Using Graphical Perception Principles to Improve the ST Tools’ Data Visualization: Revisiting the Systems Dynamics Model

In low-income and middle-income countries, such as those in sub-Saharan Africa and Latin America, the COVID-19 pandemic has had substantial implications for women's wellbeing. Policy responses to the COVID-19 pandemic have highlighted the gendered aspect of pandemics; however, addressing the gendered implications of the COVID-19 pandemic comprehensively and effectively requires a planetary health perspective that embraces systems thinking to inequalities. This Viewpoint is based on collective reflections from research done by the authors on COVID-19 responses by international and regional organisations, and national governments, in Latin America and sub-Saharan Africa between June, 2020, and June, 2021. A range of international and regional actors have made important policy recommendations to address the gendered implications of the COVID-19 pandemic on women's health and wellbeing since the start of the pandemic. However, national-level policy responses to the COVID-19 pandemic have been partial and inconsistent with regards to gender in both sub-Saharan Africa and Latin America, largely failing to recognise the multiple drivers of gendered health inequalities. This Viewpoint proposes that addressing the effects of the COVID-19 pandemic on women in low-income and middle-income countries should adopt a systems thinking approach and be informed by the question of who is affected as opposed to who is infected. In adopting the systems thinking approach, responses will be more able to recognise and address the direct gendered effects of the pandemic and those that emerge indirectly through a combination of long-standing structural inequalities and gendered responses to the pandemic.

This study explores the effect of project-based learning (PBL) integrated with STEM on improving students' systemic thinking abilities. The study employed a quasi-experimental pretest-posttest control group design as its research method. The population consisted of 244 students enrolled in the tenth grade during the odd semester of the 2023-2024 academic year, and participants involved in the study were 68 tenth-grade students from one of the Islamic Senior High Schools of the State in Jakarta. The experimental group used PjBL STEM, while the control class used 5E learning (engagement, exploration, explanation, elaboration, and evaluation). The researcher developed a system thinking instrument adapted from Melinda based on pre-requirement, basic, intermediate, and coherent expert levels. In this study, data analysis used the non-parametric Mann-Whitney U test and Wilcoxon test. The research findings indicate a significant improvement in the system thinking ability of the experimental group students, while the control group's system thinking ability did not improve significantly. The results showed that the effect of PjBL STEM improvement in the experimental class was 0.54 (effect size) on students' systems thinking ability but was still in the low category and did not improve either in the control class. In addition, the level of systems thinking increased in the expert category, while the intermediate level did not increase. This study can be a reference for further research investigating the effect of PjBL STEM on students' systems thinking in different contexts.

In a variety of disciplines, from biology and physics to computer science and the social sciences, computational topology has become a potent tool for data analysis and visualisation. This study explores computational topology's uses in data analysis and visualisation, showing its potential for revealing hidden structures and patterns in large, complicated datasets.The paper starts out by giving a general introduction of computational topology and outlining its core ideas and methods. The use of it for extracting important information from big, high-dimensional datasets is then explored in relation to data analysis. Computational topology allows for the discovery of topological properties such as holes, voids, and connection patterns by modelling data as topological structures, such as simplicial complexes or persistent homology diagrams.The study also explores the function of computational topology in data visualisation, highlighting its capacity to offer clear and insightful visual representations of challenging datasets. Computational topology enables the construction of simpler and aesthetically pleasing representations while preserving the fundamental topological properties of the data through methods like topological simplification and dimensionality reduction.The usefulness of computational topology in several application domains, such as genomics, image analysis, and network analysis, is demonstrated through a number of case studies. These instances show how computational topology can improve activities like data exploration, clustering, classification, and anomaly detection, resulting in fresh perceptions and learnings.This study emphasises the enormous potential of computational topology for applications in data processing and visualisation. Computational topology provides a new perspective that complements current approaches by utilising the inherent geometric and topological properties of data. This allows researchers and practitioners to better understand complex datasets and make decisions based on the knowledge gleaned from them.

Data clustering is an important technique for data visualization and statistical data analysis. Using data clustering technique for geo-located data analysis and visualization has been shown to be useful in many practical domains. Although there are numerous studies for data clustering algorithms, the implementations and applications based on these algorithms are rare and inadequate, especially for web-based data visualization services. In this research, a new web-based interactive data clustering prototype is proposed for geo-located data visualization. The necessary web-based infrastructure for geo-located data analysis and visualization is designed and implemented at first. And then the new interactive data clustering prototype using multi-dimensional K-Means algorithm is implemented on the existing infrastructure. In order to assess whether the new prototype can contribute meaningfully to architectural design and urban studies, qualified users with urban study or architectural backgrounds are invited to use the prototype and then make their comments and evaluations. To make it clear how this new prototype might apply in real world settings, an application scenario in which the user might interact with the prototype is provided and analyzed.

Complex systems affect every part of our lives from the ecosystems that we inhabit and share with other living organisms to the systems that supply our water (i.e., water cycle). Evaluating events, entities, problems, and systems from multiple perspectives is known as a systems thinking approach. New curriculum standards have made explicit the call for teaching with a systems thinking approach in our science classrooms. However, little is known about how elementary in-service or pre-service teachers understand complex systems especially in terms of systems thinking. This mixed methods study investigated 67 elementary in-service teachers’ and 69 pre-service teachers’ knowledge of a complex system (e.g., water cycle) and their knowledge of systems thinking. Semi-structured interviews were conducted with a sub-sample of participants. Quantitative and qualitative analyses of content assessment data and questionnaires were conducted. Results from this study showed elementary in-service and pre-service teachers applied different levels of systems thinking from novice to intermediate. Common barriers to complete systems thinking were identified with both in-service and pre-service teachers and included identifying components and processes, recognizing multiple interactions and relationships between subsystems and hidden dimensions, and difficulty understanding the human impact on the water cycle system.

This article addresses the urgency of improving hydrological monitoring and effective water resource management, which is particularly crucial for flood prevention and rapid emergency response.The problem lies in the need to develop a tool for efficient collection, processing, and visualization of water level data in the rivers of the Lviv region. The research objective is to develop and implement an interactive web application based on ArcGIS for visualizing and analyzing water level dynamics. This tool is designed to display the location of hydrological posts, provide access to their current status information, and analyze time series of hydrological data. The key feature of this approach is the integration and visualization of data from both automated and manual (gauge) posts, significantly expanding the cover-age of hydrological information.Based on the stated objectives, the geoinformation approach has been implemented for collecting and visualizing hydro-logical post data in the Lviv region using ArcGIS tools. The data source for this system comprises observations provided by the Basin Water Resources Management of the Western Bug and Sian Rivers. As a result, the interactive ArcGIS monitoring dash-board has been developed. It visualizes spatial and temporal water level data, integrating a web map with tabular time series data. This ensures automatic synchronization between selecting a post on the map and displaying dynamic charts and analytical tables. The web application facilitates comprehensive analysis of hydrological data.The proposed solution significantly enhances hydrological monitoring and supports managerial decision-making regarding flood prevention and emergency response. Its practical value lies in its application for water resource management and flood risk assessment, including the visualization of indicators and access to operational information for specialists and au-thorities. Future research will focus on real-time data updates, integration with meteorological indicators, and development of models for forecasting water levels and potential flood zones.

In parallel with technological developments, learning-teaching environments have changed and students are active and responsible for their own learning throughout the process. It is believed that the design of enriched teaching materials based on web-assisted and inquiry-based learning approaches will be important in terms of learning-teaching environments. The aim of this study is to investigate the effect of web-assisted guided inquiry approach in science education on students’ systems thinking skills. An embedded mixed methods design was used in this study. First, materials were developed for the systems in our body unit and three teachers used these materials in their classrooms. A total of 155 sixth grade students participated in the study. In the experimental group the lessons were taught according to the web-assisted guided inquiry approach, while in the control group the lessons were taught according to the science curriculum. The systems thinking skills test was used as a quantitative data collection tool and observations, student journals, and interviews were used as qualitative data collection tools. As a result of the research, it was found that there was a significant difference in the systems thinking skills of the students in the experimental group compared to the control group [F(1,152) = 12.23, p < 0.05]. It can be said that the change in systems thinking skills was due to establishing a relationship between concepts, looking at events holistically and understanding how the system can change with the disappearance or addition of a variable in the system.

Focus on software

Background: A key strength of population sciences and epidemiology is the breadth and depth of the data these studies, especially prospective cohorts, collect over time. However, publications from and websites of these studies tend to present collapsed or aggregate data in traditional tables, often underestimating the true complexity of information in those data. One goal of the NIH Strategic Plan for Data Science is to “support the development and dissemination of advanced data management, analytics, and visualization tools.” We recently developed a robust, innovative, and scalable data visualization environment for the California Teachers Study (CTS), a prospective cancer epidemiology cohort. Methods: We identified the need to eliminate the practice of storing CTS data in a collection of disparate data silos and constructed a data warehouse to compile two decades of study data, eliminating additional harmonization steps. We then developed a data commons, a remotely accessible environment where cohort data could be utilized by our researchers and study collaborators. This allowed questionnaire-based data, cancer registry information, and hospitalization and mortality data from our study participants to be easily accessed in a secure environment. Serving as a single unified source of information, the data warehouse, coupled with the industry-leading data visualization tool Tableau, was configured using the following key epidemiologic cohort activities: reports, cohort selection, calculated values, and exploring and transforming data variables for analysis. Results: With the ability to sort and filter on specific covariates we can define subsets of populations for cohort selection and endpoint-specific analysis. Using interactive reports with drags and drops, and creating calculated variables within Tableau, gives us the ability to visualize time between event data, such as time from study entry to a cancer diagnosis, multiple cancer diagnoses, hospitalizations, or mortality without requiring programmatic back-end coding. Conclusion: The creation of a data warehouse and data commons has given the CTS the ability to visualize information about the demographics and health-related patterns of our study participants, leading to faster answers to complex questions about our data. The interactive data visualizations that we have created allow users the ability to identify new patterns easily, and drill down into the data for a more detailed look. Defining analytic populations through cohort selection and adding geospatial data to our data warehouse has already resulted in the discovery of new patterns that had been unidentified before—simply by providing the same information but utilizing a more accessible and innovative approach to presenting it. As epidemiology uses and shares more of its data, it’s essential to help the widest possible community understand those data in detail. Data visualization can provide specific benefits to key, essential population sciences research activities. Citation Format: Nadia T. Chung, James V. Lacey Jr., Emma Spielfogel, Paul Hughes, Sandeep Chandra, Elena Martinez, Jennifer Benbow. Opportunities for and benefits of incorporating data visualization in population sciences [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr A01.

Objective Systems thinking can be counterintuitive to everyday ways of knowing. This can surface doubt around predicted patterns of emergence in complex systems data, especially as it relates to the current climate crisis and related justice-oriented solutions. Method Our study describes a four-year design-based research project in which we engaged high school biology students in complex systems modelling to understand linkages between increasing ocean temperatures and the rate and severity of disease outbreaks in sea stars. Results Our findings showed that students approached climate data with uncertainty and viewed their lives as separate from the impacts of climate change. Through iterative design work, youth used authentic data and computational tools to construct geospatial and causal-loop models of climate-related disease outbreaks that situated case studies within broader socioecological and sociotechnical contexts of historic and powered human actions. Through a speculative design lens, models were transformed from data visualization tools to mediums for storying and re-storying present and future worlds for multispecies survival in the face of the climate crisis. Conclusion Students shifted their understandings of disease outbreaks from a technical perspective to a more social and situated lens of care and responsibility for mitigating the impacts of the climate crisis on human and more-than-human communities. KEY POINTS What is already known about this topic: (1) Teachers that cover climate change often focus solely on technical and data-based aspects during instruction, such as the carbon cycle and increasing atmospheric CO2 levels, without including the social and political contexts through which the climate emergency emerged. (2) Engaging students in meaningful action and problem-solving creates positive affective outcomes and retains hope in students in the face of future climate impacts. (3) There is still more that we need to learn about how to design learning environments that cultivate hope, agency, and multispecies caring in K-12 contexts. What this topic adds: (1) We show how complex systems modelling and data visualization can cultivate multispecies caring and climate action by situating climate-related phenomena in larger socioecological and sociotechnical systems. (2) Through a speculative design lens, we show how modelling can be transformed from data visualization to storytelling and re-storying present and future worlds that centre on ecological and multispecies flourishing. Here we show a new ontological dimension of modelling practices as future and world making. (3) Our research shows how multispecies caring emerged as an action in this world, and for creating the future worlds students wanted to see; it became an affective dimension for making meaning amid the complexity and uncertainty of the climate crisis.

PurposeThe authors developed a pedagogical framework called the game-based structural debriefing (GBSD) to leverage the affordances of video games for teaching systems thinking. By integrating system dynamics visualization tools within a set of debriefing activities, GBSD helps teachers make systems thinking an explicit goal of the gameplay and learning when they use available educational games in the classroom.Design/methodology/approachThis study uses a design-based research methodology with the goals of validating GBSD and investigating the utility of GBSD across different contexts as a design source to develop game-based curriculum. Over the course of 12 months, the authors conducted one focus group interview and three design workshops with participating teachers and master teachers. Between the workshops, the team rapidly iterated the framework, as well as curricular materials, in collaboration with the teachers.FindingsThe authors developed a curriculum unit that integrates systems dynamics visualization tools and a video game for middle school life science ecosystem curriculum. The unit was implemented by the three teachers who participated in the co-design. The implementations confirmed the flexibility of the unit because teachers created additional instructional materials that supplemented the GBSD protocol and addressed the unique limitations and needs of their classrooms.Originality/valueGBSD builds on system dynamics, which is a distinct academic discipline and methodology, and it uses its visualization tools, which are not widely used in the systems thinking educational literature. GBSD is also unique, in that it applies these tools within the debriefing activities developed for an off-the-shelf educational game. This paper illustrates how a design framework can be used to support teachers’ thoughtful integration of games in curriculum development.

Today, individuals need a solid understanding of how earth as a system works in order to understand and develop solutions for science-based issues affecting societies on a global scale such as natural disasters, global warming or COVID-19 pandemic. Humans also deliberate space investigations, such as interplanetary travels and life in space. Therefore, it is necessary to develop new skills to meet the requirements of the current world order overcome this kind of issues through science and geography education. Spatial and systems thinking skills are two of 21st century skills in teaching and learning science-related topics. The study purposes to examine current Turkish primary science curriculum regarding these skills. Document analysis method of qualitative research design was implemented in the study. Emergent qualitative analysis methods of content analysis and open coding were used. It is sought to analyze the research literature on geography education in order to delineate competences necessary for understanding societal problems related to earth science and astronomy-based physics units considering Turkish primary science curriculum framework. The findings of the study showed that the competences spatial and systems thinking skills should be more integral part of science curricula. Especially for spatial thinking skills, educators and policy makers should reconsider the learning objectives in primary science curriculum framework. In conclusion, the results indicated that spatial and systems thinking skills were partially acquired in the curricula. Some units should be clarified and enhanced depend on spatial thinking abilities, systems thinking abilities or both of them.

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Business owners, managers, and policymakers are overwhelmed by the volume of data available in their company’s internal databases and external sources. The ability to represent meaningful and concise information that facilitates decision-making is challenging. Consider a technology firm such as Company A, where a large quantity of data is generated across numerous data sources covering all the North American locations. Traditionally, these data come in different formats such as email attachments (.pdf, .xlsx, .docx, etc.) and at different time intervals, e.g., daily, weekly, and some non-periodic. The data arrives at the team’s folder on the Microsoft SharePoint platform, where team members calculate key metrics. This existing process is tedious, manual, and prone to computation or latency errors due to database refresh. Data visualization design and automation serve as a solution to these issues by creating a method through which large volumes of data can be easily aggregated, represented, consumed, and understood. When data is visualized correctly, it is easy to draw simple, actionable conclusions. This study will explore how modern data visualization tools can be used to help policymakers and managers understand supply chain operations and performance in a relatively short time, as well as make strategic plans by interacting with the dashboard. This would be achieved by creating a visualization dashboard for the real-time monitoring of Company A North America Supply Chain data across all departments using the Microsoft Power BI tool. This would help to see the big picture all at once and enable business owners, key managers, and policymakers to make sound business choices and judgments. The overarching goal of this project is to automate the data collection and visualization process to help key business owners make informed decisions. This will allow the team managers to spend less time compiling large volumes of data and leverage the visualization tool to better identify and mitigate risk as well as proactively uncover valuable opportunities that lie in their databases.

Review of the use of system dynamics (SD) in scrutinizing local energy transitions