Problem-solving Tasks Research Articles

Learning enhancement through deploying personal learning environment, network and problem-based learning assessment

Intended to foster students’ self-directed learning and research ability at the postgraduate level, this educational research evaluated the implications of personal learning environment & network (PLE&N) and problem-based learning (PBL) assessment as student-centric and student-driven pedagogic approaches. Compared to conventional instructional paradigms, the PLE&N platform presents an agile and interactive platform that empowers students to partake in collaborative discourse while orchestrating their learning journey through the judicious utilization of communal resources. The vitality of the PLE&N model is substantiated by the enduring engagement of peers, teachers, professionals, and external experts in the field, fostering a milieu of enriched knowledge exchange, synergistic collaboration, and personalized curation of learning approaches. In contrast, the PBL assessment was designed by incorporating intricate scenarios similar to the real world, thereby affording an avenue for cultivating students’ cogitation and adroit problem-solving skills within a guided learning environment. By integrating constructivist and connectivist pedagogies, the PBL assessment facilitated teamwork while engaging students in independent research and empowered their knowledge construction on selected topics. Students’ self-evaluation through the survey indicated their enhancement in self-directed learning behavior and competence building for complex problem-solving tasks. Moreover, peer feedback and suggestions received through both PLE&N and PBL were highly beneficial for students to analyze information and learning resources from diverse perspectives, which further helped to expand their learning horizon. This study underscored the significance and advantages of modern student-centric approaches such as PLE&N and PBL. These pedagogical paradigms stand as seminal strategies for nurturing nascent post-graduate students, thereby endowing them with indispensable proficiencies in self-directed learning and robust competencies in scholarly inquiry. Keywords: PLE&N, PBL, Constructivism, Connectivism, Pedagogy, Self-directed learning.

Uncovering the interplay between drawings, mental representations, and arithmetic problem-solving strategies in children and adults.

There is an ongoing debate in the scientific community regarding the nature and role of the mental representations involved in solving arithmetic word problems. In this study, we took a closer look at the interplay between mental representations, drawing production, and strategy choice. We used dual-strategy isomorphic word problems sharing the same mathematical structure, but differing in the entities they mentioned in their problem statement. Due to the non-mathematical knowledge attached to these entities, some problems were believed to lead to a specific (cardinal) encoding compatible with one solving strategy, whereas other problems were thought to foster a different (ordinal) encoding compatible with the other solving strategy. We asked 59 children and 52 adults to solve 12 of those arithmetic word problems and to make a diagram of each problem. We hypothesized that the diagrams of both groups would display prototypical features indicating either a cardinal representation or an ordinal representation, depending on the entities mentioned in the problem statement. Joint analysis of the drawing task and the problem-solving task showed that the cardinal and ordinal features of the diagrams are linked with the hypothesized semantic properties of the problems and, crucially, with the choice of one solving strategy over another. We showed that regardless of their experience, participants' strategy use depends on their problem representation, which is influenced by the non-mathematical information in the problem statement, as revealed in their diagrams. We discuss the relevance of drawing tasks for investigating mental representations and fostering mathematical development in school.

High levels of air pollution reduce team performance

Teams play a key role in tackling complex societal challenges, such as developing vaccines or novel clean energy technologies. Yet, the effect of air pollution on team performance in non-routine problem-solving tasks is not well explored. Here, we document a sizable adverse effect of air pollution on team performance using data from 15,000 live escape games in London, United Kingdom. On high-pollution days, teams take on average 5% more time to solve a sequence of non-routine analytical tasks, which require collaborative skills analogous to those needed in the modern workplace. Negative effects are non-linear and only occur at high levels of air pollution, which are however commonplace in many developing countries. As team efforts predominantly drive innovation, high levels of air pollution may significantly hamper economic development.

Parenting Influences on Frontal Lobe Gray Matter and Preterm Toddlers' Problem-Solving Skills.

Children born preterm often face challenges with self-regulation during toddlerhood. This study examined the relationship between prematurity, supportive parent behaviors, frontal lobe gray matter volume (GMV), and emotion regulation (ER) among toddlers during a parent-assisted, increasingly complex problem-solving task, validated for this age range. Data were collected from preterm toddlers (n = 57) ages 15-30 months corrected for prematurity and their primary caregivers. MRI data were collected during toddlers' natural sleep. The sample contained three gestational groups: 22-27 weeks (extremely preterm; EPT), 28-33 weeks (very preterm; VPT), and 34-36 weeks (late preterm; LPT). Older toddlers became more compliant as the Tool Task increased in difficulty, but this pattern varied by gestational group. Engagement was highest for LPT toddlers, for older toddlers, and for the easiest task condition. Parents did not differentiate their support depending on task difficulty or their child's age or gestational group. Older children had greater frontal lobe GMV, and for EPT toddlers only, more parent support was related to larger right frontal lobe GMV. We found that parent support had the greatest impact on high birth risk (≤27 gestational weeks) toddler brain development, thus early parent interventions may normalize preterm child neurodevelopment and have lasting impacts.

Can Generative AI and ChatGPT Outperform Humans on Cognitive-Demanding Problem-Solving Tasks in Science?

Can Generative AI and ChatGPT Outperform Humans on Cognitive-Demanding Problem-Solving Tasks in Science?

Exploring Interrelationships between Mental Health Symptoms and Cognitive Impairment in Aging People Living with HIV in China

Introduction: Mental health symptoms and cognitive impairment are highly prevalent and intertwined among aging people living with HIV (PLWH). This study aimed to assess the interrelationships and strength of connections between individual mental health symptoms and cognitive impairment. We sought to identify specific symptoms linking mental health and cognitive impairment in aging PLWH. Methods: Participants in the Sichuan Older People with HIV Infections Cohort Study (SOHICS) were recruited between November 2018 and April 2021 in China. Mental health symptoms, including depression and anxiety, were assessed by the Patient Health Questionnaire (PHQ-9) and General Anxiety Disorder-7 (GAD-7), respectively. Cognitive impairment was assessed by the Montreal Cognitive Assessment-Basic (MoCA-B). Partial correlation networks were used to depict the interrelationships between mental health symptoms and cognitive impairment, and bridge strength was used to identify specific symptoms linking mental health and cognitive impairment. Results: Of the 1,587 recruited participants with a mean age of 63.0 years old, 47.0% had mild or severe cognitive impairment. Network analysis revealed that cognitive function, visual perception, and problem-solving task of the MoCA-B were negatively correlated with appetite, energy, and motor of the PHQ-9, respectively. Based on their interrelationships, problem-solving task and motor acted as bridge symptoms. Conclusion: Problem-solving task and motor may be potential intervention targets to reduce the overall risk of mental health symptoms and cognitive impairment. Future research could assess the feasibility and effectiveness of specific interventions designed for the two symptoms of aging PLWH.

Validation of new tablet-based problem-solving tasks in primary school students.

Problem-solving is an important skill that is associated with reasoning abilities, action control and academic success. Nevertheless, empirical evidence on cognitive correlates of problem-solving performance in childhood is limited. Appropriate assessment tools are scarce and existing analog tasks require extensive coding. Thus, we developed and validated new tablet-based versions of existing analog tasks assessing technical problem-solving with gear construction tasks. To validate these tasks, 215 children (6-8 years) performed the problem-solving tasks in both modalities (analog, digital). To investigate whether performances in both modalities were correlated with other cognitive abilities, participants performed three additional tasks assessing language, reasoning and problem-solving. Structural equation modelling showed that performance was substantially correlated across modalities and also correlated with language, reasoning and another problem-solving task, showing the convergent validity of the digital tasks. We also found scalar measurement invariance across task modalities indicating that both task versions can be used interchangeably. We conclude that both versions (analog and digital) draw on similar cognitive resources and abilities. The analog tasks were thus successfully transferred to a digital platform. The new tasks offer the immense benefits of digital data collection, provide a valid measuring tool advancing problem-solving research in childhood and facilitate the application in the field, e.g., in the classroom.

Enhancing flexibility and safety: collaborative robotics for material handling in end-of-line industrial operations

The manufacturing landscape is rapidly evolving, being characterized by the emergence of Industry 5.0, leading to a growing recognition of the importance of a human-centered, sustainable and resilient approach. In response to these needs, collaborative robotics has emerged as a promising solution to enhance flexibility in industrial operations, while ensuring a higher level of safety. Cobots enable human-robot collaboration without the need for physical barriers, reduce the footprint of the robotic station, and foster a closer working relationship between humans and machines.This article focuses on an application of collaborative robotics in end-of-line industrial operations, specifically in the unloading of products from an automatic machine. By managing repetitive tasks, such as placing the trays near the end of the line of the automatic machine, waiting for them to be filled, and placing them on a trolley, which will then be moved by the human operator, the robot significantly improves efficiency. The proposed solution involves alternating the placement of trays in two rack trolleys, allowing the worker to replace the full trolleys with empty ones at his own timing. The paper examines the potential advantages of using a human-centric approach that emphasizes operator empowerment and human oversight: the collaborative robotic cell and the automation implemented free the workers from the low value-added operations and allow them to oversee and manage multiple machines simultaneously, thus enhancing productivity. The approach behind this proposal is to keep robotic tasks simple, easily reprogrammable and modular, while empowering the human operator to engage in supervision, decision-making, and problem-solving tasks. This collaboration allows the operator to assume a supervisory role, organizing production based on identified needs, and elevating their involvement to production line management tasks.

Ongoing Enhancement of ESL Education Through Reflective Surveys for University Students

Reflective surveys in global best practices are used to ensure that the course meets the varied linguistic needs of university students. Therefore, we aimed to analyze quantitative and qualitative data on university students’ feedback across various dimensions of ESL courses and provide recommendations aimed at improving and sustaining the quality of teaching English at the university level. To achieve this, we used a mixed-method research design to obtain quantitative and qualitative data which revealed that university students expressed their overall satisfaction with the ESL courses. They especially appreciated the possibility of completing assignments asynchronously, as well as relaxed deadlines and discussions. They suggested creating study groups according to language proficiency levels and making them smaller to ensure an individual approach. The results of the research showed that students were mostly positive regarding ESL course content, teaching methods, instructor approach, assignments and assessments, class participation, technology integration, group work, resources and materials, time management, English proficiency, collaborative learning, and relevance to future careers. We observed statistically that students were especially enthusiastic about their teachers’ support and guidance, as well as about relaxed deadlines. This study highlighted the importance for students to communicate with each other in groups, play games, have discussions, do problem-solving tasks, focus on their professional needs, establish rapport with their teachers, get regular feedback and error correction. In conclusion, the study contributes to the importance of reflective surveys in holistically evaluating and refining the ESL learning experience for university students. In future research, reflective surveys could be used for teachers and students from several higher education institutions to further enrich our knowledge in this area.

Multi-Level Linguistic Alignment in a Dynamic Collaborative Problem-Solving Task.

Cocreating meaning in collaboration is challenging. Success is often determined by people's abilities to coordinate their language to converge upon shared mental representations. Here we explore one set of low-level linguistic behaviors, linguistic alignment, that both emerges from, and facilitates, outcomes of high-level convergence. Linguistic alignment captures the ways people reuse, that is, "align to," the lexical, syntactic, and semantic forms of others' utterances. Our focus is on the temporal change of multi-level linguistic alignment, as well as how alignment is related to communicative outcomes within a unique collaborative problem-solving paradigm. The primary task, situated within a virtual educational video game, requires creative thinking between three people where the paths for possible solutions are highly variable. We find that over time interactions are marked by decreasing lexical and syntactic alignment, with a trade-off of increasing semantic alignment. However, greater semantic alignment does not translate into better team performance. Overall, these findings provide greater clarity on the role of linguistic coordination within complex and dynamic collaborative problem-solvingtasks.

Does Artificial Intelligence have the Possibility of Taking Over Designers' Jobs in the Future?

Technological advances in tools such as artificial intelligence have made it easier for architects, planners, interior designers and urban designers to visualize their designs easily and save time. Today, AI (Artificial Intelligence) is integrated into many different professions, and the design sector is no exception. Also, new design technologies, for example, OpenAI, ChatGPT, Dall-E 2, and Mid Journey, quickly dissolve the lines between what humans do at work and what machines and algorithms do. Despite the fact that design is a highly creative discipline that calls for a special blend of technical expertise, aesthetic judgment, and user empathy, AI systems are currently unable to fully recreate these skills. They only provide output in accordance with system input. They are unable to independently innovate new things. And indeed, we discovered that AI fundamentally alters the practice of design. Traditional problem-solving tasks performed by designers are increasingly computerized into unrestricted volume and speed learning loops. This situation raises the question of whether artificial intelligence will replace the job of designers in the future. This essay would attempt to give an explanation of two factors, first, how AI technology would mitigate designers’ duties, and second, the reasons why it would not be able to replace designers and architects in the near future. The article seeks to do a discourse, summarizing the studies on the effects of AI on the professional activity of designers and projecting where future designers will stand in their respective fields. Furthermore, this paper discusses the implications of artificial intelligence on the profession of designers, such as job displacement, job redefinition, and changes in the skill set required to be successful in the profession. Finally, the outcome of the essay is to understand the revolutionary impact of AI tools in design sectors in the future.

교육대학원 인공지능 융합교육 전공 캡스톤 디자인 교과목 개발 및 적용

Objectives This study aimed to develop a capstone design course in the artificial intelligence convergence education major at the graduate school of education and apply it to actual classes to assess its educational effectiveness.
 Methods To achieve this, the artificial intelligence convergence education capstone design course was developed and implemented following the ADDIE model. Additionally, a survey consisting of problem-solving efficacy, task value, task authenticity, and satisfaction was conducted before and after the class to examine educational effectiveness. The collected data was analyzed using descriptive statistics and t-tests.
 Results Artificial intelligence convergence education capstone design course and teaching materials were developed for a total of 45 sessions, consisting of the first half of tool learning and example project implementation and the second half of presenting and discussing the research plan. The results showed statistically significant improvements across all areas after applying the course.
 Conclusions While the operation of capstone design courses for field research in the graduate school's artificial intelligence convergence education major is recommended, the lack of operational cases and efficacy evaluations is evident. Further research building upon these findings is anticipated to contribute to substantial course operations in the future.

SKILLS TO SUPPORT SUSTAINABLE DEVELOPMENTAND ENTREPRENEURSHIP - SOME OF THE IMPORTANT COMPETENCIES IN THE EDUCATION OF PLANT PROTECTION

The process of formation of initiative and entrepreneurial attitudes, of the ability to support sustainable development in students from the professional field Plant protection is realized both in the course of teaching various academic disciplines from the curriculum, and through a variety of extracurricular activities. The technology and methodology of this process includes the application of a competence approach in combination with interactive methods, which allows and ensures the achievement of the competencies set in the curricula as expected learning outcomes. The article discusses problem-solving tasks which require the use of interactive methods (case study, incident, the method of associations by means of mind maps), as well as other activities aimed at forming a skill to support sustainable development and entrepreneurship. In the training university students we invite business experts and use modern innovative ways of teaching which are in line with the standards applied in European education. The main goal based on the multidisciplinary and interdisciplinary approach is the education of highly qualified future employees, possessing skills and competencies for finding modern technological solutions, for successful planning, organization, implementation and management in the field of plant protection.

A hybrid computational approach to anticipate individuals in sequential problem solving.

Human-awareness is an ever more important requirement for AI systems that are designed to assist humans with daily physical interactions and problem solving. This is especially true for patients that need support to stay as independent as possible. To be human-aware, an AI should be able to anticipate the intentions of the individual humans it interacts with, in order to understand the difficulties and limitations they are facing and to adapt accordingly. While data-driven AI approaches have recently gained a lot of attention, more research is needed on assistive AI systems that can develop models of their partners' goals to offer proactive support without needing a lot of training trials for new problems. We propose an integrated AI system that can anticipate actions of individual humans to contribute to the foundations of trustworthy human-robot interaction. We test this in Tangram, which is an exemplary sequential problem solving task that requires dynamic decision making. In this task the sequences of steps to the goal might be variable and not known by the system. These are aspects that are also recognized as real world challenges for robotic systems. A hybrid approach based on the cognitive architecture ACT-R is presented that is not purely data-driven but includes cognitive principles, meaning heuristics that guide human decisions. Core of this Cognitive Tangram Solver (CTS) framework is an ACT-R cognitive model that simulates human problem solving behavior in action, recognizes possible dead ends and identifies ways forward. Based on this model, the CTS anticipates and adapts its predictions about the next action to take in any given situation. We executed an empirical study and collected data from 40 participants. The predictions made by CTS were evaluated with the participants' behavior, including comparative statistics as well as prediction accuracy. The model's anticipations compared to the human test data provide support for justifying further steps built upon our conceptual approach.

Domain-specific knowledge and domain-general abilities in children's science problem-solving.

Problem-solving in early and middle childhood is of high relevance for cognitive developmental research and educational support. Previous research on science problem-solving has focussed on the process and strategies of children handling challenging tasks, but less on providing insights into the cognitive network that enables science problem-solving. In this study, we aimed to investigate whether performance in science problem-solving is mainly determined by domain-specific rule knowledge, by domain-general cognitive abilities or both. In our study, 215 6- to 8-year-old children completed a set of three domain-specific rule knowledge tasks and three corresponding problem-solving tasks that were content-coherent, as well as a vocabulary task, and a reasoning task. Correlational and regression analyses revealed a negligible impact of domain-specific rule knowledge on corresponding problem-solving tasks. In contrast, the associations between problem-solving performance in different domains and the associations between problem-solving performance and domain-general abilities (vocabulary and reasoning) were comparably strong. The findings suggest that science problem-solving in primary school children primarily relies on domain-general cognitive abilities. Implications of these findings are discussed with regard to cognitive theories and early science education.

Guidance and Counseling Program for Problem Solving Skills Development: A Literature Review

The aim of learning to solve problems is to cultivate students' cognitive processes by presenting them with problem-solving tasks. Depending on the nature of the problem introduced in the classroom, problem-solving techniques may be applied either collaboratively in groups or individually. The guidance and counseling program contributes to the development of problem-solving skills by incorporating instructional methods that train students to address a range of challenges—both personal and group-related—whether tackling them independently or in collaboration. This research adopts a library research method, following the steps outlined by Zed (2014): a) formulating a general understanding of the research topic, b) seeking supporting information, c) refining the research focus, d) locating and organizing relevant reading materials, e) reading and annotating research notes, f) revisiting and augmenting reading materials, and g) reorganizing materials for the writing phase. Data sources, obtained through offline and online means such as books, journals, and articles, are systematically collected to establish a comprehensive reference database focused on studies investigating the application of guidance and counseling in the digital era. The selected timeframe for inclusion spans the last 5 years, encompassing studies in both Indonesian and English. The chosen data analysis technique for this study is content analysis, providing a methodological approach to systematically examine and interpret the content of the gathered materials.

IMPLEMENTING COMPUTATIONAL THINKING IN ELEMENTARY SCHOOL ICT EDUCATION AND EVALUATION USING THE KIRKPATRICK METHOD

The lack of understanding of computational concepts and problem-solving among students who still consider computers merely as tools for gaming and entertainment, along with their limited exposure to computational Thinking, constrains their ability to address problems systematically. Although the 2013 Curriculum emphasizes critical Thinking, basic data analysis, problem-solving, creativity, and innovation, aligned with the computational thinking method, it remains a distinct challenge for students and teachers to integrate it into their teaching. The objective of computational thinking education is to shift the paradigm and introduce students to computational thinking skills, particularly in ICT Education. Testing this method employs the Kirkpatrick method, which demonstrates a significant improvement in students' ability to solve problems with a more systematic and computational approach. Students can comprehend the concepts of decomposition, pattern recognition, abstraction, and algorithm development. This improvement is reflected in their performance on various computer-based problem-solving tasks. The benefits of implementing computational Thinking include students applying computational thinking principles not only in computer-based learning but also in their daily lives, gaining more confidence in facing technology-based challenges.

Functional graph contrastive learning of hyperscanning EEG reveals emotional contagion evoked by stereotype-based stressors

IntroductionThis study delves into the intricacies of emotional contagion and its impact on performance within dyadic interactions. Specifically, it focuses on the context of stereotype-based stress (SBS) during collaborative problem-solving tasks among female pairs. Through an exploration of emotional contagion, this study seeks to unveil its underlying mechanisms and effects.MethodsLeveraging EEG-based hyperscanning technology, we introduced an innovative approach known as the functional graph contrastive learning (fGCL), which extracts subject-invariant representations of neural activity patterns from feedback trials. These representations are further subjected to analysis using the dynamic graph classification (DGC) model, aimed at dissecting the process of emotional contagion along three independent temporal stages.ResultsThe results underscore the substantial role of emotional contagion in shaping the trajectories of participants' performance during collaborative tasks in the presence of SBS conditions.DiscussionOverall, our research contributes invaluable insights into the neural underpinnings of emotional contagion, thereby enriching our comprehension of the complexities underlying social interactions and emotional dynamics.

Förskoleklasselever utforskar kombinatorik genom digitala animeringar

The focus of this article is on digital tools as part of mathematics education with six-year-olds. More explicitly, we study how the creation of digital animations, as a part of working on a problem-solving task, enables young students’ learning of combinatorics. In the article, the creation of digital animations implies that the students re-design, that is, recreate their solution procedure with a digital application. The aim with letting the young students create digital animations is diverse. We presume that such work enables problem solving as well as the learning of combinatorics, but also that it has potential to enable creativity and agency in learning. Video-documentations from three classrooms where students work on the problem-solving task were analysed from a multimodal perspective where teaching is seen as a design process. In that process, the teacher designs learning activities that give students access to different resources for their meaning making process. The results show that working with digital animations, when integrated in a learning design sequence, amplify students’ learning of combinatorics.

Interpretable self-organizing map assisted interactive multi-criteria decision-making following Pareto-Race

The problem-solving task of the multi-criteria decision-making (MCDM) approach involves decision makers’ (DMs’) interaction by incorporating their preferences to arrive at one or more preferred near Pareto-optimal solution(s). The Pareto-Race is an interactive MCDM approach that relies on finding preferred solutions as the DM navigates on the Pareto surface by selecting the preferred reference direction and uniform/varying speed using the concept of Achievement Scalarization Function (ASF) or its augmented version (AASF). Selecting a reference direction or speed is possible, algorithmically, but the effectiveness of the Pareto-Race concept relies on suitable visualization methods that present the series of past solutions and convey the current objective trade-off information to assist the DM’s in decision-making task. Traditional multi-dimensional Pareto-optimal front visualization methods like parallel coordinate plots, radial visualization, and heat maps are deemed insufficient for this purpose. Therefore, the paper proposes integrating the concept of the Pareto-Race MCDM approach with a recently developed interpretable self-organizing map (iSOM) based visualization method. The iSOM maps high-dimensional data into lower-dimensional space, facilitating visual convenience for the DM. The proposed method requires a finite-size representation of non-dominated solutions near the complete Pareto Front to generate iSOM plots of objectives. We also propose visualizing the metrics such as closeness to constraint boundaries, trade-off value, and robustness using iSOM to check the quality of the preferred solutions, which is rarely considered in existing MCDM approaches. iSOM plots of objective functions, closeness to constraint, trade-off, and robustness metrics assist the DM in effectively choosing new reference direction and step size in each iteration to arrive at the most preferred solution. The proposed iSOM-enabled Pareto-Race approach is demonstrated on benchmark analytical and real-world engineering examples with 3 to 10 objectives.