Problem-solving Environment Research Articles

Using Digital Game-Based Learning in Mathematics Education: A Case Study with Teacher Training Students

Using game-based learning (GBL) and digital game-based learning (DGBL) as a teaching and learning environment can be a pedagogical resource and a good strategy in the classroom to support mathematical learning (Santos et al.,2023). Even though primary school teachers are demonstrating strong preferences for using non-digital games instead of digital games to support mathematics instruction, much of the research review literature has focused on learning outcomes associated with digital mathematical games (Russo et al., 2024). In this paper, I conduct a quasi-experiment using an empirical action research method and the same logical problem-solving environment–game-based or digital game-based learning–that I designed and used with two groups of pre-service teacher university students. I tested three digital games with their non-digital equivalent and observed the problem solvers primarily from a qualitative point of view. Most of the participants enjoyed working with both physical cards and digital games, which effectively engaged students in solving the tasks. My findings suggest that students found the task-solving enjoyable in both types of game-based learning and that solving tasks non-digitally with cards could be more flexible for the problem solvers.

Problem solving in mathematics education: tracing its foundations and current research-practice trends

In tracing recent research trends and directions in mathematical problem-solving, it is argued that advances in mathematics practices occur and take place around two intertwined activities, mathematics problem formulation and ways to approach and solve those problems. In this context, a problematizing principle emerges as central activity to organize mathematics curriculum proposals and ways to structure problem-solving learning environments. Subjects’ use of concrete, abstract, symbolic, or digital tools not only influences the ways to pose and pursue mathematical problems; but also shapes the type of representation, exploration, and reasoning they engage to work and solve problems. Problem-solving foundations that privilege learners’ development of habits of mathematical practices that involve an inquiry method to formulate conjectures, to look for different ways to represent and approach problems, and to support and communicate results shed light on directions of current research trends and the relevance of rethinking curriculum proposals and extending problem-solving environments in terms of teachers/students’ consistent use of digital tools and online developments.

AI-assisted evaluation of problem-solving performance using eye movement and handwriting

With the ability to predict learning behaviors, artificial intelligence (AI) is increasingly involved in assessing the performance of problem solving. This study explored the potential of AI to predict whether mathematics problems could be solved based on eye movements and handwriting in a digital problem-solving environment. Sixty-one students participated in the experiment. The goal is to examine whether types of eye movement features (AOI-based and fixation-based features), levels of information while solving problems (separated and integrated steps), and handwriting could impact the performance of AI. The results indicated that fixation-based features outperform AOI-based features. Furthermore, information in separated steps could provide higher accuracy than that in integrated steps. Inconsistent patterns between human and AI-based assessment of solvers’ answers are discussed.

Special Functions in Problem Solving Environments: A personal view

This paper discusses some of the philosophical and historical underpinnings of the talk “The Mathieu Functions: Computational and Historical Perspectives” given at the Maple Conference 2022. I also comment on the role Problem Solving Environments (PSEs) play in curating the computational knowledge of the 19th century, which is so necessary for us to think of special functions as answers rather than questions.

BoxStacker: Deep Reinforcement Learning for 3D Bin Packing Problem in Virtual Environment of Logistics Systems.

Manufacturing systems need to be resilient and self-organizing to adapt to unexpected disruptions, such as product changes or rapid order, in supply chain changes while increasing the automation level of robotized logistics processes to cope with the lack of human experts. Deep Reinforcement Learning is a potential solution to solve more complex problems by introducing artificial neural networks in Reinforcement Learning. In this paper, a game engine was used for Deep Reinforcement Learning training, which allows visualization of view learning and result processes more intuitively than other tools, as well as a physical engine for a more realistic problem-solving environment. The present research demonstrates that a Deep Reinforcement Learning model can effectively address the real-time sequential 3D bin packing problem by utilizing a game engine to visualize the environment. The results indicate that this approach holds promise for tackling complex logistical challenges in dynamic settings.

Common Interests Without Common Expertise: Reflections on Early-career Experiences in Cross-Disciplinary Research.

Cross-disciplinary research enables us to tackle complex problems that require expertise from different fields. Such collaborations involve researchers who have different perspectives, communication styles, and knowledge bases, and can produce results far greater than the sum of their parts. However, in an era of increasing scientific specialization, there exist many barriers for students and early-career researchers (ECRs) interested in training and undertaking interdisciplinary research endeavors. This perspective examines the challenges that students and ECRs perceive and experience in cross-disciplinary work and proposes pathways to create more inclusive and welcoming research environments. This work emerges from a National Science Foundation (NSF)-funded workshop held during the Society for Integrative and Comparative Biology (SICB) Annual Meeting in January 2023 in Austin, TX. The workshop brought together seasoned interdisciplinary scientists with undergraduate and graduate students to identify and discuss perceived challenges through small group discussions and experience sharing. Through summarizing a range of student concerns about embarking on careers as interdisciplinary scientists and identifying ways to dismantle institutional and lab management-level barriers, we aim to promote an inclusive and collaborative problem-solving environment for scientists of all experience levels.

Assessing Saiph, a task-based DSL for high-performance computational fluid dynamics

Scientific applications face the challenge of efficiently exploiting increasingly complex parallel and distributed systems. Developing hand-tuned codes is a time-consuming, tedious and hardly reusable task. Reaching high performance appears detrimental to productivity and portability and unreasonable to expect from scientists. Domain-Specific Languages (DSLs) are collaborative environments aiming to overcome such difficulties by decoupling the problem description from the algorithmic implementation. However, developing a competitive tool in High-Performance Computing (HPC) is challenging: DSLs for HPC environments have two additional critical requirements, performance and scalability. Moreover, documented and successful cases are few, making it difficult to popularise DSLs as problem-solving environments for scientific HPC code development. In this context, Saiph is a task-based DSL easing the simulation of physical phenomena from Computational Fluid Dynamics (CFD), developed to meet HPC productivity and performance requirements. This work reports the tuning and evaluation of Saiph using the Taylor–Green Vortex (TGV) problem as a case study. We assess Saiph’s productivity, numerical methods, and high-performance strategies to illustrate its use and demonstrate its competitiveness, viability and benefits for CFD software developments in HPC environments. Hence, we contribute to the popularisation of HPC DSLs as suitable problem-solving environments able to unify modern computational and scientific knowledge.

Integrating Two-Level Reinforcement Learning Process for Enhancing Task Scheduling Efficiency in a Complex Problem-Solving Environment

Task scheduling scenarios require the system designers to have complete information about the resources and their capabilities, along with the tasks and their application-specific requirements. An effective task-to-resource mapping strategy will maximize resource utilization under constraints, while minimizing the task waiting time, which will in-turn maximize the task execution efficiency. In this work, a two-level reinforcement learning algorithm for task scheduling is proposed. The algorithm utilizes a deep-intensive learning stage to generate a deployable strategy for task-to-resource mapping. This mapping is re-evaluated at specific execution breakpoints, and the strategy is re-evaluated based on the incremental learning from these breakpoints. In order to perform incremental learning, real-time parametric checking is done on the resources and the tasks; and a new strategy is devised during execution. The mean task waiting time is reduced by 20% when compared with standard algorithms like Dynamic and Integrated Resource Scheduling, Improved Differential Evolution, and Q-learning-based Improved Differential Evolution; while the resource utilization is improved by more than 15%. The algorithm is evaluated on datasets from different domains like Coronavirus disease (COVID-19) datasets of public domain, National Aeronautics and Space Administration (NASA) datasets and others. The proposed method performs consistently on all the datasets.

THE ACCEPTANCE OF AN EDUCATIONAL INTEGRATED DEVELOPMENT ENVIRONMENT TO LEARN PROGRAMMING FUNDAMENTALS

Programming is an important course for any IT or engineering-related course. However, previous research shows that students face difficulties in learning programming due to its abstract concepts. This study aims to evaluate the acceptance of a developed Integrated Development Environment (IDE), namely C-SOLVIS which is a web-based application that specifically intends to facilitate the teaching and learning of the C programming fundamentals in Malaysian tertiary education. The C-SOLVIS integrates problem-solving into a program development environment for the C language. The goal is to guide the users in problem-solving and help them write C programs based on problem-solving algorithms. The Rapid Application Development (RAD) Model was employed in the C-SOLVIS development process. Based on this model, the requirement planning phase was carried out through the triangulation technique by applying qualitative approaches comprising a literature review supported by semi-structured interviews, document reviews, and content validation by expert programming lecturers. Subsequently, the design of the application was accomplished through the iterative prototyping process which was then followed by the application construction. Then, the C-SOLVIS is deployed to be used by several programming lecturers to evaluate its usability by adopting a quantitative method using the System Usability Scale (SUS) questionnaire. The study has discovered several suitable techniques and designs for the problem-solving and program development environment. For the problem-solving environment, the Computational Thinking (CT) concepts were applied which were supported by the Input-Process-Output (IPO) Model through Scientific Instructions and Inquiries. Meanwhile, the program development environment was designed and developed based on frame-based programming using a set of developed Code Patterns. The C-SOLVIS evaluation using the SUS instrument has yielded a SUS mean score of 86.07. This score is interpreted by SUS as an A grade that indicates C-SOLVIS as a highly usable application and thus is accepted for C programming learning. Hence, the development process of the C-SOLVIS can be used as a guideline for educational software development, especially in the field of programming education.

Failure Prediction for Scientific Workflows Using Nature-Inspired Machine Learning Approach

Scientific workflows have gained the emerging attention in sophisticated large-scale scientific problem-solving environments. The pay-per-use model of cloud, its scalability and dynamic deployment enables it suited for executing scientific workflow applications. Since the cloud is not a utopian environment, failures are inevitable that may result in experiencing fluctuations in the delivered performance. Though a single task failure occurs in workflow based applications, due to its task dependency nature, the reliability of the overall system will be affected drastically. Hence rather than reactive fault-tolerant approaches, proactive measures are vital in scientific workflows. This work puts forth an attempt to concentrate on the exploration issue of structuring a nature inspired metaheuristics-Intelligent Water Drops Algorithm (IWDA) combined with an efficient machine learning approach-Support Vector Regression (SVR) for task failure prognostication which facilitates proactive fault-tolerance in the scheduling of scientific workflow applications. The failure prediction models in this study have been implemented through SVR-based machine learning approaches and the precision accuracy of prediction is optimized by IWDA and several performance metrics were evaluated on various benchmark workflows. The experimental results prove that the proposed proactive fault-tolerant approach performs better compared with the other existing techniques.

Dynamic Reduction-Based Virtual Models for Digital Twins—A Comparative Study

Digital models are the foundation of digital twins, which form the basis of autonomous off-road vehicles. Developing virtual models of off-road vehicles using dynamic reduction techniques is one of several approaches. The article commences with a comprehensive overview of the most widely used dynamic reduction methods and then introduces performance metrics for assessing their efficacies in the context of digital twins. The paper additionally includes a detailed mathematical derivation of the state-space representation for reduced-order finite element models. The state-space representation of the reduced finite element models facilitates their export to problem-solving environments for dynamic analysis. The state-space models are eventually solved utilizing the built-in libraries of numerical solvers in textual and graphical programming platforms. In addition, the article identifies the set of solvers that best suit the simulation of virtual models for off-road vehicles. This article also includes an evaluation of the simulation results for digital models with modes ranging from 0 to 30 Hz. In addition, the article demonstrates the lower bound of the frequency range necessary and sufficient to be retained in off-road vehicle virtual models. Finally, the paper presents the simulation outcomes for digital models of commercial off-road vehicles with custom-built virtual modules of powertrain, electrical, and control systems in a problem-solving environment.

Contributions of Design Thinking in Inter and Transdisciplinary Communication, Research & Co-work Environment for Complex Problem Solving

Design, as research process, management strategy or problem-solving methodology, is nowadays embedded on organizational structures, social demands and in human interaction and experiences. Involves digital & analogue technologies, requires immersion in complex problems and systems, since the stage of identifying problems, constraints and consequently needs and opportunities, with constant feedback that prompts multidisciplinary team's cooperation looping, from gathering data, to ideation, model and prototype, analysis, and synthesis, evaluate and test, experiment, thus presenting solutions. This cybernetic loop research process stimulates and feeds itself of feedbacks gained also from Inter and Transdisciplinary Communication, that stimulates new knowledge [7], in Research & Development + Innovation (R&D+I). This exploratory research approaches a Project-Based Methodology known as Design Thinking (DT), considering four models: Stanford d. School - 5 Step Design Thinking; Design Council - Double Diamond; IDEO Human-Centered Design, 3I's model (Inspiration, Ideation, Implementation); and Katja Tschimmel - Evolution 6*, and the Creative Techniques used in these methodologies. The present deductive research tries to explain correlations between its use in academia/business/social environments, as a transdisciplinary communication approach, to complex problem resolution, embedding Design Thinking methodologies in applied research. * "R&D is increasingly viewed as an input to innovation in the context of the overall efforts made in a knowledge-based global economy." pp.3 [9] "2.9. The term R&D covers three types of activity: basic research, applied research and experimental development. … Applied research is original investigation undertaken to acquire new knowledge. It is, however, directed primarily towards a specific, practical aim or objective." pp.45 [9]

Anchored Instruction in Teaching Business Mathematics

This study aims to determine the effectiveness of Anchored Instruction in teaching Business Mathematics to the senior high school students of Simala National High School in Cebu, Philippines. Anchored Instruction is a teaching method in which the learners can generate problems, look for the relevant data, and find solutions from the situated problem-solving environment shown through videos. Its instructional design highlights three main features: generative stories, embedded data, and problem complexity. A quasi-experimental research method was utilized using pretest-posttest control and experimental group design. Both the control and experimental groups with 40 subjects each were not significantly different from each other as established in the match pairing of their profile. They were given a pretest, exposed to their respective interventions, and then were given a posttest. The control group was exposed to Conventional Instruction, while the experimental group was exposed to Anchored Instruction as the method in teaching Business Mathematics. Findings revealed that Anchored Instruction is a more effective method in teaching Business Mathematics than conventional instruction. The learner-centeredness of Anchored Instruction provided opportunities for the learners to be highly engaged in the learning process, as noted in the researcher’s observation and interview with selected subjects. As a result, they could identify the useful knowledge and eventually overcome the inert knowledge problem. Hence, it is recommended to utilize this method in teaching Business Mathematics.

Escape Rooms as a Collaborative Problem-Solving Environment

Escape room games are a popular recreative activity that recently started gaining popularity as both a means for conducting research studies and a teaching and learning environment. This paper follows a group of 16- to 17-year-old students in an escape room while they encounter logical challenges in order to escape from the room in a limited amount of time. Their ability to work effectively as a team and complete activities quickly is a key element to success. In this setting, the aim was to examine in what ways this innovative environment might help students benefit in terms of social metacognition. The findings gave evidence that during collaborative problem-solving in an escape room, cognitive demands are distributed among the players. This makes metacognition visible, facilitates shared management thus improving individual cognition, facilitates reciprocal scaffolding, and enhances motivation.

The Python/Jupyter Ecosystem: Today’s Problem-Solving Environment for Computational Science

The EiC looks back on a concept presented in CiSE’s inaugural issue: that of problem-solving environments, with a promise to be ‘all things to all people’ in computational science. Reviewing the progress over the past decades of the Python ecosystem for scientific computing, and the coverage in CiSE, she makes the case that Python and Jupyter today provide that rich environment that can make computational scientists most productive. This editorial anticipates the next issue, when CiSE will feature scientific applications that embody the powerful idea of combining high researcher productivity via Python and high performance through code generation, just-in-time compilation, or exploiting advanced Python libraries.

Neo-Hippocratic healthcare policies: professional or industrial healthcare delivery? A choice for doctors, patients, and their organisations

BackgroundEthical medical practice requires managing health services to promote professionalism and secure accessibility to care. Commercially financed and industrially managed services strain the physicians’ clinical autonomy and ethics because the industry’s profitability depends on commercial, clinical standardisation. Private insurance companies also reduce access to care whilst fragmenting and segmenting health systems. Against this background, given the powerful, symbolic significance of their common voice, physicians’ and patients’ organisations could effectively leverage together political parties and employers’ organisations to promote policies favouring access to professional care.Main textTo provide a foundation for negotiations between physicians’ and patients’ organisations, we propose policy principles derived from an analysis of rights-holders and duty-bearers’ stakes, i.e., patients, physicians and health professionals, and taxpayers. Their concerns are scrutinised from the standpoints of public health and right to health. Illustrated with post-WWII European policies, these principles are formulated as inputs for tentative action-research. The paper also identifies potential stumbling blocks for collective doctor/patient negotiations based on the authors’ personal experience.The patients’ concerns are care accessibility, quality, and price. Those of physicians and other professionals are problem-solving capacity, autonomy, intellectual progress, ethics, work environment, and revenue. The majority of taxpayers have an interest in taxes being progressive and public spending on health regressive. Mutual aid associations tend to under-estimate the physician’s role in delivering care. Physicians’ organisations often disregard the mission of financing care and its impact on healthcare quality.ConclusionThe proposed physicians-patients’ alliance could promote policies in tune with professional ethics, prevent European policies’ putting industrial concerns above suffering and death, bar care financing from the ambit of international trade treaties, and foster international cooperation policies consistent with the principles that inspire the design of healthcare policies at home and so reduce international migration. To be credible partners in this alliance, physicians’ associations should promote a public health culture amongst their members and a team culture in healthcare services. To promote a universal health system, patients’ organisations should strive to represent universal health interests rather than those of patients with specific diseases, ethnic groups, or social classes.

Collaboration between supply chain partners: when does it matter? Evidence from collaborative profiles

PurposeConsidering the limitations of supply chain collaboration (SCC) literature, the purpose of this paper is to develop SCC framework and analyze the SCC practice based on the framework. The framework incorporating internal and external knowledge sharing under a collaborative culture (CC) and problem-solving environment preceded by preparedness is required.Design/methodology/approachTo achieve successful collaboration, this paper encompasses problem-solving and performance measurement, preparedness, culture, relationship strength (RS) and gives resource sharing and planning as internal and external focused perspectives. Exploratory methods are used to analyze the data from industries.FindingsCC, RS and preparedness are crucial that reflect the health of collaboration. Three levels of collaboration practice – good, moderate and weak – are prominent where the extent of SCC practice seems weak. Collaborative activities that matter more are identified. Market-based knowledge sharing is important for good and moderate SCC, while weaker collaboration does not focus on it. Interestingly, this paper finds an increasing level of collaboration may not always result in increased performance. Firms from the pharmaceutical, automobile and electrical/electronic industry tended to practice higher collaboration as compared to other industries.Originality/valueBy integrating market-driven management, resource-based view, and contingency theory, the SCC framework gives a new perspective. Joint planning and resource sharing are categorized as internal and external focused functional areas. The paper identifies three profiles of collaboration and discusses the importance of SCC activities in achieving performance.

Describing collective creative acts in a mathematical problem-solving environment

In this article, I report on a study of the nature of collective creativity in mathematics learning settings. Based on an extensive review of the literature on creativity, the study was initially framed by a number of themes to encompass a variety of visions of creativity. These themes were used in the analysis of data collected in a Grade 6 mathematics learning environment. The themes were then refined and (re)developed to distill four creative acts to describe the experience of creativity with(in) the collective. These collective creative acts are: summing forces, expanding possibilities, divergent thinking, and assembling things in new ways. Here, I present the findings of the study, then, I speculate on some of the logical implications of the four collective creative acts for teaching and learning.

The Analysis of Dielectric Constant, Loss Factor and Q-Factor of Selected Fruits at Microwave Frequency Range

The dielectric constant, dielectric loss factor and Q-Factor of orange, red and green apples were studied at Microwave frequency range. An algorithm was written using the Debye equations and the interactive problem-solving environment of Maple-18 was used to generate results for the dielectric constant, loss factor and quality factor. The variation in the dielectric constant, loss factor and Q-factor as both frequency and temperature changes respectively within and range were shown graphically. The dielectric constant of all the fruits were higher at lower frequencies, it then decreases continuously as frequency increases. On the other hand, the loss factor of this fruits were small at lower frequencies but increased to its peak before decreasing continuously for all temperatures. Interestingly, the fruits’ Q-Factor were higher at lower frequencies and temperatures but decreases rapidly as the frequency increases. The contribution of the space charge, orientation, ionic and electronic polarizations to the dielectric constant, dielectric loss factor and Q-Factor of these fruits at lower frequency and higher frequency were discussed. The effect of the excess sugar and water content on the dielectric constant, loss factor and Q-Factor attributed to the appearance of vibrational peaks was also discussed. This work hereby provides a guidance in developing new microwave processes.

Translational computer science at the scientific computing and imaging institute

The Scientific Computing and Imaging (SCI) Institute at the University of Utah evolved from the SCI research group, started in 1994 by Professors Chris Johnson and RobMacLeod. Over time, research centers funded by the National Institutes of Health, Department of Energy, and State of Utah significantly spurred growth, and SCI became a permanent interdisciplinary research institute in 2000. The SCI Institute is now home to more than 150 faculty, students, and staff.The history of the SCI Institute is underpinned by a culture of multidisciplinary, collaborative research, which led to its emergence as an internationally recognized leader in the development and use of visualization, scientific computing, and image analysis research to solve important problems in a broad range of domains in biomedicine, science, and engineering. A particular hallmark of SCI Institute research is the creation of open source software systems, including the SCIRun scientific problem-solving environment, Seg3D, ImageVis3D, Uintah, ViSUS, Nektar++, VisTrails, FluoRender, and FEBio. At this point, the SCI Institute has made more than 50 software packages broadly available to the scientific community under open-source licensing and supports them through web pages, documentation, and user groups.While the vast majority of academic research software is written and maintained by graduate students, the SCI Institute employs several professional software developers to help create, maintain, and document robust, tested, well-engineered open source software. The story of how and why we worked, and often struggled, to make professional software engineers an integral part of an academic research institute is crucial to the larger story of the SCI Institute’s success in translational computer science (TCS).