary schools. A quasi-experimental study employing a pre-post, non-equivalent control group design was conducted. In a flipped classroom, students use digitised or online lectures as preclass homework, then engage in active learning process in the classroom such as peer teaching, projects, problem solving and group activities. In other words, the typical classroom of lecturing only during class time is “flipped” now to active activities that involve problem solving and group project work in class. Results show that this flipped learning approach improves rural learners’ proficiency, particularly in vocabulary acquisition. It was found that there was a statistically significant difference in vocabulary level between trials at the midway time point. Apart from that, there is a statistically significant difference in the vocabulary level in the intervention trial at the end (post) of the trials. However, successful implementation of this technology necessitates a comprehensive approach considering cultural and infrastructural factors. [ABSTRACT FROM AUTHOR]

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