About the course
The general objectives of this course are to understand and resolve (1) conceptual problems in the domain of participatory (citizen) science and (2) related problem situations in digital environments for data analysis and reporting for social purposes.
This upskilling short course is thus aligned with:
- DIGCOMP 2.2: Competence area 5. Problem-solving (Competence 5.3 Creatively using digital technologies)
- ESCO v1.1.1. S5.6.0: using digital tools for collaboration, content creation, and problem-solving (http://data.europa.eu/esco/skill/cacc62f3-2df4-4cc3-9d5d-0d014db56bd9)
Course enrolment requirements and entry competencies required for the course:
- Basic understanding of simple digital tools and technologies (e.g. Office, social media).
- Interest in conceptual problems and problem situations in digital environments.
Learning outcomes expected at the level of the course:
- Engage in collaborative processes to support and enhance citizens’ participation in society using digital technologies and platforms.
- Understand that technology has the potential to be used for social purposes (e.g. in support of citizen science activities).
- Identify online platforms that can be used to design, develop and test visualizations of open or proprietary data.
- Design a digital story using Tableau Public.
Module Overview
Projected Timeline:
Dates for live lectures:
Course Features
- Lectures 23
- Quizzes 0
- Duration Lifetime access
- Skill level All levels
- Language English, German, Spanish, Croatian, Dutch
- Students 78
- Assessments Yes
Curriculum
- 5 Sections
- 23 Lessons
- Lifetime
Expand all sectionsCollapse all sections
- Pre Course Survey1
- Part 1: Citizen Science: from a Learner to a “Producer"19
- 2.1A) WHAT IS PARTICIPATORY SCIENCE OR CITIZEN SCIENCE?
- 2.2Benefits of citizen science
- 2.3The citizen science landscape
- 2.4B) What constitutes a good citizen science project?
- 2.5Readiness level toward public engagement
- 2.6The importance of engagement
- 2.7Transdisciplinary research
- 2.8The spatial and temporal scale
- 2.9The amount of data that needs to be analyzed
- 2.10The complexity of the data protocol
- 2.11The available project budget
- 2.12Typologies of citizen science projects
- 2.13C) CRUCIAL DESIGN FACTORS FOR A CITIZEN SCIENCE PROJECT
- 2.14A communication and feedback culture
- 2.15Motivational strategies for participation
- 2.16Mechanisms for ensuring data quality
- 2.17Citizen science platforms for data management
- 2.18Inspire yourself and find more resources
- 2.19Workshop
- Part 2: Visualizing Data for Social Purposes with Tableau PublicLearning resources presented here are taken and adapted from https://www.tableau.com/ It is a step-by-step guide to get you started on your data viz journey.1
- Part 3: Grading and evaluating student work1
- Post Course Survey1
Students List
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marinaLearning0 %
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Karla MaticLearning100 %
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Miriam HoffmannLearning100 %
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Perez Abad EvaLearning100 %
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Nikolina BaradaLearning100 %
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Julian NicolasLearning100 %
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Alberto García GumersindoLearning100 %
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Aleksandra GidionovaLearning100 %
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Adrian MarasovicLearning100 %
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Carla Parra PradoLearning100 %
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Aysu LyutviLearning100 %
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Mirko DelićLearning100 %
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Ivan MarinLearning100 %
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Antonija BuzovLearning100 %
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Gabrijela MaticLearning100 %
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Silvija MaticLearning100 %
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Andelina MihaljevicLearning100 %
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Pardo Menezes LuanaLearning100 %
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Roko Srđan PalčićLearning100 %
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Reeb ElisaLearning100 %
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Toni VujevicLearning100 %
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LenaLearning100 %
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Ivan GluscevicLearning4.35 %
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Ana BasicLearning100 %
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Viktoria ZrnicLearning100 %
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Santos Suarez SamuelLearning100 %
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Suncica KarinLearning100 %
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Dino BakicLearning100 %
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Fernandez de la Iglesia Vicente JavierLearning100 %
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Dora KuzmanicLearning100 %
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Antea JovanovićLearning100 %
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Iva GugicLearning100 %
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Ivo MatulicLearning100 %
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Dinko Mudnic BiskoLearning100 %
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Josip GoretaLearning100 %
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Gomez-landero Lopez DavidLearning100 %
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rianLearning100 %
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Ronald TopalovicLearning100 % -
Josip JurkovicLearning100 %
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Borja TrigoLearning100 %
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Motiramani Motiramani AmanLearning100 %
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Antica TomicLearning100 %
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NataliaGalvezLearning100 %
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Lachhab Haida HafidaLearning100 %
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JorgeLearning100 %
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Mario Quintana FernándezLearning100 %
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Sara BobanacLearning100 %
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Franjo KraljevicLearning100 %
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Ana Pina MarinkovicLearning100 %
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Lucia GordoLearning100 %
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Lizett SamaniegoLearning0 %
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Robin SengezenerLearning100 %
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Pablo Moncada SoriaLearning91.3 %
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John KrewedlLearning100 %
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na042Learning100 %
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Komal SinghLearning100 %
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Tonka ŠimrakLearning100 %
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NoeliaLearning100 %
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YasminaLearning100 %
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Sarah_KaltenbachLearning100 %
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davidxgrossLearning100 %
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Nila.JavanshadLearning100 %
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nessieLearning100 %
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Dominik WunderlichLearning100 %
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Moritz StieberLearning100 %
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cedric.brunsLearning100 %
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Adriana BottLearning100 %
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Marc HornbacherLearning100 %
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CheyenneLearning100 %
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florian_heideckerLearning100 %
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sk368Learning100 %
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Kubilay KelesogluLearning100 %
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Korina Cuk KristoLearning100 %
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Josipa KupresLearning100 %
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mcukusicLearning0 %
and 3 students enrolled.
