Collaborative Problem Research Articles

Exploring Collaborative Problem Solving in STEM Contexts for Middle School Students

In the 21st century, advances in science and technology require students to adapt. Classroom dynamics are still often dominated by passive learning methods, such as lectures, which result in a lack of student participation. PISA evaluation shows the low problems-solving abilities of Indonesian students. Therefore, the STEM approach emerged as a solution with a focus on essential 21st century skills, such as collaborative problem solving. The aim of the research is to investigate and analyze the collaborative problem solving process of junior high school students in solving STEM problems. The research method used is a case study with a qualitative approach. The research population included all students of SMP PGRI 13 Surabaya. Data collection techniques include observation, interviews, and questionnaires. Research instruments include questionnaires, observation sheets, and structured interviews. The research results show that collaborative learning in STEM context problem solving can help students develop problem solving skills. Students demonstrate good collaborative skills and their response to this learning is very positive. Evaluation of student learning outcomes also shows an increase, indicating the success of STEM-based learning methods in increasing student motivation. This research contributes to the understanding of the importance of learning approaches that integrate STEM with collaborative problem solving.

Core-based cost allocation for collaborative multi-stop truckload shipping problem

With the recently emerged digital platforms in logistics, shippers can easily collaborate by bundling their heavy less-than-truckload orders via multi-stop truckload shipping to reduce transportation cost. The platform has responsibility for planning the shipping routes, for bundling the orders and fairly allocating the cost to shippers. To address this challenging problem in practice, we propose a new cooperative game based on a variant of a pickup and delivery model with soft time windows for shipper collaboration. The centralized optimization model is NP-hard and the core of the game may be empty. We adopt the least-core concept and simplify the core stability constraints as route-wise conditions. Based on theoretical results, we propose an innovative route-generation joint searching algorithm that iteratively solves the centralized optimization and least-core allocation problems at the same time, where the route-generation subproblem is solved by a customized multi-start local search subroutine. Extensive computational experiments on a real-world case demonstrate that the proposed algorithm can quickly generate a near-optimal solution with minor optimality gap and a least-core allocation with small stability deviation. With our algorithm, the shippers also receive substantial cost savings from collaboration.

CVDMARL: A Communication-Enhanced Value Decomposition Multi-Agent Reinforcement Learning Traffic Signal Control Method

Effective traffic signal control (TSC) plays an important role in reducing vehicle emissions and improving the sustainability of the transportation system. Recently, the feasibility of using multi-agent reinforcement learning technology for TSC has been widely verified. However, the process of mapping road network states onto actions has encountered many challenges, due to the limited communication between agents and the partial observability of the traffic environment. To address this problem, this paper proposes a communication-enhancement value decomposition, multi-agent reinforcement learning TSC method (CVDMARL). The model combines two communication methods: implicit and explicit communication, decouples the complex relationships among the multi-signal agents through the centralized-training and decentralized-execution paradigm, and uses a modified deep network to realize the mining and selective transmission of traffic flow features. We compare and analyze CVDMARL with six different baseline methods based on real datasets. The results show that compared to the optimal method MN_Light, among the baseline methods, CVDMARL’s queue length during peak hours was reduced by 9.12%, the waiting time was reduced by 7.67%, and the convergence algebra was reduced by 7.97%. While enriching the information content, it also reduces communication overhead and has better control effects, providing a new idea for solving the collaborative control problem of multi-signalized intersections.

Developing Integrated STEM Teacher Identity Using Guided Curation of K-12 Engineering Activities

Abstract This qualitative study examines how guided curation develops practicing teachers’ task perception as a dimension of integrated STEM teacher identity. Task perception is defined as teachers’ beliefs about their roles and responsibilities as integrated STEM educators. Analysis of teachers’ (n = 22) guided curation assignments from a graduate STEM education course revealed how teachers perceived their roles and responsibilities as integrated STEM educators and potential challenges to integrating STEM. These beliefs included the importance of centering the engineering design process, encouraging collaborative problem solving, providing productive mistake-making opportunities, and connecting mathematics and engineering. Teacher educators’ adaptation of a freely available STEM lesson specification tool scaffolded the collection and analysis of curriculum resources. This adaptation was instrumental in developing task perception. Implications for building elementary teachers’ capacity to implement and sustain innovative integrated STEM instruction are offered.

Speech analysis of teaching assistant interventions in small group collaborative problem solving with undergraduate engineering students

AbstractThis 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.

Collaborative strategy research of target tracking based on natural intelligence by UAV swarm

Regarding the regional area target collaborative tracking problem widely existing in intelligent scenarios, this paper built a distributed UAV swarm framework inspired by natural intelligence to heighten intricate missions’ efficiency. Also, a standoff collaboratively continuous tracking strategy was proposed based on a lateral guidance law with an improved Reference Point Guidance (RPG) and a longitudinal guidance law with an improved phase collaboration. Under an uncertain environment, this framework used an improved bat algorithm (IBA) to optimize the speed allocation of the UAV swarm’s online control strategy with information consensus estimation. Compared with a case without the designed transformation, statistically, the results demonstrate that the framework operates efficiently and robustly in phase error convergence, swarm flight distance, and fuel consumption, where a dynamic target exists.

The EMPOWER program: a history and guide for increasing diversity using integrated research and education.

The prevalence and pervasive nature of emerging chemicals of concern have created widespread environmental injustice apprehensions in vulnerable communities. To alleviate and address these concerns, identifying, engaging, and training a diverse environmental health research workforce will be critical and necessary steps to combat and prevent the consequences of environmental injustice. While there is an obvious need to enhance diversity in environmental health research, this process is hampered by facets of systemic racism that reduce access to educational resources needed to build interest and knowledge in students and teachers. We present here a historical perspective to offer a guide for building programs and relationships with underserved schools to help overcome limiting factors that have plagued certain public school systems. With the proper training and mentorship, the untapped workforce present within these schools will be empowered to understand and address current and emerging environmental health and safety threats. Through this transformative 8-week high school research program, we will develop well-prepared, ethical researchers committed to scientific inquiry, intensive fieldwork, and collaborative problem solving to address environmental health challenges. Following the four-step risk assessment process, students, teachers, and faculty mentors will work collaboratively to identify toxicants, potential hazards and risks, and environmental disparities in urban neighborhoods, which provides the necessary training to formulate critical thinking skills for use in academic or nonacademic careers.NEW & NOTEWORTHY The Engaging Multidisciplinary Professional Opportunities for Women in Environmental Research (EMPOWER) program is a one-of-a-kind research summer experience for minority female high school students in the state of Georgia. In addition, this program provides high school teachers with hands-on experiences that can be adapted to use in the classrooms. This combination of lab and field research immerses participants in understanding urban environmental exposures and their health effects. The EMPOWER program was established to meet the critical need for increasing diversity in science, technology, engineering, and mathematics.

Promoting Students’ Conceptual Understanding through Directive Teacher Guidance and Non-directive Teaching Model in a Collaborative Problem Solving

This study examined and aimed to promote students’ level of conceptual understanding in problem-solving through Directive Teacher Guidance (DTG) and the Non-directive Teaching Model (NDTM). The study employed sequential-explanatory mixed research using quasi-experimental switching replication treatment involving quantitative and qualitative data. Two groups of Grade 8 high school students were used as participants in the study. One group had 36 students, and the other had 38 students exposed to DTG and NDTM approaches. Quantitative data were obtained from the scores of students from the pretest, first posttest, and second posttest about the topics in Mathematics subject. Qualitative data were from the analysis of participants’ responses in problem-solving, focus group discussion, and classroom observation. The t-test for the dependent sample was utilized to determine the significant change in students’ level of conceptual understanding after the first and second implementation phases, while the t-test for the independent sample was used to find out the significant difference between the pretest and posttests between the two groups. Results showed that students’ conceptual understanding and problem-solving skills significantly improved after exposure to the two approaches. The two groups’ levels of conceptual understanding in switching replication treatment are found to have no significant difference. Moreover, participants preferred guided questions and guided directions and believed in the importance of attaining a conceptual understanding of the lesson. It is suggested to integrate DTG and NDTM as teaching strategies and conduct a study considering a longer span of implementation to determine the effectiveness of the two approaches.

Innovative Practices in Teachers’ and Students’ Trainings

The present work is dedicated to innovative educational practices, that are based on teaching and learning through the methods of science, technology, engineering and mathematics (STEM), combined with the use of various digital tools. These project activities are suitable for implementation in the learning process both in face-to-face and distance learning. They enable the development of communication and collaboration skills, critical thinking and problem solving, creativity and innovation, as well as the creation of digital content. The described practices and digital tools are used in the preparation of teachers in various qualification courses conducted by the Department for Qualification and Professional Development of Pedagogical Specialists at University of Plovdiv “Paisii Hilendarski”. They are used and implemented also in education of students in lower-secondary schools (5th – 7th grade). The teaching practices are based on the results of a three-year study of already trained Bulgarian teachers, who try them daily in the learning process and notice their positive impact on students. The article describes a realized STEM project, named “European countries”.

Impact of Problem-Based Learning on the Perception, Understanding, and Application of Statistical Concepts in Business Administration and Management Students

We investigate the impact of the Problem-Based Learning (PBL) approach supplemented by student-created videos on the teaching of Probability Calculation and Inference Statistics in Business Administration and Management Studies. To that aim, we analyse survey results gathered from a sample of students from the Business Administration and Management and Law-Business Administration and Management degrees at the University of Zaragoza who participated in the PBL experience, focusing on their perceptions, skill development, dynamics of collaboration, and communication with teachers and classmates. Additionally, a comparative study of their final grades is conducted, comparing them with that obtained by students who did not engage in PBL. Our findings reveal that PBL significantly enhances students’ understanding and motivation, along with their communication with teachers, particularly among repeating students, and teamwork skills, particularly among beginners. Furthermore, students participating in PBL activities tended to achieve significantly higher grades than their nonparticipating counterparts did. Moreover, our study contributes to the discourse on sustainable education by highlighting the potential of PBL as a pedagogical approach aligned with the Sustainable Development Goals. By promoting active learning, collaborative problem solving, and communication skills, PBL fosters a culture of lifelong learning and empowers students to address real-world challenges in a sustainable manner. These findings have implications for educators, policymakers, and researchers seeking to promote sustainability in higher education through effective pedagogical practices.

Exploring student reasoning in statistical mechanics: Identifying challenges in problem-solving groups

Statistical mechanics has received limited attention in physics education research and remains a relatively underrepresented topic even in research on upper-division physics courses. The purpose of this study was to explore potential challenges that physics students encounter when they solve statistical mechanics problems in groups. Adopting a grounded approach, we video recorded and analyzed nine small student groups engaging in collaborative problem solving on the topic. The analysis involved iterative thematic coding, which gave rise to ten emergent categories of challenges. These were later divided into two broad groupings: and . In the first grouping, we list seven identified categories related to the concepts of macrostates and microstates, distinguishable and indistinguishable particles, temperature, entropy, energy, equilibrium, heat bath, the Boltzmann distribution, and the partition function. In the second grouping, we list three categories related to the inappropriate application of common relations, difficulty managing tensions between calculated results and qualitative reasoning, and coming up with definitions of new and inconsistent concepts. Some of our findings are supported by existing research on the topic, and others are previously unreported. Based on our findings, we propose that future research should investigate the relations between the identified challenges on one hand, and students’ epistemological framing, reasoning, and use of multiple representations on the other. Finally, we suggest that teachers should spend time engaging students in a conceptual discussion of the central ideas of statistical mechanics, motivating the choice and pointing out limitations of commonly used toy models, and linking course content to real-world phenomena. Published by the American Physical Society 2024

Secure Video Offloading in MEC-Enabled IIoT Networks: A Multicell Federated Deep Reinforcement Learning Approach

Wireless video offloading in mobile-edge-computing (MEC)-enabled Industrial Internet of Things imposes a risk of exposing users' private data to eavesdroppers. It is difficult for existing secure video offloading schemes to simultaneously guarantee security, reduce latency and energy consumption in privacy-sensitive multi-cell scenarios where users are unwilling to offload data to other cells. In this paper, a secure video offloading scheme based on multi-cell federated deep reinforcement learning (DRL) is proposed to facilitate a secure, real-time and efficient MEC network by efficient orchestration of limited resources. We formulate a collaborative optimization problem of video frame resolution and resources to minimize latency and energy consumption while maximizing the security rate subject to analytic accuracy and limited resources. To solve the formulated NP-hard problem, a multi-cell federated DRL algorithm based on the frameworks of multi-cell horizontal federated learning (FL) and hierarchical reward function-based twin delayed deep deterministic policy gradient (TD3) is proposed. First of all, hierarchical reward function-based TD3 is employed to solve the collaborative optimization NP-hard problem formulated for each single cell, where the optimal solution can be efficiently approached by the agent under the guidance of the innovatively designed hierarchical reward function. Then, multi-cell horizontal FL is applied on TD3 to obtain a model with higher model quality by averagely aggregating multiple individual TD3 models. Simulation results reveal that the proposed algorithm outperforms comparison algorithms in terms of utility, cost, latency, energy consumption and security rate.

Adaptive Collaboration With Training Plan Considering Role Correlation

Based on role-based collaboration (RBC), group role assignment (GRA) optimizes a team’s overall performance by assigning the most appropriate individual agents from the team’s viewpoint based on agents’ role-playing abilities. As an extension of GRA, GRA with a training plan (GRATP) deals with the impact of training on team management. Considering the correlation between roles, the training of one agent on one role also affects the performance of the agent in other roles. Moreover, in the adaptive collaboration (AC) problem, the training time also affects significantly the agent’s ability, as an agent’s ability changes over time. However, the existing GRATP models fail to consider these factors in the collaboration process. Therefore, we aim to address the role-correlation-based adaptive GRATP (RCA-GRATP) in this article. This article contributes two aspects to the literature on AC. 1) RCA-GRATP problem is abstracted based on RBC and GRA. To the best of the authors’ knowledge, this is the first article that explicitly considers role correlation in the RBC problems. 2) A comprehensive formalization of RCA-GRATP and two solving algorithms for diverse situations are proposed to solve the formalized problems. Experiments are carried out to verify the effectiveness of the proposed algorithms in diverse scenarios.

Collaborative Optimization of Learning Team Formation Based on Multidimensional Characteristics and Constraints Modeling: A Team Leader-Centered Approach via E-CARGO

With the massive popularization of e-learning, collaborative learning via learning teams has become indispensable to enhancing the learning efficiency and learning quality of overall learners. The team leader usually plays a key role in collaborative learning. However, the existing research ignores the key characteristics of learners and constraints relevant to e-learners when identifying appropriate team leaders and compatible members. A novel collaborative optimization approach to learning team formation is proposed based on a refined learner model and the environments—classes, agents, roles, groups, and objects (E-CARGO) model. With the proposed approach, a learner is modeled by combining 5-D characteristics (i.e., cognitive ability, leadership, sociability, learning style, and personality) and three types of constraints (e.g., conflicts, genders, and the number of members), and an assessment mechanism is designed to measure the comprehensive abilities of learners for identifying an ideal team leader and selecting the team members for a team. By innovatively introducing the role-based collaboration theory and E-CARGO model, the leader-centered learning team formation problem is formalized as a collaborative optimization problem. The mathematical model and the constraint relations are established for this problem, which is solved based on the IBM CPLEX package. Finally, a case study and experiments demonstrate that the proposed approach is efficient and feasible, in favor of improving the satisfaction degree of learners.

Collaborative Problem Solving and Literacy Practices

Collaborative Problem Solving and Literacy Practices

The Peer Tutor Model with Cognitive Conflict Strategies in Collaborative Learning Mathematical Problem Solving and Its Effect on Individual Achievement.

Cognitive conflict is a condition where students encounter new experiences that are not neatly arranged in their cognitive structure. The process of assimilation between old and new concepts in students' cognition. The assimilation process is more effective when done together with peer tutors. The purpose of this study was to see the effect of cognitive conflict on collaborative mathematical problem solving and its effect on individual learning success. Case study research methods, descriptive qualitative and quantitative. Learning is carried out to 28 semester II students collaboratively in solving integral problems. Pretest knowing the initial ability of mathematics, given the problem of integral functions of fractional powers. Given posttest independently. In the learning process students with low initial abilities and difficult to ask questions with peer tutors dare to ask information that has just been received with the help of students with moderate and high initial abilities. Cognitive conflict strategies with peer tutors are very useful. The impact of the experience of peer tutors, posttest results is very significant. This means that the posttest results increased significantly for the material of integral functions of fractional powers and motivation to learn for the better.

Curricular, interactional, and structural diversity: identifying factors affecting learning outcomes

ABSTRACT Discussions of ‘diversity’ in higher education are often limited to structural diversity (i.e. access to higher education based on demographic variables), which is just one form of diversity that impacts student learning outcomes. Others include curricular diversity (a measure of the interdisciplinary of the coursework) and interactional diversity (a measure of informal interaction). This study aims to explore the effects of all three categories of diversity on the academic performance of college students. Using data from a top higher education institution in Taiwan, we construct indicators of curricular diversity (i.e. curricular evenness (CE), ratio of elective credits (REC), ratio of inter-collegiate credits (RIC), and interdisciplinary experience (IDE). Interactional diversity was measured using student's participation in activities involving interaction with others, and structural diversity was measured by student's personal and contextual characteristics. Our goal was to determine which of these forms of diversity impacted students’ self-reported skills in interdisciplinary communication (ICS) and collaborative problem solving (CPS). Results show that curricular diversity is significantly associated with interactional diversity. In terms of learning outcomes, curricular and structural diversity are associated with both ICS and CPS, while interactional diversity is associated with CPS. Interactional diversity also moderates the relationships between curricular diversity and ICS as well as CPS. This research implies that institutions of higher education need to promote curricular and interactive diversity to fix systemic biases that limit or relate to structural diversity.

Collaborative Path Planning of Multiple AUVs Based on Adaptive Multi-Population PSO

Collaborative operations of multiple AUVs have been becoming increasingly popular and efficient in underwater tasks of marine applications. Autonomous navigation capability and cooperative control stability of multiple AUVs are crucial and challenging issues in underwater environments. To address the collaborative problem of path planning for multiple AUVs, this paper proposes an adaptive multi-population particle swarm optimization (AMP-PSO). In AMP-PSO, we design a grouping strategy of multi-population and an exchanging mechanism of particles between groups. We separate particles into one leader population and various follower populations according to their fitness. Firstly, in the grouping strategy, particles within the leader population are updated by both the leader population and follower populations so as to keep global optimization, while particles within the follower population are updated by their own group so as to keep local priority. Secondly, in the exchanging mechanism, particles are exchanged between the leader population and follower populations so as to improve multi-population diversity. To accommodate multi-population characteristics, an adaptive parameter configuration is also included to enhance the global search capability, convergence speed, and complex environment adaptability of AMP-PSO. In numerical experiments, we simulate various scenarios of collaborative path planning of multiple AUVs in an underwater environment. The simulation results convincingly demonstrate that AMP-PSO can obtain feasible and optimal path solutions compared to classic PSO and other improved PSO, which enable multiple AUVs to effectively achieve objectives under the conditions of collision avoidance and navigation constraint.

Research on multi-robot collaborative operation in logistics and warehousing using A3C optimized YOLOv5-PPO model.

In the field of logistics warehousing robots, collaborative operation and coordinated control have always been challenging issues. Although deep learning and reinforcement learning methods have made some progress in solving these problems, however, current research still has shortcomings. In particular, research on adaptive sensing and real-time decision-making of multi-robot swarms has not yet received sufficient attention. To fill this research gap, we propose a YOLOv5-PPO model based on A3C optimization. This model cleverly combines the target detection capabilities of YOLOv5 and the PPO reinforcement learning algorithm, aiming to improve the efficiency and accuracy of collaborative operations among logistics and warehousing robot groups. Through extensive experimental evaluation on multiple datasets and tasks, the results show that in different scenarios, our model can successfully achieve multi-robot collaborative operation, significantly improve task completion efficiency, and maintain target detection and environment High accuracy of understanding. In addition, our model shows excellent robustness and adaptability and can adapt to dynamic changes in the environment and fluctuations in demand, providing an effective method to solve the collaborative operation problem of logistics warehousing robots.

An offloading method in new energy recharging based on GT-DQN

The utilization of green edge has emerged as a promising paradigm for the development of new energy vehicle (NEV). Nevertheless, the recharging of these vehicles poses a significant challenge in due to limited power resources and enormous transmission demands. A novel architecture based on Wifi-6 communication is proposed, which makes the most of heterogeneous edge nodes to achieve real-time processing and computation of tasks. To address the collaborative power resource optimization problem, the interference between different vehicles is considered, and the task offloading is optimized. In particular, the power contention among recharging clusters is modeled as an exact game and a task offloading strategy model is proposed jointly with the Deep Q-Network (DQN) algorithm, which is employed by a secondary application. Thereby, the recharging efficiency and task offloading computation are optimized and improved. Results indicate that the total resource consumption is favorably improved with this architecture and algorithm and the Nash equilibrium is also demonstrated.