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Wolff, Annika; Wermelinger, Michel and Petre, Marian
(2019).
DOI: https://doi.org/10.1016/j.ijhcs.2019.03.006
Abstract
Data literacy is gaining importance as a general skill that all citizens should possess in an increasingly data-driven society. As such there is interest in how it can be taught in schools. However, the majority of teaching focuses on small, personally collected data which is easier for students to relate to. This does not give the students the breadth of experience they need for dealing with the larger, complex data that is collected at scale and used to drive the intelligent systems that people engage with during work and leisure time. Neither does it prepare them for future jobs, which increasingly require skills for critically querying and deriving insights from data.
This paper addresses this gap by trialling a method for teaching from complex data, collected through a smart city project. The main contribution is to show that existing data principles from the literature can be adapted to design data literacy activities that help pupils understand complex data collected by others and form interesting questions and hypotheses about it. It also demonstrates how smart city ideas and concepts can be brought to life in the classroom.
The Urban Data School study was carried out over two years in three primary and secondary schools in England, using smart city datasets. Three teachers took part, providing access to different age groups, subject areas, and class types. This resulted in four distinctive field studies, with 67 stu- dents aged between 10-14 years, each lasting a few weeks within the two year period. The studies provide evidence that when engaging with data that has not been personally collected, activities designed to give the experience of collecting the data can help in critiquing it.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)
