Group Admins
Description
Open Data is produced and used at various levels in research, governance, policy making and civil society. So far though, conversation around its value and significance has tended to occur within an Open Data silo, existing in parallel with other open discussions around Open Educational Resources and Open Access. In our presentation we explore practices which make use of Open Data as OER, with a focus on the the opportunities and challenges inherent in this approach.
For the OECD, “All citizens should have equal opportunities and multiple channels to access information, be consulted and participate. Every reasonable effort should be made to engage with as wide a variety of people as possible.” A central challenge in higher education is to develop skills useful not only at subject/professional level, but which also engage students with real-word problems. The skills needed to participate in democratic discussions can be understood as transversal skills, defined by UNESCO (2015) as “Critical and innovative thinking, inter-personal skills; intra personal skills, and global citizenship”. If one of our goals as educators is to develop these transversal skills in students, towards enabling them to function as citizens, to actively participate in the discourse and debates of society, then we propose that Open Data can play a key role.
Open Data has been understood as key to research, policy and governance development, and also heralded as a force for democratic discourse and participation, but in our view, this is not achieved by opening data alone. By using Open Data in research- and scenario- based learning activities, educators can enhance the information, digital, statistical and data analysis literacies that can empower students, and ultimately citizens and communities. Such pedagogic activities allow students to learn using the same raw materials researchers and policy- makers produce and use.
Drawing from a series of case studies of the use of Open Data as OER, we suggest educators consider the following elements
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Focus: define the research problem and its relation to the environment students.
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Practicality: match technical applications and practices to expected solutions.
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Expectations: set realistic expectations for data analysis.
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Directions: support in finding data portals which contain appropriate information.
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Training: provide training materials for the software students will need to analyse the data.
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Location: use global, local and scientific data which is as granular as possible.
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Modelling: develop model solutions to guide students on the challenges and activities.
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Collaboration: support students to work collaboratively and at multidisciplinary level.
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Communication: support students in communicating their findings to local or wider communities.
Participants
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julieerobinson
joined 8 years, 8 months ago -
Susan Greig
joined 8 years, 8 months ago -
lunderwood
joined 8 years, 8 months ago -
Richard Leeming
joined 8 years, 8 months ago -
ALT
joined 8 years, 9 months ago
