310,575 research outputs found

    Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems

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    The modelling, analysis, and visualisation of dynamic geospatial phenomena has been identified as a key developmental challenge for next-generation Geographic Information Systems (GIS). In this context, the envisaged paradigmatic extensions to contemporary foundational GIS technology raises fundamental questions concerning the ontological, formal representational, and (analytical) computational methods that would underlie their spatial information theoretic underpinnings. We present the conceptual overview and architecture for the development of high-level semantic and qualitative analytical capabilities for dynamic geospatial domains. Building on formal methods in the areas of commonsense reasoning, qualitative reasoning, spatial and temporal representation and reasoning, reasoning about actions and change, and computational models of narrative, we identify concrete theoretical and practical challenges that accrue in the context of formal reasoning about `space, events, actions, and change'. With this as a basis, and within the backdrop of an illustrated scenario involving the spatio-temporal dynamics of urban narratives, we address specific problems and solutions techniques chiefly involving `qualitative abstraction', `data integration and spatial consistency', and `practical geospatial abduction'. From a broad topical viewpoint, we propose that next-generation dynamic GIS technology demands a transdisciplinary scientific perspective that brings together Geography, Artificial Intelligence, and Cognitive Science. Keywords: artificial intelligence; cognitive systems; human-computer interaction; geographic information systems; spatio-temporal dynamics; computational models of narrative; geospatial analysis; geospatial modelling; ontology; qualitative spatial modelling and reasoning; spatial assistance systemsComment: ISPRS International Journal of Geo-Information (ISSN 2220-9964); Special Issue on: Geospatial Monitoring and Modelling of Environmental Change}. IJGI. Editor: Duccio Rocchini. (pre-print of article in press

    The Outcomes of Teaching and Learning about Sound based on Science Technology and Society (STS) Approach

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    AbstractThis study reported Grade 11 students analytical thinking and attitude toward science in teaching and learning about sound through science technology and society (STS) approach. The participants were 46 Grade 11 students in Maung, Kalasin, Thailand. The teaching and learning about sound through STS approach had carried out for 6 weeks. The sound unit through STS approach was developed based on framework of Yuenyong (2006a) that consisted of five stages including (1) identification of social issues, (2) identification of potential solutions, (3) need for knowledge, (4) decision-making, and (5) socialization stage. Students’ analytical thinking and attitude toward science was collected during their learning by participant observation, analytical thinking protocol, students’ tasks, and journal writing. The findings revealed that students could gain their capability of analytical thinking. They could give ideas or behave the characteristics of analytical thinking such as thinking for classifying, compare and contrast, reasoning, interpreting, collecting data and decision making. Students’ journal writing reflected that the STS class of sound motivated students. The paper will discuss implications of these for science teaching and learning through STS in Thailand

    PROFILE OF STUDENTS ANALYTICAL THINKING SKILLS IN LEARNING STYLE FOR COMPLETING SUBSTANCE PRESSURE PROBLEMS

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    The skill to think analytically is an important skill for students to have, where the skill to think analytically helps students solve problems in science lessons that require an analytical skill to solve them. The research method is qualitative with a naturalistic design. This research was conducted at some Junior High schools in Ponorogo using the purposive sampling technique. Data were collected using in-depth interviews, observation, and documentation, then analyzed using quantitative descriptive and qualitative descriptive. This study aims to determine the profile of students' analytical thinking skills in solving problem-based problems in the grade 8th at pressure material and determine the pattern of its relationship with scientific exploration. The results showed that: 1) the profile of the analytical thinking skill in terms of the learning styles of students, namely the visual style subjects intend to explain what is known through the direct explanation in more detail, the audiovisual subjects are more likely to form simpler patterns with reasoning patterns generalization and on kinesthetic subjects tend to apply different (unique) concepts, but still have a relationship with the problem, 2) the pattern of the relationship between analytical thinking skills and learning styles, namely the exploration of science, which includes aspects of experience, reasoning, modalities, and the mindset of students. Through the results of this study, it is hoped that it can provide theoretical and practical insights for educators in determining approaches and strategies for achieving science analytical competence according to students' learning styles at school

    Extreme-scale visual analytics

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    pre-printThe September/October 2004 CG&A introduced the term visual analytics (VA) to the computer science literature.1 In 2005, an international advisory panel with representatives from academia, industry, and government defined VA as "the science of analytical reasoning facilitated by interactive visual interfaces."2 VA has grown rapidly into a vibrant R&D community offering data analytics and exploration solutions to both scientific and nonscientific problems in diverse domains and platforms. This special issue further examines advances related to extreme-scale VA problems, their analytical and computational challenges, and their real-world applications

    Active-Learning Methods to Improve Student Performance and Scientific Interest in a Large Introductory Course

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    Teaching methods that are often recommended to improve the learning environment in college science courses include cooperative learning, adding inquiry-based activities to traditional lectures, and engaging students in projects or investigations. Two questions often surround these efforts: 1) can these methods be used in large classes; and 2) how do we know that they are increasing student learning? This study, from the University of Massachusetts, describes how education researchers have transformed the environment of a large-enrollment oceanography course (600 students) by modifying lectures to include cooperative learning via interactive in-class exercises and directed discussion. Assessments were redesigned as "two-stage" exams with a significant collaborative component. Results of student surveys, course evaluations, and exam performance demonstrate that learning of the subject under these conditions has improved. Student achievement shows measurable and statistically significant increases in information recall, analytical skills, and quantitative reasoning. There is evidence from both student surveys and student interview comments that for the majority of students, the course increased their interest in science -- a difficult effect to achieve with this population. Educational levels: Graduate or professional, Graduate or professional

    The challenge of complexity for cognitive systems

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    Complex cognition addresses research on (a) high-level cognitive processes – mainly problem solving, reasoning, and decision making – and their interaction with more basic processes such as perception, learning, motivation and emotion and (b) cognitive processes which take place in a complex, typically dynamic, environment. Our focus is on AI systems and cognitive models dealing with complexity and on psychological findings which can inspire or challenge cognitive systems research. In this overview we first motivate why we have to go beyond models for rather simple cognitive processes and reductionist experiments. Afterwards, we give a characterization of complexity from our perspective. We introduce the triad of cognitive science methods – analytical, empirical, and engineering methods – which in our opinion have all to be utilized to tackle complex cognition. Afterwards we highlight three aspects of complex cognition – complex problem solving, dynamic decision making, and learning of concepts, skills and strategies. We conclude with some reflections about and challenges for future research

    Teaching Specifications Using An Interactive Reasoning Assistant

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    The importance of verifiably correct software has grown enormously in recent years as software has become integral to the design of critical systems, including airplanes, automobiles, and medical equipment. Hence, the importance of solid analytical reasoning skills to complement basic programming skills has also increased. If developers cannot reason about the software they design, they cannot ensure the correctness of the resulting systems. And if these systems fail, the economic and human costs can be substantial. In addition to learning analytical reasoning principles as part of the standard Computer Science curriculum, students must be excited about learning these skills and engaged in their practice. Our approach to achieving these goals at the introductory level is based on the Test Case Reasoning Assistant (TCRA), interactive courseware that allows students to provide test cases that demonstrate their understanding of instructor-supplied interface specifications while receiving immediate feedback as they work. The constituent tools also enable instructors to rapidly generate graphs of student performance data to understand the progress of their classes. We evaluate the courseware using two case-studies. The evaluation centers on understanding the impact of the tool on students\u27 ability to read and interpret specifications

    KEMAMPUAN PENALARAN MATEMATIK SISWA MTs

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    The reasoning is the ability to find a way to get the truth. In terminology, logic is the science that governs the process of human thinking so that the results presented can reach the truth. So it can be concluded also that by reasoning, humans can make an argument that is absolute and can be accepted by others through logical thinking. This research was conducted to determine the mathematical reasoning ability of MTs class 8 students on Triangle and Quadrilateral material based on indicators of mathematical reasoning ability that is to draw a logical conclusion and compose an argument, analogical reasoning, transductive reasoning, deductive reasoning, generalization and estimate answers, solutions or tendency with non-routine troubleshooting. The research method used is descriptive qualitative with data to be analyzed is qualitative data in the form of written and oral answers obtained from the written test. This research was conducted in MTs PPI 38 Padalarang with the subject of this research is class VIII-A which amounted to 31 people. Based on the results of research, students' mathematical reasoning abilities on MTs PPI 38 Padalarang are classified as sufficient. This is because there are some reasoning indicators that still need to be improved especially on analytical reasoning and generalization indicator as stated in the table which has been described
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