. "Environmental sciences"@en . . "Environmental Sciences"@en . . . "English"@en . . "Environmental quality and governance"@en . . "6" . "Contents:\nThis course enables students to identify how interdisciplinary scientific perspectives from the social and natural sciences can contribute to a shared understanding of risk and problem solving in complex environmental problems. You will be asked to explore the possible role of science in the public policy process by bringing together key concepts in environmental toxicology, animal ecology, public policy, and environmental governance. In the first half of the course you will become acquainted with technical skills required for gathering, processing, and interpreting data on environmental toxicology and animal ecology, as well as relevant social science theories on the relationship between science and politics in the public policy process. You will participate in a policy simulation in which you must generate, interpret, and present scientific data needed to estimate and reduce the risk associated with poor environmental quality and unsustainable use of ecosystem services. The course caters for students with a background in either natural or social sciences by providing a unique opportunity to integrate both perspectives into practical process of environmental research and policy. You will be introduced to a range of natural science concepts and methods used to assess the exposure and effect of persistent toxic compounds accumulating in the food chain, posing risks for eel population success and the health of eel consumers. From the social science side you will be introduced to concepts that can be used to analytically interpret the values, interests, and strategies of stakeholders involved in policy processes around risk identification, definition, acceptance, and management. By the end of the course you will be able to apply these skills in both the analysis and practice of science and policy making, while also taking into account other possible explanations and solutions for the dramatic decline in eel populations.\nLearning outcomes:\nAfter successful completion of this course students are expected to be able to:\n- understand environmental quality issues in a holistic way, taking into account the interplay of social and biophysical dynamics;\n- explain the basic principles and indicators of environmental quality and appraise their application in environmental risk assessment;\n- become acquainted with a range of toxicological and water quality research methods and analyse the uncertainty scientists and policy makers face when using the results of environmental risk assessments;\n- use social science concepts such as risk society and uncertainty to explain and assess the role of public and private actors in negotiations over environmental policy;\n- critically assess the formulation of policy goals, as well as technical and political strategies for engaging public and private sector actors to improve environmental quality;\n- critically assess the role of natural and social science research in addressing an environmental quality issue and draw lessons for one's own (future) professional practice;\n- identify and reflect upon selected key requirements for successful interdisciplinary or transdisciplinary environmental research." . . "Presential"@en . "TRUE" . . "General safety"@en . . "0" . "Contents:\nAt WUR we value your safety. Therefore many measures are taken in order to facilitate a safe study in Wageningen. And although all these measures are in place still one of the most important factors for a safe stay is YOUR behavior and knowledge of these measures. Therefore the General Safety course is mandatory for all new students at WUR. This course will introduce you to safety at Wageningen University & Research. We'll cover a broad range of topics to prepare you for a safe stay at our university: \n- house rules for working safe at WUR;\n- in case of emergency: how to respond to emergencies;\n- computer work: how to prevent complaints on arms, neck and shoulder (CANS/ RSI);\n- where to seek help/ assistance.\nLearning outcomes:\nAfter successful completion of this course, students are expected to be able to:\nhave a basic understanding on how safety is managed at WUR;\nknow the difference between risks and hazards;\ncan report accidents and incidents;\nknow which general rules apply regarding safety at WUR;\nknow which factors contribute to an ergonomic workspace;\ncan adjust the computer workspace to work ergonomically correct;\nknow how to act as an active bystander;\nknow how to give and ask for consent in social engagements;\nknow where to seek help." . . "Presential"@en . "TRUE" . . "Fieldwork safety"@en . . "0" . "Contents:\nAt WUR we value your safety. Going into the field for an excursion or for observations/ measurments exposes you to specific risks some of which you'd take for granted while visiting nature or urban environments outside the university perspective. Things like traffic or uneven terrain seems something that you face on a day to day basis, but need a more strikt approach while facing them in a professional way. Furthermore infection prevention and social responsible behavior are topics that require specific attention. Therefore many measures are taken in order to facilitate a safe study in Wageningen. And although all these measures are in place still one of the most important factors for a safe stay is YOUR behavior and knowledge of these measures. Therefore the Fieldwork Safety course is mandatory for all students going into the field for their education.This course will introduce you to safe fieldwork during courses at WUR and will cover a range of topics:\n- how to prepare for fieldwork;\n- what risk can you be exposed to when working in the field;\n- golden rules for fieldwork.\nLearning outcomes:\nAfter completing the course the student:\nknows which basic safety rules apply to fieldwork;\nknows how to prepare for the safety risks in fieldwork;\nknows some basic risks that apply to working in the field." . . "Presential"@en . "TRUE" . . "Principles of environmental sciences"@en . . "6" . "Contents:\nThis course offers students the opportunity of updating and extending their knowledge of the basic concepts of environmental sciences. Environmental problems in soil, water, and atmosphere are described and analysed. Attention is given to the socio-economic causes of these problems and their effects on organisms (including man) and ecosystems. The role science and technology can play in solving these problems is discussed, as is the role of interested actors such as government, business, environmental movement and individual citizens.\nIn a case study, small groups of students analyse a specific environmental problem, write a report and present a paper.\nLearning outcomes:\nAfter successful completion of this course students are expected to be able to:\n- demonstrate insight in the functioning of life/ecosystems and their response to changes in environment;\n- demonstrate insight in impacts of society on ecosystems and human beings;\n- demonstrate insight in possible technical solutions to environmental problems;\n- demonstrate insight in the role of social sciences in tackling environmental problems;\n- demonstrate insight in environmental awareness and environmental policy, and how they changed in time;\n- demonstrate insight in social causes of environmental problems, and their implications for environmental reform;\n- integrate and apply obtained knowledge by analyzing a particular environmental issue;\n- practice in determining one's own opinion on an environmental issue." . . "Presential"@en . "TRUE" . . "Research methodology in environmental science"@en . . "6" . "Contents:\nIn this course students study research design and methodology for interdisciplinary inquiry in the environmental sciences. Students will be prepared to think critically and systematically and to reflect on the trade-offs of methodological choices in research design in the interdisciplinary environmental sciences. There is a strong focus on methodology as an interrelated series of transparently justified, subjective, theory-dependent choices appropriate to context and purpose, rather than a fixed, technical set of rules leading to ‘objective scientific truth’. The knowledge that students gain on a theoretical level is applied in developing components of several designs for a practical and relevant research challenge. After laying out the fundamentals and characteristics of scientific methodology, we discuss how to choose a research topic, formulate research objectives, specify questions and then operationalize their key concepts into concrete variables and measurement strategies. Using examples from the environmental sciences, through which we improve students’ ability to read articles critically, we discuss three research designs (experimental, cross-sectional, longitudinal), various sampling strategies and data collection methods.\nLearning outcomes:\nAfter successful completion of this course students are expected to be able to:\n- explain the difference between a conceptual and a technical research design;\n- describe the defining features of an experimental, cross-sectional, and longitudinal design;\n- discuss the operationalization of one- and multidimensional concepts;\n- understand reasons and strategies for random and non-random sampling;\n- describe the pros and cons of the taught methods of data collection;\n- understand the conditions that must be met for causal claims;\n- discuss the reliability and validity of measurements;\n- contribute to interdisciplinary research designs for the environmental sciences;\n- critically read scientific literature." . . "Presential"@en . "TRUE" . . "Econometrics"@en . . "6" . "Contents:\nSocial scientists are often interested in relationships between different variables, e.g. consumption and prices. The objective of econometrics is to quantify such relationships using available data and statistical techniques and to interpret and use the resulting outcomes. So, econometrics is the interaction between (economic) theory, data and statistical methods (e.g. estimation and testing). The interaction of these three elements makes econometrics interesting and a must for the dedicated social scientist. The use of econometric tools stretches from economic and business disciplines to social science fields like sociology and history.\nThe course helps to understand a number of econometric issues and techniques, where emphasis is on applying econometrics to research questions in the broad field of social sciences.\nLearning outcomes:\nAfter successful completion of this course students are expected to be able to:\n- compare various econometric techniques\n- interpret econometric models and results of analysis\n- calculate econometric tests statistics and indicators\n- assess the quality of econometric analyses on the basis of various statistical tests and indicators\n- construct simple econometric models and estimate them using econometric techniques." . . "Presential"@en . "TRUE" . . "Programming in python"@en . . "6" . "Contents:\nProgramming plays an important role in many domains. In business and science writing or adapting computer programs to process, analyse and visualize data in a suitable format has become common practice. This course aims to help students to understand the underlying principles of programming and equip them with basic skills to create computer programs. The programming language Python serves broad application domains. Furthermore, Python is the most commonly used programming language in Machine Learning and Artificial Intelligence. The course also gives an introduction to libraries of available components, and how to use these for building your own programs.\nNote: The course in P5 is primarily for MSc students starting in Feb from programmes that include our course as a RO.\nLearning outcomes:\nAfter successful completion of this course students are expected to be able to:\n- create a computer program based on a given basic algorithm expressed in plain English;\n- adapt and combine standard algorithms to solve a given problem;\n- apply standard programming constructs for a given goal: repetition, selection, functions, composition, modules, aggregated data (arrays, lists, etc.), object-oriented concepts;\n- explain what a given piece of programming code (in Python) does;\n- detect and repair coding errors in a given piece of programming code;\n- use existing libraries taught during the course in programs, e.g., for data manipulation and visualization (Numpy, Pandas and Matplotlib)." . . "Presential"@en . "TRUE" . . "Basic statistics"@en . . "3" . "Contents:\nThe content of the course consists of the following topics:\ndata collection, descriptive statistics, introduction to probability theory;\nintroduction to probability distributions: binomial, normal, and student;\nestimation, testing hypotheses, constructing confidence intervals;\napplication of a binomial test for a population proportion;\napplication of t-tests to standard situations; one sample, two sample, one sample with paired observations;\ncorrelation and Simple linear regression with associated t-tests for coefficients;\nusing statistical software, in particular R-Commander;\nethical issues, as touching upon good statistical practice, will be discussed in class.\nIn the course it will be shown, where these statistical concepts are applied in scientific research. In the tutorials the practical problems are introduced, and a detailed program is given linking the content of the course to the tutorials.\nLearning outcomes:\nAfter successful completion of this course, students are expected to be able to:\n- remember and understand basic ideas of statistical inference and data collection\n- determine and explain the appropriate statistical procedure, given the description of the experiment, the research question, and the type of data\n- carry out the needed analyses for the discussed standard situations and assess the results in terms of the problem\n- perform a hypothesis test for intercept and slope and validate the model assumptions of a simple linear model\n- independently analyze data with the computer software R-Commander" . . "Presential"@en . "TRUE" . . "Advanced statistics"@en . . "6" . "Contents:\nThis course covers several more advanced statistical models and associated designs, and techniques for statistical inference, as relevant to life science studies. The main topics are categorical data, (multiple) regression, analysis of variance (including multiple comparisons), analysis of covariance, and non-parametric tests. The aims of an analysis, the model assumptions, the properties (and limitations) of the models and associated inferential techniques and the interpretation of results in terms of the practical problem will be discussed. Focus will be upon students gaining an understanding of the model ingredients, an (intuitive) understanding of inferential techniques, insight into data structures and implications for choice of model and analysis. Students will be able to perform analysis of data with statistical software, i.e. with R-Studio.\nLearning outcomes:\nAfter successful completion of this course students are expected to (within the limits of the subjects treated) be able to:\n- translate a research question into a statistical hypothesis: make a plan (type of design or sampling procedure) for the data collection.\n- choose an appropriate model with an understanding of the ingredients of the model in relation to the data;\n- analyse the data (with R-Studio);\n- interpret the results and form conclusions relevant for the actual problem." . . "Presential"@en . "TRUE" . . "Multivariate mathematics applied"@en . . "6" . "Contents:\nlinear algebra: matrices, eigenvalues and eigenvectors;\ncomplex numbers;\nordinary differential equations: separation of variables and variation of constants; systems of linear differential equations; systems of non-linear differential equations and classification of steady states;\nnumerical methods for ordinary differential equations: difference quotients and the Euler method; systems of differential equations; trapezoidal rule and Runge-Kutta; discretization errors; error propagation, stability and stiffness;\nintegration in two or three dimensions: limits of integration; coordinate systems and the Jacobian;\nintroduction to partial differential equations: flow models, diffusion and convection; boundary and initial conditions; steady states;\nvector fields: flow fields and force fields; the gradient and the laws of Fick, Fourier and Darcy; the potential function; divergence and the Laplace operator;\nFourier series for partial differential equations: separation of variables and the Sturm-Liouville problem; boundary value problems and Fourier series;\nuse of computer software.\nLearning outcomes:\nAfter successful completion of this course students are expected to be able to:\nexplain and apply concepts, methods and techniques from linear algebra, calculus, vector calculus and numerical mathematics;\napply mathematical knowledge, insights and methods to solve problems in the technological sciences using a systematic approach;\ncritically reflect upon the results;\ncorrectly report mathematical reasoning and argumentation;\ninterpret and evaluate the results in terms of the (physical, chemical, biological) problem that was modelled mathematically;\nuse mathematical software (Maple) in elaborating mathematical models." . . "Presential"@en . "TRUE" . . "Data science concepts"@en . . "6" . "Contents:\nThe amount and variety of data in the domains of living environment, food, health, society and natural resources increases very rapidly. Data thus plays an ever more central role in these areas, and careful processing and analysis can help extract information and infer new knowledge, eventually leading to new insights and a better understanding of the problem at hand. Knowledge of core concepts in data science – acquisition, manipulation, governance, presentation, exploration, analysis and interpretation – and elementary data science skills have become essential for researchers and professionals in most scientific disciplines. This course is an introduction to data science concepts, combining computer science, mathematics and domain expertise: acquiring and manipulating raw data, obtaining information by processing and exploration, and finally reaching understanding by analysis and modelling. This will be complemented by elementary skills in data wrangling, exploration and analysis. The content of the course is strongly embedded in a number of provided domain-specific cases from biology, health and nutrition and the environment, allowing students from many disciplines to appreciate the relevance of data science in their domains.\nLearning outcomes:\nAfter successful completion of this course students are expected to be able to:\n- explain the relevance of data and data science in research and application within their field of study;\n- recognize key concepts as used in data science practice and elaborated in continuation courses;\n- discuss the need for and describe approaches to data acquisition, manipulation, storage, governance, exploration, presentation, analysis and modeling;\n- apply a number of basic techniques for data wrangling, exploration and analysis in use cases related to their field of study, including practicing elementary scripting skills." . . "Presential"@en . "TRUE" . . "Data science for smart environments"@en . . "6" . "Contents:\nNew sources of data available from all kind of ‘smart technologies’ such as sensors, tracking-devices, crowd sourcing and social media open possibilities to create information and gain knowledge about our environment beyond that what is possible with ‘traditional’ sources of data. Especially analyses of spatial-temporal processes and interactions between people and their environment are accelerated by these new sources of data. Examples are the movements of people (tourists) through a city and the consequences for its accessibility or the perception of people about certain places.\nThe drawback is that these data often comes in high volumes, are often ill structured, and often are collected with a different purpose than that of environmental analyses. This means that (pre) processing, analyses, and visualization of such data requires specific skills. This includes, for example skills to create meaningful patterns from the data by applying (spatial) classification and clustering techniques, or applying sentiment and topic analyses techniques on for example social-media data. Knowing how to visualize these often-complex type of data is essential to effectively share and communicate the outcomes of analyses.\nMoreover, making sense of these data and transform it to information useful for design, participation, decision-making and governance processes requires a critical attitude and good knowledge about the quality of the data, as well as critical reflections on the social and political implications of using smart technologies in environmental policy and decision-making. This course will pay ample attention to societal aspects such as citizen engagement in data gathering, ethical questions around big data and automation, and implications of using smart technologies on social and power relation in (urban) environmental policy. \nTo successfully follow this course knowledge about modern data-science concepts and techniques such as treated in Data Science Concepts (INF-xxxxx) or a data science minor is assumed.\nLearning outcomes:\nAfter successful completion of this course students are expected to be able to:\n- understand the specific aspects of applying data-science for the environmental science domains;\n- evaluate the quality and understand the limitations of data-sources from ‘smart technologies’;\n- design procedures to solve an information need using data-science and visualization techniques;\n- extract meaningful patterns/knowledge and synthesize it in an appropriate way such that is can be understood and used within an environmental design or planning process;\n- apply appropriate data visualization techniques to complex environmental data;\n- develop an attitude of responsibility by reflecting on the societal implications of using smart technologies and big data;\n- identify boundaries between practices and develop and demonstrate the competences necessary for crossing these boundaries." . . "Presential"@en . "TRUE" . . "International and eu environmental law"@en . . "6" . "Contents:\nThis course offers an overview of how international and EU law responds to today’s most pressing environmental problems. It shows which unique solutions, and problems, the legal system presents in addressing these environmental problems. We discuss topics such as climate change, marine protection, biodiversity, energy, and the role of human rights. Overarching themes include the interaction and overlap of national and international legal systems in addressing environmental problems, and the role of individuals in these processes. In order to provide students with a solid foundation, fundamentals of national, EU and international (environmental) law are also set out, leading to the development of legal skills and knowledge that students can use beyond the current course.\nLearning outcomes:\nAfter successful completion of this course students are expected to be able to:\n- discuss the main regulatory challenges for environmental law from a global, national and local perspective;\n- identify the key public and private actors, institutions and processes of international and EU environmental law;\n- appraise the different ways in which law can protect, and endanger, the environment;\n- critically compare different regulatory approaches to environmental problems." . . "Presential"@en . "TRUE" . . "Environmental analytical techniques"@en . . "6" . "Contents:\nThe lectures give an introduction into analytical chemistry with emphasis on spectrometry to measure inorganic coumpounds, structure elucidation and chromatography of organic compounds, organic carbon (humus) fractionation and free ion analysis using electrodes and the Donnan membrane technique (DMT). Selection of a particular method is exemplified by real-world problems in air, soil and water chemistry, environmental chemistry, environmental technology, etc. (case-study).\nTutorials related to the lecture topics help improving insight by answering questions and solving assignments (simple calculations).\nIn the practical students determine different chemical forms of compounds (e.g. heavy metals, benzene) in groundwater, surface water, soil, and plant material with a variety of analytical techniques, such as: inductively coupled plasma optical emission spectrometry (ICP-OES), mass spectrometry (MS), gas chromatography and high pressure liquid chromatography (HPLC). The structure of unknown organic constituents is elucidated by means of mass spectroscopy (MS) and nuclear magnetic resonance (NMR). Free ions are analysed using specific metallic electrodes and with a specific separation method (DMT). Organic material is fractionated to determine humic and fulvic acid concentrations using TOC analysis (Total Organic Carbon). The various methods available are compared with respect to their field of application, limits of detection, selectivity, accuracy, precision, throughput and robustness.\nGroups of students (3-4) will work on a case-study reflecting real-life problems. The group has to analyse the problem situation regarding chemical analytical aspects, formulate a proposal for further research and specify the chemical analytical techniques to be used.\nLearning outcomes:\nAfter successful completion of this course students are expected to be able to:\n- demonstrate insight into how to tackle practical chemical analytical problems;\n- demonstrate understanding of the basic theory and relevant parameters in analytical chemistry;\n- apply methods of instrumental chemical analysis to natural materials and (eco)systems;\n- demonstrate awareness of the limitations of the various methods;\n- report about experimental analytical results and draw correct conclusions;\n- discuss chemical analytical aspects relevant for the selection of proper analytical techniques for real-life problem situations." . . "Presential"@en . "TRUE" . . "Interviews and questionnaires: design and analysis"@en . . "6" . "Contents:\nThe course is especially useful for students who plan to use interviews and/or questionnaires as part of their MSc. thesis or PhD dissertation. It is targeted at students of all study programs who would like to learn about the design, data collection and analysis of interview and questionnaire data. Using subject-response data, i.e. ‘asking questions to people’, can happen in many modalities, ranging from qualitative, exploratory and relatively unstructured interviews to quantitative, explanatory and tightly structured self-administered questionnaire. The complexity of choosing the right method, designing your data collection plan, pre-testing your instruments, gathering and analyzing your subject-response data is often underestimated.\nDuring this course students will follow interactive sessions where core principles will be explained and then applied in groups to concrete research projects. In-class discussions and (roleplay) exercises will be used to stimulate effective learning. There will be practicals using Atlas.ti and RStudio to analyse the data. With the help of instructor and peer feedback, students will learn about and practice with the complete cycle of research design, pre-testing, data collection, coding and analysis for both semi-structured interviews and structured questionnaires (with the latter building on the outcomes of the former).\nLearning outcomes:\nAfter successful completion of this course students are expected to be able to:\nwork out and justify a conceptual design (or 'blueprint’) for the interview guide and for the questionnaire;\ndesign a practical interview guide based on the blueprint and following taught design principles;\ndesign a practical questionnaire based on the blueprint and following taught design principles;\npre-test the questionnaire using appropriate interviewing techniques;\ncode and analyze interview data using methods such as inductive and deductive content analysis in Atlas.ti;\nconstruct and test scales for a questionnaire using Cronbach’s alpha and Principal Component Analysis in RStudio;\nconduct meaningful statistical analysis of questionnaire using among others general linear models and confirmatory factor analysis (CFA)." . . "Presential"@en . "TRUE" . . "Empowerment for sustainability"@en . . "6" . "Contents:\nThis course aims to foster a multifaceted understanding of empowerment for sustainability, and to equip you to walk your talk of sustainability. Firstly, it engages you to explore conceptually the complex nature of (un)sustainability and empowerment, and to distinguish paradigms in terms of worldviews and mindsets impacting society and the environment. Secondly, it exposes you to your own agency and it supports you to uncover your potential through which you can contribute to sustainability endeavors within your sphere of influence. Thirdly, it enables you to cultivate qualities and capacities (competencies) for actively participating into the quest for sustainability and navigating its complexities, namely: reflexivity, perspective-taking, (worldview) communication, personal leadership, entrepreneurial mindset, emotional awareness, care and self-sustainability. This course integrates theory, reflexive thinking and actions.\nLearning outcomes:\nAfter successful completion of this course students are expected to be able to:\n- explain and personalize concepts related to (un)sustainability, modernity, post- and trans-modernity paradigms, empowerment and agency related competencies;\n- understand their own agency and own competencies by creating and implementing a personal real life sustainability project of their own choice, within their own sphere of influence;\n- share and discuss their own personal real life sustainability project, related outcomes and learning process with clarity, inspiration and sense of ownership of own project." . . "Presential"@en . "TRUE" . . "Environmental education and learning for sustainability"@en . . "6" . "Contents:\nEducation and learning, citizen participation and whole system innovations are considered important tools in developing people's environmental and sustainability interests, concerns and competences, but also to improve the performance of people, organizations and systems in transitioning towards sustainable living and ecological mindfulness. This course enables students to actively engage with critical issues in designing an appropriate environmental and sustainability education programme or activity, using a variety of learning and community engagement approaches. During the course students explore the emancipatory use of education, learning, communication, multi-stakeholder participation and whole system re-design. The emancipatory capacity-building perspective, as opposed to an instrumental behaviour change-oriented perspective, will be explored, related, challenged and illustrated by practical examples from multiple contexts.\nLearning outcomes:\nAfter successful completion of this course students are expected to be able to:\nidentify new forms of education, learning and capacity-building that can contribute to restoring, regenerating and re-imagining human - nature - environment relationships;\nunderstand the role of education and learning in environmental policy making, sustainability transitions, stimulating awareness, action and a change in lifestyle, connecting people, young and old, with the natural world, and other sustainability-oriented learning initiatives;\ndevelop and critique an environmental/sustainability education strategy or a specific strategy for an environmental innovation/sustainability transition process of their own choice\nactively engage their peers in a sustainability topic through a hands-on educational activity they co-design." . . "Presential"@en . "TRUE" . . "Communicating for sustainability and responsible innovation"@en . . "6" . "Contents:\nWhile sustainability is the most intractable and daunting challenge of our generation, it is less clear how to communicate, engage, empower and use science and innovation responsibly. Communicating for sustainability used to be considered a straight-forward affair: a matter of providing the public with scientific information. Yet, this 'information deficit' model proved elusive. Sustainability came to be recognised as a ‘wicked’ problem with no single solution and with equally legitimate definitions, each shaped by different values and producing different outcomes. In this course we provide students with concepts and methods to explore the challenge of communicating for sustainability and responsible innovation. Across the six lectures we explore the need for reframing environmental communication, for dialogue and co-design, and for transformative, system-wide and integrative approaches. To give the course a practical edge, we deliver six skills sessions that enable students in small groups to develop their own focus group project on a “wicked” sustainability challenge of their own choice recruiting fellow WUR students as participants (from outside the course). Learning and developing skills of focus group design, active listening, small group moderation and analysis we explore how the views, values and expectations of citizens can co-produce new approaches for communicating a 'wicked' sustainability challenge.\nLearning outcomes:\nAfter successful completion of this course students are expected to be able to:\n- explain social science concepts and theories on communication for sustainability;\n- explain social science concepts and theories on responsible research and innovation;\n- apply social science concepts and theories to the course profile;\n- design and carry out a public engagement focus group project;\n- design a communication strategy using the results of empirical research." . . "Presential"@en . "FALSE" . . "Risk communication"@en . . "6" . "Contents:\nThis intensive course provides insight into theoretical and practical-strategic matters of risk communication. Special attention will be given to risk communication in the context of the life sciences issues and life science technologies such as malaria, zoonoses, gene technology, flooding, climate change, novel agro-technologies, and food scares. In our rapidly changing knowledge society, experts and non-experts tend to have different appreciations of science and technology issues. What exactly is the nature of these differences and what are the communicative implications? We will discuss psychological theories regarding risk perception. How do people process complex information regarding particular risks and what is the role of emotions therein? How does media coverage of risks affect the public's sense of anxiety? Attention will also be paid to sociological theories of risk and trust. Is there a general mistrust of science and technology, and can this be explained by a trust or knowledge deficit of the public? Under what conditions are institutions capable of handling and communicating risks? Throughout the course assignments and group work help students to translate theoretical insights to risk communication practices based on their own choice. Students from the bachelor Communication and Life Sciences (BCL) are expected to work on a risk topic for the assignment that is related to their choice of track.\nLearning outcomes:\nAfter successful completion of this course students are expected to be able to:\n- explain the core theoretical concepts in the field of risk communication;\n- identify relevant social processes related to the perception and acceptance of risks;\n- analyse and evaluate current communication practices in the field of risk communication;\n- apply these insights to develop practical suggestions for effective and legitimate forms of risk communication;\n- analyse a case on the topic of a particular risk, report the results, and give a presentation on this study." . . "Presential"@en . "FALSE" . . "Educational design and teaching for sustainability"@en . . "6" . "Contents:\nThis course enables students to understand, design, facilitate and experience sustainability-oriented teaching, learning and capacity-building processes. It is targeted towards students who seek to build their capacities as future sustainability-oriented educators, and who like to like to design and engage with deep learning processes fostering responsibility and transformation in educational contexts (e.g. a school), in communities (e.g. a group of citizens), and in organizations (e.g. a local government).\n\nThe course is envisioned as a ‘laboratory’. It is intended to facilitate students' abilities to integrate the theory and the practice of education, teaching and learning aiming at fostering responsible engagement into sustainability challenges, at the crossroad between science and society. Based on their own aspirations, students design an educational initiative of their own choosing to address a sustainability aspect they care about, and put into action their didactical abilities while considering the socio-cultural characteristics of the context of their initiative. Through this initiative, students gain the tools to design an educational module or trajectory, and to facilitate the cultivation of multiple ways of knowing, being and doing that help responding to sustainability challenges. Depending on the nature of the initiative, students can experiment with a variety of creative methods of teaching, facilitation, and capacity-building in order to encourage mental, emotional, somatic, moral and aesthetic forms of learning. Students, from a variety of disciplines, closely interact throughout the course by working in peer groups supporting each other and exchanging feedback.\nLearning outcomes:\nAfter successful completion of this course students are expected to be able to:\n- understand education and learning theories and how they can be applied for cultivating people's sustainability-oriented understanding, engagement, and transformation;\n- understand the key challenges of teaching, learning, and capacity-building in relation to a wicked problem such as sustainability in formal education settings (e.g. in higher education) and non-formal or informal settings (e.g. in multi-stakeholder community);\n- identify a sustainability challenge they care about and articulate its characteristics;\n-design an educational initiative of own choice (e.g. a module, course, training, blog, etc.) to respond to that identified sustainability challenge;\n- apply a variety of teaching and learning activities and articulate and justify corresponding theoretical underpinnings from the education and learning sciences;\n- support peer learning by providing constructive feedback to the design of the intervention and didactical performance of peers from their own and other disciplines;\n- reflect on their (future) role and purpose as an educational professional and the competences needed to perform this role within the context of sustainability challenges." . . "Presential"@en . "FALSE" . . "Msc research practice knowledge, technology and innovation"@en . . "24" . "Contents:\nThe MSc Research Practice is a research project under supervision of a Wageningen university supervisor that replaces the internship in the programme of the student (only possible for students of MSc programmes that allow students to choose for a Research Practice). The Research Practice should differ from a regular thesis in the following way:\nthe Research Practice has additional learning outcomes related to career preparation and personal development;\nthe Research Practice has additional assessment criteria related to the above mentioned additional learning outcomes.\nLearning outcomes:\nAfter successful completion of the MSc research practice, the student is expected to be able to:\nevaluate career interests and ambitions in relation to the research project and reflect on professional ambitions and capabilities;\ndevelop a research plan, including: a description of the research topic in relation to the wider scientific context; identification of the knowledge gap; formulation of research questions and/or a hypothesis, aims and objectives; an explanation of how you intend to conduct the research (e.g. in terms of a design for the project, data-collection and -analysis methods, research tools);\ncollect, select and process data, using the design for the project, methods and tools described in the research plan;\nanalyse and synthesise the data in order to answer the research questions and/or test the hypothesis;\nformulate answers to the research questions that are supported by the research outcomes; pay attention to potential limitations; critically discuss the outcomes in relation to the wider scientific and societal context;\nreport on the research, both in writing and in oral presentation;\nwork in compliance with academic codes of conduct and with proper management of time and resources;\nmake use of input and feedback for executing the research project and provide feedback to others;\ndefine personal learning goals, which could include domain-specific skills, and reflect on development therein. The student should formulate at least two specific personal learning goals in consultation and agreement with the supervisor.\nActivities:\nresearch proposal and planning: the student prepares by reading literature related to the project, formulating research question/hypothesis and proposing approaches and methods;\ncarrying out the research project: the student executes research activities and document findings and sources carefully;\nfeedback: the student receives and processes feedback while working on the project and provides feedback to other students and staff;\nresearch report: the student writes a comprehensive, consistent and concise report containing all the elements of a full scientific paper in the discipline of the chair group;\nreflection report: the student reflects on the academic skills applied or learned during the Research Practice, the general and personal learning goals that have been achieved (or are still to be achieved) and the contribution of the Research Practice to the student’s career interests and ambitions;\noral presentation: the student presents major research findings to other MSc students and staff members of the Chair Group;\noral defence: the student defends the Research Practice and development of scientific skills and attitude, and places results and conclusions in the wider context of the field of science." . . "Presential"@en . "FALSE" . . "Msc research practice strategic communication"@en . . "24" . "Contents:\nThe MSc Research Practice is a research project under supervision of a Wageningen university supervisor that replaces the internship in the programme of the student (only possible for students of MSc programmes that allow students to choose for a Research Practice). The Research Practice should differ from a regular thesis in the following way:\nthe Research Practice has additional learning outcomes related to career preparation and personal development;\nthe Research Practice has additional assessment criteria related to the above mentioned additional learning outcomes.\nLearning outcomes:\nAfter successful completion of the MSc research practice, the student is expected to be able to:\nevaluate career interests and ambitions in relation to the research project and reflect on professional ambitions and capabilities;\ndevelop a research plan, including: a description of the research topic in relation to the wider scientific context; identification of the knowledge gap; formulation of research questions and/or a hypothesis, aims and objectives; an explanation of how you intend to conduct the research (e.g. in terms of a design for the project, data-collection and -analysis methods, research tools);\ncollect, select and process data, using the design for the project, methods and tools described in the research plan;\nanalyse and synthesise the data in order to answer the