Space and Global Health/geo-located OER

SDG4: Quality Education - Learning Resouce supports the SDG-Tagging - [1]
SDG3: Good Health and Well-being - Learning Resouce supports the SDG-Tagging - [2]



Capacity building and learning environments incorporate local and regional requirements and constraints (prerequisites of the learner, local and regional, geographical, social, legal, cultural and health related requirements and constraints, among others).[3] A learning environment can be understood as an extension of the usual term task, essentially a work situation as a whole, which should enable and support active discovery and social learning (substantive understanding of a learning environment).[4] Learning trails and geocachings offer the opportunity to combine outdoor learning, health awareness and STEM education.[5]

Objective in Health Domain


Through finding the healthiest path in an outdoor learning trail, health awareness is supposed to be created. SDG3 ist combined with SDG4.

Involved Space Technology


GPS is used within a learning trail or geocaching. GPS is a global satellite navigation system for position determination. GPS requires at least 24 operational satellites orbiting the Earth at an altitude of 20,200 km in a slightly elliptical orbit in the Earth's medium orbit. On six different orbits (all with an angle of 55° with respect to the equatorial plane) there are at least four satellites in equal distances. Through this arrangement of satellite orbits at least four satellite signals can be received from any place on at least four satellite signals can be received.[6] Geocaching is a type of treasure hunt, which is carried out with the help of GPS-enabled devices.[7] The "treasure" is referred to as a "cache" and can be found with the help of coordinates. [8]

Geographical aspect


Capacity building and learning with the aim to increase the risk literacy in the context of health or environmental risks has also a geographical context: health and environmental risks have a spatial distribution and are not in all regions present. Also learning and capacity building materials or environments have a spatial component as they are located in a specific region or place and can be tagged with for example GPS coordinates. A metric can be defined, like the euclidean distance between the learning or capacity resource and the learners, which defines, if a risk is present in a region or if learning resources are available for the learners in their home region.

  • What are applicable metrics beside the euclidean distance ?

Workflow from to Space Technology to Health Domain


Learning Environment: "The path is the true destination"


The students work with the App MathCityMap. The app has been developed in the project MATIS I at Goethe-University in Frankfurt (Germany) and is available for iOS as well as Android mobile devices. The app features geolocation based learning by so called trails, a list of locations in a specific area (e. g. the city center) students have to go to and solve mathematical problems regarding everyday objects at the destination, e. g. sightseeing objects or even – at first glance – unconspicous objects like a bike stand [upload image to wikimedia commons?]. At Saarland University such a trail has been developed in the city of Saarbrücken and on the university campus for students to work on [link to in-App trails?].

Along with solving the exercises given in the trail, the students are requested to create a map of the visited locations. A template with a grid of 5‰ steps of latitude and longitude is provided [is this fine enough?]. In the map locations are inserted as points (nodes) based on their geographical coordinates, which the students can find out with their mobile devices. In addition the students can insert ways between two nodes (edges) that can be of various types: streets to take by car or by bus, bike or foot paths, etc. The types can for example be color coded.

With the described map the students will now try to find what is the best route to take to visit all places mentioned in the trail. Best is hereby not specified at first. The students have to develop a system to put weights (indexes, ranks) on the graphs edges that describe the efficiency of the route (e. g. regarding time or distance) but also the healthiness, sportiness or sustainability when taking a specific route. How this ranking system is developed should be worked out by the students themselves to foster creativity along with the process of constructing.

In the end the students can develop personas with a special focus on health-related conditions such as disabilities or diseases. To the personas one can now suggest personalized routes that take their medical conditions into account. A person in need of a wheelchair might be suggested a barrier free route without stairs while a person that needs to work on their cardio might be suggested long foot paths.

Finding the best path


Finding paths with specific properties has been the object of interest for a long time with its roots going back to Leonhard Euler and the Königsberger Brückenproblem. Today we mostly take it for granted to just set a destination in a navigation system and press a button to get a detailed live description of the route we have to take to get to our workplace, to visit a friend or to go on vacation. However an algorithmic solution of the shortest path problem – however one wants to interpret short – is relatively young. One of the most widely known algorithms, Dijsktra's algorithm, has been developed in 1957[9]. The algorithm can be implemented with students of upper classes in various programming languages and by changing the graphs weights to the system found in the learning environment, also finding specific routes can be implemented by the students.

But there are also possibilities for younger children to experience the automatisation of finding the shortest or best path according to the developed indexing system. Lambert proposes the idea to model the graph with washers and strings[10]. The washers describe the locations, the length of the string relates to the weight of an edge between two locations. By lifting up two locations and tensioning the strings between them, one can see the shortest path between them as the straightened chain of strings connected by several locations (washers). Lifting up just one location yields locally shortest paths to all other locations and by that offers a possibility to make Dijkstra's greedy algorithm for finding shortest paths tangible.

Furthermore, interactive visual aids can help students to find the best path.[11][12]

For more on working on shortest path problems with students see e. g. [13].

Learning Activies

  • Identify the basic principles of geolocated OER and explain the application in the health domain.
  • Explain how global satellite navigation is involved to implement geolocated OER and create educational resources and capacity building with a reference to the current geolocation of a patient of a healthcare facility or for public health staff operating in a specific region.
  • Develop a ranking system to identify healthy and/or sustainable routes (regarding a specific health care topic) between two geolocations.
  • Construct personas regarding specific health-related conditions
  • Explore the concept of Open Badges to be awarded for successful outdoor activities with geo-located OER. How would you combine an Open Badge with a geo-located OER? Start with a one-to-one matching of reward a successful geo-located OER with a single Open Badge.

See also



  1. SDG4 in UN-Guidelines for Use of SDG logo and the 17 SDG icons (2019/05/10) -
  2. SDG3 in UN-Guidelines for Use of SDG logo and the 17 SDG icons (2019/05/10) -
  3. Platz, M., Rieger M. B., Rapp, J., Risch, B., Hartmann, M., & Riedler, B. (2020). Geolocated Learning Environments and Capacity Building for tailored support in the context of an Open Web Index. In Proceedings of the 2nd International Symposium on Open Search Technology, 12-14 October 2020, CERN, Geneva, Switzerland.
  4. Wollring, B. (2008). Zur Kennzeichnung von Lernumgebungen für den Mathematikunterricht in der Grundschule. Kasseler Forschergruppe (Hrsg.). Lernumgebungen auf dem Prüfstand. Bericht 2 der Kasseler Forschergruppe Empirische Bildungsforschung Lehren – Lernen– Literacy (S. 9–26). Kassel: kassel university press GmbH.
  5. Platz, M. & Ritter, S. (2021). Schatzsuche in der Stadt – mathematisches Geocaching. In P. Hauns & W. Schnatterbeck (Hrsg.). Wenn die Hummel wüsste, dass sie nicht fliegen kann. Wissenswertem und Erstaunlichem auf der Spur (S. 149–165). Heidelberg: Regionalkultur.
  6. Frank, M., Richter, P., Roeckerath, C., & Schönbrodt, S. (2018). Wie funktioniert eigentlich GPS? – ein computergestützter Modellierungsworkshop. In Digitale Werkzeuge, Simulationen und mathematisches Modellieren (S. 137-163). Springer Spektrum, Wiesbaden.
  7. Lammersen, H. (2015). Geocaching mit Schülern: Wochenplan, Tagespläne und alle Arbeitsmaterialien für die Projektwoche. Hamburg: AOL-Verlag.
  8. Benz, N. (2018). Geocaching - Komm, wir finden einen Schatz! Mathematik differenziert, 2-2018: 34–40
  9. Haftendorn, Dörte (2016). Mathematik sehen und verstehen : Schlüssel zur Welt (2., erw. Aufl ed.). Berlin [u.a]: Springer Spektrum. ISBN 978-3-662-46613-1. OCLC 952192154. 
  10. Lambert, A. (2015) Mit Dijsktra zum kürzesten Weg. In mathematik lehren (188, 48-49).
  11. Platz, M., & Niehaus, E. (2012) Test-Umgebung für räumliche Entscheidungsunterstützung zur späteren Verwendung in Augmented Reality für mobile Endgeräte. In M. Ludwig, & M. Kleine (Hrsg.), Beiträge zum Mathematikunterricht 2012 (S. 661–664). Münster: WTM-Verlag.
  12. Platz, M., & Niehaus, E. (2013). Augmented Reality und räumliche Entscheidungsunterstützung mit dem Smartphone. In G. Greefrath, F. Käpnick, & M. Stein (Hrsg.), Beiträge zum Mathematikunterricht 2013 (S. 757–760). Münster: WTM-Verlag.
  13. Gritzmann, Peter (2005). Das Geheimnis des kürzesten Weges : ein mathematisches Abenteuer. René Brandenberg (3., überarbeitete. Aufl ed.). Berlin: Springer. ISBN 3-540-27114-7. OCLC 288136216.