Geographical data are increasingly available for agriculture and environment applications, as shown by the ever growing use of satellite imagery, expanding use of drones for image capture in agriculture and environment management and ubiquity of GPS technology in modern data acquisition tools. Therefore, professionals in agriculture and environment, among others, need to overcome their current limitations in extracting and exploring those important and frequently freely available resources. Professionals might also have access to large sets of spatial information through their own institutions (either public or private) without having the necessary knowledge and skills to be able to process it. To be able to address those challenges, the user does not absolutely need to be an expert in GIS or Statistics, but needs to have a sufficient background to address the problems in hand. This projects aim at improving the skills of those professionals in that respect. It will help them to use available open source tools to extract and analyze spatial data which are relevant for their professional activity. This project is developed by three partners : the University of Lisboa, which have a strong background on teaching courses on GIS and spatial data analysis with R software, SupAgro Montpellier, which has skills on Bayesian statistics, an experience in the creation of mooc and offer technical and logistic supports to the project (including a video studio), and the University of Liege, which has experience in the organization and management of a master degree in applied statistics and has a dedicated higher education pedagogical department (IFRES) which can provide the needed support during the construction and evaluation of the learning modules. Year one will be dedicated to the creation of the training material. In addition to the online communication tools, the transnational meetings will be the occasion to exchange practices , meet local stakeholders and integrate their constraints into the project. During year 2, the created cursus will be tested on a panel of selected students originating from the 3 partners. The efficiency of the chosen learning techniques (MOOC, blended learning) will be monitored and analyzed to fine tune the learning activities of year 3. Learning activities of year 3, corresponding to 6 ECTS of intensive courses on spatial data analysis with open tools will be held in Belgium, but transferable in partners' institutions after the project. The activities will take form of an introductive MOOC followed by blended learning activities, with a maximum of two weeks of face to face activities. During year 3, the results of the monitoring of the learning activities of the project will be proposed for a communication in a pedagogical conference, possibly one of the International Academy of Technology, Education and Development (IATED), INTED or EDULEARN. Promotion of the project will be conducted through the creation of a website, a video teaser, a flyer and through the organisation of a local seminar in each partner's institution, and one international seminar on year 2 in Belgium. For the target groups and stakeholders (entreprises active in life sciences needing expertise in spatial data analysis), the project will fill a gap in the training offer, permitting to acquire key competences in spatial data analysis in a organisational design especially tailored for their needs and constraints and therefore increasing competences in applied statistics for spatial data analysis on the job market and the partial filling of the high demand for spatial data analyst workers . Apart from the creation of the learning modules themselves, we expect creating emulation between partners of the project with other organisations active in data analysis and innovative pedagogy, and using the created contacts as well as the experience gathered during the project in order to improve the learning offer. The expected impact is a greater experience in transnational teaching, to explore the benefits and constraints of such programs. We also expect to create a worthy emulation on innovative teaching methods by exchange of our mutual experience, acquired before and during the project and use the project as a same scale laboratory of learning practices, which will be used to test the long term feasibility of a greater scale transnational program, e.a. European Master Degree.
Geographic data are increasingly readily available for agricultural and environmental applications, as evidenced by the increasing use of satellite imagery and drones for image capture in agriculture and environmental management and the ubiquity of GPS technology in modern data collection tools. Professionals may also have access to vast amounts of spatial information through their own institutions (public or private) without having the necessary knowledge and skills to process it. Therefore, agriculture and environmental professionals, among others, must be able to acquire these new skills to extract and explore these important resources in current decision-making processes. To be able to meet these challenges, the user does not absolutely need to be an expert in GIS or statistics, but must have sufficient experience to solve common practical problems. The OpenSpat project aims to improve the skills of these professionals in this regard. It helps them use the open source tools available to extract and analyze spatial data in a way that is relevant to their professional activity. This project is being developed by three partners: the University of Lisbon, which has solid experience in teaching courses on GIS and spatial data analysis with R software, SupAgro Montpellier, which has skills in statistical modelling, experience in the creation of MOOC and has technical and logistical support for the project (including a video studio), and the University of Liège, which has experience in the organisation and management of master's degree courses in applied statistics and which has a department dedicated to higher education (IFRES) that can provide the necessary support during the construction and evaluation of the training modules. The first year was devoted to the creation of training materials. The many exchanges, whether via online communication tools or transnational meetings, made it possible to share pedagogical practices, meet local actors and integrate their constraints into the project, determine the final content of the training and the tools implemented. During the second year, the curriculum thus created was tested on a panel of students selected from among the 3 partners. The effectiveness of the selected learning techniques (video clips, blended learning) was evaluated and analyzed to refine the learning activities of the third year. The learning activities in year 3, corresponding to 6 ECTS of intensive courses on spatial data analysis with open tools, took place in Belgium, but will be transferable to the partners' institutions after the project. The activities take the form of an introductory MOOC followed by blended learning activities, with a maximum of two weeks of face-to-face activities, and are based on teaching materials created during the project: syllabus, presentation materials, data sets, exercises and computer scripts. In the second year, the first results of the follow-up of the project's learning activities were presented during the UseR2017 conference in Brussels, thus ensuring wide international publicity for the project. For target groups and stakeholders (public or private life sciences companies needing expertise in spatial data analysis), the project fills a gap in the training offer, allowing them to acquire key competences in spatial data analysis in an organisational design specially adapted to their needs and constraints and thus to increase the skills in applied statistics for spatial data analysis on the labour market and the high demand for spatial data analysis workers. In addition to the creation of the learning modules themselves and their availability via the European Valor/PRP platform, the project has enabled partner institutions to come closer together and new Erasmus+ agreements to be signed, aimed at ensuring the sustainability of the education offer thus created. At the end of the project, the training module based on the materials created during the project will be delivered each year alternately in each partner institution, starting with the University of Lisbon in 2019. The project also created an emulation between the project partners in the fields of data analysis and innovative pedagogy, which allowed the dissemination of new practices between partner institutions, such as new tools for active pedagogy, sharing, script editing, etc. Thanks to the experience in transnational education, the partners involved are now studying the long-term feasibility of a larger-scale transnational programme, such as a European Master's degree.