Exploring design principles for data literacy activities to support children’s inquiries from complex data
•Interactive data visualisations can support school children between the ages of 10–18 in asking valid questions from data.•Curated snapshots of part of a small part of dataset can help school children understand how to frame questions around the extended dataset, along dimensions such as space and...
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Published in | International journal of human-computer studies Vol. 129; pp. 41 - 54 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Elsevier Ltd
01.09.2019
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Subjects | |
Online Access | Get full text |
ISSN | 1071-5819 1095-9300 |
DOI | 10.1016/j.ijhcs.2019.03.006 |
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Abstract | •Interactive data visualisations can support school children between the ages of 10–18 in asking valid questions from data.•Curated snapshots of part of a small part of dataset can help school children understand how to frame questions around the extended dataset, along dimensions such as space and time to explore more and more of.•A guided data inquiry can help lead to a more open data inquiry.•The learning of data skills lends itself to cross-curricular learning and can begin with primary school students.•There are a number of design principles that may help in developing new applications to engage children with complex data whilst developing new data literacies.
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 students 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. |
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AbstractList | •Interactive data visualisations can support school children between the ages of 10–18 in asking valid questions from data.•Curated snapshots of part of a small part of dataset can help school children understand how to frame questions around the extended dataset, along dimensions such as space and time to explore more and more of.•A guided data inquiry can help lead to a more open data inquiry.•The learning of data skills lends itself to cross-curricular learning and can begin with primary school students.•There are a number of design principles that may help in developing new applications to engage children with complex data whilst developing new data literacies.
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 students 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. |
Author | Petre, Marian Wermelinger, Michel Wolff, Annika |
Author_xml | – sequence: 1 givenname: Annika surname: Wolff fullname: Wolff, Annika email: Annika.wolff@open.ac.uk, Annika.Wolff@lut.fi organization: School of Computing and Communications, The Open University, Milton Keynes MK7 6AA, United Kingdom – sequence: 2 givenname: Michel orcidid: 0000-0002-6467-3293 surname: Wermelinger fullname: Wermelinger, Michel organization: School of Computing and Communications, The Open University, Milton Keynes MK7 6AA, United Kingdom – sequence: 3 givenname: Marian orcidid: 0000-0001-9052-3881 surname: Petre fullname: Petre, Marian organization: School of Computing and Communications, The Open University, Milton Keynes MK7 6AA, United Kingdom |
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Cites_doi | 10.1075/idj.23.1.03dig 10.1109/TLT.2016.2597142 10.1080/10382049908667591 10.2139/ssrn.2445621 10.2139/ssrn.2508051 10.1007/s10758-014-9235-3 10.1080/09500690600621035 10.2505/4/jcst15_045_01_84 10.1080/10580530.2012.716740 10.1002/pra2.2017.14505401004 |
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Snippet | •Interactive data visualisations can support school children between the ages of 10–18 in asking valid questions from data.•Curated snapshots of part of a... |
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SubjectTerms | Data literacy Human-data interaction Open data Smart city |
Title | Exploring design principles for data literacy activities to support children’s inquiries from complex data |
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