An official website of the United States government
Visual thinking with diagrams is a crucial skill for learning and problem-solving in STEM subjects. To improve in this area, students need a variety of visual problems for deliberate practice. However, in our interviews, educators shared that they struggle to create these practice exercises because of limitations of existing tools. We introduce Edgeworth, a tool designed to help educators easily create visual problems. Edgeworth works in two main ways: firstly, it takes a single diagram from the user and systematically alters it to produce many variations, which the educator can then choose from to create multiple problems. Secondly, it automates the layout of diagrams, ensuring consistent high quality without the need for manual adjustments. To assess Edgeworth, we carried out case studies, a technical evaluation, and expert walkthrough demonstrations. We show that Edgeworth can create problems in three domains: geometry, chemistry, and discrete math. These problems were authored in just 15 lines of Edgeworth code on average. Edgeworth generated usable answer options within the first 10 diagram variations in 87% of authored problems. Finally, educators gave positive feedback on Edgeworth's utility and the real-world applicability of its outputs.
more »
« less
Solving Geospatial Problems under Extreme Time Constraints: A Call for Inclusive Geocomputational Education
To prepare our next generation to face geospatial problems that have extreme time constraints (e.g., disasters, climate change) we need to create educational pathways that help students develop their geocomputational thinking skills. First, educators are central in helping us create those pathways, therefore, we need to clearly convey to them why and in which contexts this thinking is necessary. For that purpose, a new definition for geocomputational thinking is suggested that makes it clear that this thinking is needed for geospatial problems that have extreme time constraints. Secondly, we can not further burden educators with more demands, rather we should work with them to better understand the existing curricular context and implement sensible changes where it is most impactful. Lastly, the impacts of these implementations need to be carefully measured, and particularly in terms of broadening participation. A few examples are provided that show promise.
more »
« less
- Award ID(s):
- 2031418
- PAR ID:
- 10480946
- Publisher / Repository:
- Purdue e-Pibs
- Date Published:
- Journal Name:
- Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE) Forum 2023 Harnessing the Geospatial Data Revolution for Sustainability Solutions
- Subject(s) / Keyword(s):
- computational thinking, geocomputation, geospatial, education, broadening participation
- Format(s):
- Medium: X
- Location:
- New York City
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Geospatial technologies and geographic methods are foundational skills in modern water resources monitoring, research, management, and policy-making. Understanding and sustaining healthy water resources depends on spatial awareness of watersheds, land use, hydrologic networks, and the communities that depend on these resources. Water professionals across disciplines are expected to have familiarity with hydrologic geospatial data. Proficiency in spatial thinking and competency reading hydrologic maps are essential skills. In addition, climate change and non-stationary ecological conditions require water specialists to utilize dynamic, time-enabled spatiotemporal datasets to examine shifting patterns and changing environments. Future water specialists will likely require even more advanced geospatial knowledge with the implementation of distributed internet-of-things sensor networks and the collection of mobility data. To support the success of future water professionals and increase hydrologic awareness in our broader communities, teachers in higher education must consider how their curriculum provides students with these vital geospatial skills. This paper considers pedagogical perspectives from educators with expertise in remote sensing, geomorphology, human geography, environmental science, ecology, and private industry. These individuals share a wealth of experience teaching geographic techniques such as GIS, remote sensing, and field methods to explore water resources. The reflections of these educators provide a snapshot of current approaches to teaching water and geospatial techniques. This commentary captures faculty experiences, ambitions, and suggestions for teaching at this moment in time.more » « less
-
As problems become more complex, global, and interdisciplinary, engineers need to develop novel solutions and utilize resources, information, and tools in strategic and creative ways. Divergent thinking describes a process where multiple options, pathways, alternatives, or ideas are developed. For engineering students, divergent thinking can facilitate flexibility and expand opportunities considered when solving problems. To develop divergent thinking skills in engineering, we must understand how it is (and is not) facilitated in current engineering education experiences. Current pedagogy and resources available in engineering education on divergent thinking are limited. Thus, our research focused on exploring educational experiences in which students felt they considered divergent thinking. In this paper, we describe the iterative development of an interview protocol to elicit student experiences related to opportunities for divergent thinking. From the initial round of piloting, we found student awareness of divergent thinking was limited. Our findings highlight the need to structure questions in ways that align with students’ existing understandings of their engineering experiences. Our team made modifications to the protocol to address this, including using accessible terms to describe divergent thinking, asking students to describe one example project they remembered well, and. focusing questions within one step of the project selected by the student as most relevant to their exploration of alternatives. This iterative development of the protocol was successful in eliciting divergent thinking experiences across their work.more » « less
-
This thesis explores geospatial vector data, including geometric shapes such as points, lines, and polygons. This data is crucial in navigation, urban planning, and many more applications. Geospatial computing is a multidisciplinary field that focuses on creating techniques and tools to handle large geospatial datasets. Given the reliance on data lakes to store large data sets in their raw formats, it is critical to have full support for geospatial datasets to enable scalable processing. To address this, we make two contributions in this area. First, we propose a column-oriented binary format called Spatial Parquet, which integrates geospatial vector data into Apache Parquet that enables significant data compression and efficient querying. Second, to improve support for semi-structured data, we introduce a distributed JSON processor for scalable SQL queries on large JSON datasets, including GeoJSON. It processes complex datasets like Open Street Map with features such as projection and filter push-down. Advances in Deep Learning (DL), including foundation models and Large Language Models (LLMs), offer opportunities for geospatial data analysis. We make three main contributions in this area. First, we study how to design DL models that can express a wide range of geospatial functions. We explore three representations: an image-based representa- tion using geo-referenced histograms (GeoImg), a graph-based point-set representation (Ge- oGraph), and a vector-based representation using a Fourier encoder (GeoVec). We formal- ize these representations and design corresponding models: ResNet and UNet for the first, PointNet++ for the second, and Poly2Vec with Transformers for the third. We evaluate all approaches on four spatial problems, showing the accuracy and effectiveness of the three approaches. Second, we create a benchmark called GS-QA for evaluating spatial question- answering with LLMs. A semi-automated process generates diverse question-answer pairs that cover various spatial objects, predicates, and complexities. An evaluation methodology is suggested with some experiments. Finally, a prototype for generating geospatial vector data from text prompts, called GeoGen I, is proposed. It has potential for applications such as spatial interpolation, data augmentation, and change analysis. We adapt diffusion models, traditionally used for generating realistic images, as geospatial data generators. We also explore their use for similarity search through geospatial data embeddings, highlighting the potential of vector databases in this domain. This thesis advances geospatial data processing, storage, analysis, and generation, opening new research pathways in geospatial computing.more » « less
-
null (Ed.)The ever rising volume of geospatial data is undeniable. So is the need to explore and analyze these datasets. However, these datasets vary widely in their size, coverage, and accuracy. Therefore, users need to assess these aspects of the data to choose the right dataset to use in their analysis. Unfortunately, all the publicly available repositories for geospatial datasets provide a list of datasets with some information about them with no way to explore the datasets beforehand. Through this demonstration, we propose the repository, UCR-Star, that is capable of hosting hundreds of thousands of geospatial datasets that a user can explore visually to judge their quality before even downloading them. This demo provides a deeper dive into the core engine behind UCR-Star. It provides a web interface geared towards database researchers to understand how the index internally works. It provides a comparison interface where the attendees can see side-by-side how two versions of the system work with the ability to customize each of them separately. Finally, the interface reports the response time of the indexes for a quantitative comparison.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Proceeding:
- https://doi.org/10.5703/1288284317669
