An Agent-Based Model of Discourse Pattern Formation in Small Groups of Competing and Cooperating Members

Discourse patterns in a small group are assumed to form largely through the group's internal social dynamics when group members compete for floor in discourse. Here we approach such discourse pattern formation through the agent-based model (ABM). In the ABM introduced here the agents' interactions and participation in discussions are dependent on the agents' inherent potential activity to participate in discussion and on realised, externalised activity, discursivity. The discourse patterns are assumed to be outcomes of peer-to-peer comparison events, where agents competitively compare their activities and discursivities, and where activities also affect agents' cooperation in increasing the discursivity, i.e. floor for discourse. These two effects and their influence on discourse pattern formation are parameterised as comptetivity and cooperativity. The discourse patterns are here based on the agents' discursivity. The patterns in groups of four agents up to seven agents are characterised through triadic census (i.e. though counting triadic sub-patterns). The cases of low competitivity is shown to give rise to fully connected egalitarian, triadic patterns, which with increasing competitivity are transformed to strong dyadic patterns. An increase in cooperativity enhances the emergence of egalitarian triads and helps to maintain the formation of fully and partially connected triadic pattern also in cases of high competitivity. In larger groups of six and seven agents, isolation becomes common, in contrast to groups of four agents where isolation is relatively rare. These results are in concordance with known empirical findings of discourse and participation patterns in small groups.Discourse patterns in a small group are assumed to form largely through the group's internal social dynamics when group members compete for floor in discourse. Here we approach such discourse pattern formation through the agent-based model (ABM). In the ABM introduced here the agents' interactions and participation in discussions are dependent on the agents' inherent potential activity to participate in discussion and on realised, externalised activity, discursivity. The discourse patterns are assumed to be outcomes of peerto- peer comparison events, where agents competitively compare their activities and discursivities, and where activities also affect agents' cooperation in increasing the discursivity, i.e. floor for discourse. These two effects and their influence on discourse pattern formation are parameterised as comptetivity alpha and cooperativity lambda. The discourse patterns are here based on the agents' discursivity. The patterns in groups of four agents up to seven agents are characterised through triadic census (i.e. though counting triadic sub-patterns). The cases of low competitivity alpha is shown to give rise to fully connected egalitarian, triadic patterns, which with increasing competitivity are transformed to strong dyadic patterns. An increase in cooperativity lambda enhances the emergence of egalitarian triads and helps to maintain the formation of fully and partially connected triadic pattern also in cases of high competitivity. In larger groups of six and seven agents, isolation becomes common, in contrast to groups of four agents where isolation is relatively rare. These results are in concordance with known empirical findings of discourse and participation patterns in small groups.Peer reviewe

The Effectiveness of Simulation in Science Learning on Conceptual Understanding: A Literature Review

Conceptual understanding, which permits one to transfer an explanation of a phenomenon with different ways is clearly a goal in science learning. One way to achieve conceptual understanding is using simulation. The purpose of this review is to analyze the effectiveness the impact of the use simulation on conceptual understanding in science learning. Based on the literature review, significant aspects of how such simulation influence on conceptual understanding is identified. The finding indicated that simulations work to improve understanding the concept of science, not only students’ understanding but also pre-service teachers’ understanding

GroundsWell: Community-engaged and data-informed systems transformation of Urban Green and Blue Space for population health – a new initiative

This is the published version. Available on open access from F1000 Research via the DOI in this record[version 1; peer review: 1 approved].Data availability: No data are available with this article.Natural environments, such as parks, woodlands and lakes, have positive impacts on health and wellbeing. Urban Green and Blue Spaces (UGBS), and the activities that take place in them, can significantly influence the health outcomes of all communities, and reduce health inequalities. Improving access and quality of UGBS needs understanding of the range of systems (e.g. planning, transport, environment, community) in which UGBS are located. UGBS offers an ideal exemplar for testing systems innovations as it reflects place-based and whole society processes, with potential to reduce non-communicable disease (NCD) risk and associated social inequalities in health. UGBS can impact multiple behavioural and environmental aetiological pathways. However, the systems which desire, design, develop, and deliver UGBS are fragmented and siloed, with ineffective mechanisms for data generation, knowledge exchange and mobilisation. Further, UGBS need to be co-designed with and by those whose health could benefit most from them, so they are appropriate, accessible, valued and used well. This paper describes a major new prevention research programme and partnership, GroundsWell, which aims to transform UGBS-related systems by improving how we plan, design, evaluate and manage UGBS so that it benefits all communities, especially those who are in poorest health. We use a broad definition of health to include physical, mental, social wellbeing and quality of life. Our objectives are to transform systems so that UGBS are planned, developed, implemented, maintained and evaluated with our communities and data systems to enhance health and reduce inequalities. GroundsWell will use interdisciplinary, problem-solving approaches to accelerate and optimise community collaborations among citizens, users, implementers, policymakers and researchers to impact research, policy, practice and active citizenship. GroundsWell will be shaped and developed in three pioneer cities (Belfast, Edinburgh, Liverpool) and their regional contexts, with embedded translational mechanisms to ensure that outputs and impact have UK-wide and international application.UK Prevention Research PartnershipHSC Research and Development Office Northern Irelan

GroundsWell: Community-engaged and data-informed systems transformation of Urban Green and Blue Space for population health – a new initiative

[version 1; peer review: awaiting peer review]Natural environments, such as parks, woodlands and lakes, have positive impacts on health and wellbeing. Urban Green and Blue Spaces (UGBS), and the activities that take place in them, can significantly influence the health outcomes of all communities, and reduce health inequalities. Improving access and quality of UGBS needs understanding of the range of systems (e.g. planning, transport, environment, community) in which UGBS are located. UGBS offers an ideal exemplar for testing systems innovations as it reflects place-based and whole society processes, with potential to reduce non-communicable disease (NCD) risk and associated social inequalities in health. UGBS can impact multiple behavioural and environmental aetiological pathways. However, the systems which desire, design, develop, and deliver UGBS are fragmented and siloed, with ineffective mechanisms for data generation, knowledge exchange and mobilisation. Further, UGBS need to be co-designed with and by those whose health could benefit most from them, so they are appropriate, accessible, valued and used well. This paper describes a major new prevention research programme and partnership, GroundsWell, which aims to transform UGBS-related systems by improving how we plan, design, evaluate and manage UGBS so that it benefits all communities, especially those who are in poorest health. We use a broad definition of health to include physical, mental, social wellbeing and quality of life. Our objectives are to transform systems so that UGBS are planned, developed, implemented, maintained and evaluated with our communities and data systems to enhance health and reduce inequalities. GroundsWell will use interdisciplinary, problem-solving approaches to accelerate and optimise community collaborations among citizens, users, implementers, policymakers and researchers to impact research, policy, practice and active citizenship. GroundsWell will be shaped and developed in three pioneer cities (Belfast, Edinburgh, Liverpool) and their regional contexts, with embedded translational mechanisms to ensure that outputs and impact have UK-wide and international application. Keyword

Interactive Computer Simulation and Animation Learning Modules: A Mixed-Method Study of Their Effects on Students\u27 Problem Solving in Particle Dynamics

Computer simulation and animation (CSA) has been receiving growing attention and wide application in the engineering education community. The goal of this dissertation research was to improve students\u27 conceptual understanding and procedural skills for solving particle dynamics problems, by developing, implementing and assessing 12 interactive computer simulation and animation learning modules. The developed CSA learning modules integrate visualization with mathematical modeling to help students directly connect engineering dynamics with mathematics. These CSA modules provide a constructivist environment where students can study physical laws, demonstrate mental models, make predictions, derive conclusions, and solve problems. A mixed-method research was conducted in this study: quasi-experimental method (quantitative), and survey questionnaires and interviews (qualitative and quantitative). Quasi-experimental research involving an intervention group and a comparison group was performed to investigate the extent that the developed CSA learning modules improved students\u27 conceptual understanding and procedural skills in solving particle dynamics problems. Surveys and interviews were administrated to examine students\u27 learning attitudes toward and experiences with the developed CSA learning modules. The results of quasi-experimental research show that the 12 CSA learning modules developed for this study increased students\u27 class-average conceptual and procedural learning gains by 29% and 40%, respectively. Therefore, these developed CSA modules significantly improved students\u27 conceptual understanding and procedural skills for solving particle dynamics problems. The survey and interview results show that students had a positive experience with CSA learning

インドネシア中学生における光及び光学機器の概念的理解改善のためのコンピュータ・シミュレーションの効果

広島大学(Hiroshima University)博士(教育学)Doctor of Philosophy in Educationdoctora

Estética y modelos computacionales para analizar dinámicas urbanas

Este trabajo de investigación teórica busca aproximarse al estudio de las dinámicas urbanas a través de la teoría de la complejidad, en este sentido la implementación de modelos computacionales como método de análisis, es crucial a la hora de estudiar sistemas complejos como la ciudad, ya que contribuyen a la construcción de un conocimiento cuantificable y científico en torno a las dinámicas urbanas. Gracias a estos modelos, es posible experimentar, construir escenarios prospectivos y desarrollar teorías que apoyen la toma de decisiones en cuanto al diseño y la planeación del espacio urbano.This theoretical research work seeks to approach the study of urban dynamics through complexity theory in this sense the implementation of computational models as a method of analysis is crucial when studying complex systems as the city, as they contribute to the construction of a quantifiable and scientific knowledge about urban dynamics. With these models, you may experience, build future scenarios and develop theories to support decision making in the design and planning of urban space.Arquitecto (a)Pregrad

Towards Bayesian Model-Based Demography

This open access book presents a ground-breaking approach to developing micro-foundations for demography and migration studies. It offers a unique and novel methodology for creating empirically grounded agent-based models of international migration – one of the most uncertain population processes and a top-priority policy area. The book discusses in detail the process of building a simulation model of migration, based on a population of intelligent, cognitive agents, their networks and institutions, all interacting with one another. The proposed model-based approach integrates behavioural and social theory with formal modelling, by embedding the interdisciplinary modelling process within a wider inductive framework based on the Bayesian statistical reasoning. Principles of uncertainty quantification are used to devise innovative computer-based simulations, and to learn about modelling the simulated individuals and the way they make decisions. The identified knowledge gaps are subsequently filled with information from dedicated laboratory experiments on cognitive aspects of human decision-making under uncertainty. In this way, the models are built iteratively, from the bottom up, filling an important epistemological gap in migration studies, and social sciences more broadly

Das conceções alternativas às conceções científicas com metodologias ativas de aprendizagem e utilização de simuladores: uma intervenção didática para a aprendizagem da Física do som

A aprendizagem da Física é uma área repleta de dificuldades e é frequente as conceções alternativas limitarem a compreensão profunda de diversos domínios desta disciplina. Em especial, no domínio do som, é comum encontrar essas conceções. As metodologias ativas de aprendizagem têm sido usadas com eficácia para o ensino das ciências numa abordagem construtivista, onde se parte das conceções alternativas para promover a construção de modelos mentais que se aproximam dos modelos conceptuais cientificamente aceites. Por seu lado, também a utilização de simuladores computacionais contribui significativamente para vários domínios de aprendizagem, no âmbito do ensino das ciências. Nesta investigação, pretende-se averiguar quais os contributos que uma sequência didática de atividades aporta para a evolução conceptual dos alunos relativamente aos conceitos envolvidos no domínio curricular “Produção e Propagação do Som” da disciplina de Físico-Química. As atividades assentam numa metodologia de aprendizagem baseada em problemas com utilização de simuladores computacionais, constituindo-se uma metodologia ativa de aprendizagem, nas quais os alunos têm um papel central e o professor é um facilitador. Analisam-se as conceções alternativas dos alunos acerca dos diferentes conteúdos curriculares em estudo e a sua respetiva evolução conceptual, assim como a perceção dos alunos relativamente à metodologia de aprendizagem e aos simuladores utilizados. A investigação seguiu uma metodologia de cariz qualitativo, operacionalizada numa investigação-ação, com uma turma de alunos do 8.º ano, do Agrupamento de Escolas Domingos Sequeira, em Leiria. Os resultados mostram que existiu evolução na compreensão conceptual dos conteúdos curriculares em estudo em todas as questões analisadas, mas que a sequência didática não foi suficiente para que todos os alunos construíssem conceções cientificamente aceites em todos os conteúdos curriculares estudados, para além de se verificar o desenvolvimento de algumas conceções alternativas. Os alunos revelam satisfação com a metodologia de aprendizagem utilizada e com a utilização dos simuladores, mas afirmam sentir necessidade do papel mais interventivo do professor para sustentar e validar as aprendizagens realizadas

Towards Bayesian Model-Based Demography

This open access book presents a ground-breaking approach to developing micro-foundations for demography and migration studies. It offers a unique and novel methodology for creating empirically grounded agent-based models of international migration – one of the most uncertain population processes and a top-priority policy area. The book discusses in detail the process of building a simulation model of migration, based on a population of intelligent, cognitive agents, their networks and institutions, all interacting with one another. The proposed model-based approach integrates behavioural and social theory with formal modelling, by embedding the interdisciplinary modelling process within a wider inductive framework based on the Bayesian statistical reasoning. Principles of uncertainty quantification are used to devise innovative computer-based simulations, and to learn about modelling the simulated individuals and the way they make decisions. The identified knowledge gaps are subsequently filled with information from dedicated laboratory experiments on cognitive aspects of human decision-making under uncertainty. In this way, the models are built iteratively, from the bottom up, filling an important epistemological gap in migration studies, and social sciences more broadly