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Atenas, Javiera; Havemann, Leo (2018)
Publisher: Open Knowledge, Open Education Working Group
Types: Book
Subjects: its, Open Data;
This collection presents the stories of our contributors’ experiences and insights, in order to demonstrate the enormous potential for openly-licensed and accessible datasets (Open Data) to be used as Open Educational Resources (OER). Open Data is an umbrella term describing openly-licensed, interoperable, and reusable datasets which have been created and made available to the public by national or local governments, academic researchers, or other organisations. These datasets can be accessed, used and shared without restrictions other than attribution of the intellectual property of their creators.While there are various de nitions of OER, these are generally understood as openly-licensed digital resources that can be used in teaching and learning. Open Data has been highlighted as a key to information transparency and scienti c advancement. Students who are exposed to the use of Open Data have access to the same raw materials that scientists and policy makers use. This enables them to engage with real problems at both local and global levels. Educators who make use of Open Data in teaching and learning encourage students to think as researchers, as journalists, as scientists, and as policy makers and activists. They also provide a meaningful context for gaining experience in research work ows and processes, as well as learning good practices in data management, analysis and reporting. The pedagogic deployment of Open Data as OER thus supports the development of critical, analytical, collaborative and citizenship skills, and has enormous potential to generate new knowledge.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • AWW Video: http://alandix.com/academic/talks/AWW-sensing-the-miles-2014/
    • AWW (2013-2015). Alan Walks Wales Report, http://alanwalks.wales/report/
    • Dix, A. (2013). The Walk: exploring the technical and social margins. Keynote, APCHI 2013 / India HCI 2013, Bangalore India, 27th September 2013. Retrieved from http://www.hcibook.com/alan/talks/APCHI-2013/
    • Dix, A. (2014). Alan Walks Wales: Sensing the Miles. Video Presentation, 'Enhancing SelfReflection with Wearable Sensors', workshop at mobileHCI 2014 Toronto, 23rd September 2014. Retrieved from http://www.alandix.com/academic/talks/AWW-sensing-the-miles-2014/
    • Dix, A. (2015). More than one way to flip a class: learning analytics for mixed models of learning. APT 2015, Greenwich, 7th July 2015.Retrieved from http://www.hcibook.com/alan/papers/apt2015-more-than-one-way/
    • Guardian (2010)Deficit, national debt and government borrowing - how has it changed since 1946?Guardian Data Blog 19th Oct. 2010. Retrieved from http://www.guardian.co.uk/news/datablog/2010/oct/18/deficit-debt-government-borrowingdata.
    • Kolb, D. (2015).Walking Wales : The Data Challenge. Bachelor Project, University Konstanz,
    • Morgan, A., Dix, A., Phillips, M. and House, C. (2014). Blue sky thinking meets green field usability: can mobile internet software engineering bridge the rural divide? . Local Economy, September-November 2014. 29(6-7):750-761. DOI: 10.1177/0269094214548399
    • Simm, W., Ferrario, M., Friday, A., Newman, P., Forshaw, S., Hazas, M., and Dix, A. (2015). Tiree Energy Pulse: Exploring Renewable Energy Forecasts on the Edge of the Grid. CHI'2015, Seoul, S. Korea, April 2015. ACM pp.1965-1974. DOI: 10.1145/2702123.2702285
    • Newman, A., Newman, A., Kowalczyk, P., Newman, A., Richardson, J., & Coley, A. (2015). Defra digital. Defradigital.blog.gov.uk. Retrieved from https://defradigital.blog.gov.uk/.
  • Inferred research data

    The results below are discovered through our pilot algorithms. Let us know how we are doing!

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