DataBasic: Design Principles, Tools and Activities for Data Literacy Learners

D’Ignazio, Catherine; Bhargava, Rahul

doi:10.15353/joci.v12i3.3280

Cited by 51 publications

(23 citation statements)

References 20 publications

(16 reference statements)

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“…Whilst several definitions of data literacy can be found (e.g. [19,20]), in this article data literacy is defined as follows: "the ability to ask and answer real-world questions from large and small data sets through an inquiry process, with consideration of ethical use of data. It is based on core practical and creative skills, with the ability to extend knowledge of specialist data handling skills according to goals.…”

Section: The Sensei Data Exhibitionmentioning

confidence: 99%

“…The creation stage provides visitors with artwork creation space to reflect a story they want to tell. Craft materials are provided, inspired by the data sculptures approach of [19]. After representation, they write a story card explaining what they have made and why it is interesting (like in museum exhibition), which visitors can add to museum by leaving their sculptures, or by taking a polaroid picture instead, if visitors prefer to keep the sculpture.…”

Section: Stage To Creatementioning

confidence: 99%

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From Data Literacy to Co-design Environmental Monitoring Innovations and Civic Action

Happonen

Wolff

Palacin

2022

Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications

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SENSEI is an environmental monitoring initiative run by Lappeenranta University of Technology (LUT University) and the municipality of Lappeenranta in south-east Finland. The aim was to collaboratively innovate and co-design, develop and deploy civic technologies with local civics to monitor positive and negative issues. These are planned to improve local’s participation to social governance issues in hand. These issues can be e.g. waste related matters like illegal dumping of waste, small vandalism into city properties, alien plant species, but on the other hand nice places to visits too. This publication presents initiatives data literacy facet overview, which is aimed at creating equitable access to information from open data, which in turn is hoped for to increase participants motivation and entrepreneurship like attitude to work with the municipals and the system. This is done by curating environmental datasets to allow participatory sensemaking via exploration, games and reflection, allowing citizens to combine their collective knowledge about the town with the often-complex data. The ultimate aim of this data literacy process is to enhance collective civic actions for the good of the environment, to reduce the resource burden in the municipality level and help citizens to be part of sustainability and environmental monitoring innovation activities. For further research, we suggest follow up studies to consider on similar activities e.g. in specific age groups and to do comparisons on working with different stage holders to pin point most appropriate methods for any specific focus group towards collaborative innovation and co-design of civic technologies deployment.

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Section: The Sensei Data Exhibitionmentioning

confidence: 99%

Section: Stage To Creatementioning

confidence: 99%

From Data Literacy to Co-design Environmental Monitoring Innovations and Civic Action

Happonen

Wolff

Palacin

2022

Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications

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“…From a competency development perspective, data literacy is the ability to understand and work with different kinds of data with the purpose of making data-informed decisions and arguments. For example, D’Ignazio and Bhargava (2016) defined the concept as “the ability to read, work with, analyze and argue with data” (p. 84). Similarly, Mandinach and Gummer (2013) defined data literacy as “the ability to understand and use data effectively to inform decisions” (p. 30).…”

Section: Open Data and Data Literacymentioning

confidence: 99%

“…The need for developing data literacy is often informed by the unprecedented growth in the amount and role of data in our society. Approaches to developing data literacy build on historical disciplines such as statistics and mathematics with a focus on technical and algorithmic aspects of data literacy such as data mining, information design and data visualization (D’Ignazio and Bhargava, 2016). With the exception of some studies (Hautea et al , 2017), current data related skill development efforts are predominantly in postsecondary contexts where a number of universities in Canada and the US established graduate and undergraduate programs in data science.…”

Section: Open Data and Data Literacymentioning

confidence: 99%

How “accessible” is open data?

Gebre

Morales

2020

ILS

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Purpose This paper aims to examine the nature and sufficiency of descriptive information included in open datasets and the nature of comments and questions users write in relation to specific datasets. Open datasets are provided to facilitate civic engagement and government transparency. However, making the data available does not guarantee usage. This paper examined the nature of context-related information provided together with the datasets and identified the challenges users encounter while using the resources. Design/methodology/approach The authors extracted descriptive text provided together with (often at the top of) datasets (N = 216) and the nature of questions and comments users post in relation to the dataset. They then segmented text descriptions and user comments into “idea units” and applied open-coding with constant comparison method. This allowed them to come up with thematic issues that descriptions focus on and the challenges users encounter. Findings Results of the analysis revealed that context-related descriptions are limited and normative. Users are expected to figure out how to use the data. Analysis of user comments/questions revealed four areas of challenge they encounter: organization and accessibility of the data, clarity and completeness, usefulness and accuracy and language (spelling and grammar). Data providers can do more to address these issues. Research limitations/implications The purpose of the study is to understand the nature of open data provision and suggest ways of making open data more accessible to “non expert users”. As such, it is not focused on generalizing about open data provision in various countries as such provision may be different based on jurisdiction. Practical implications The study provides insight about ways of organizing open dataset that the resource can be accessible by the general public. It also provides suggestions about how open data providers could consider users' perspectives including providing continuous support. Originality/value Research on open data often focuses on technological, policy and political perspectives. Arguably, this is the first study on analysis of context-related information in open-datasets. Datasets do not “speak for themselves” because they require context for analysis and interpretation. Understanding the nature of context-related information in open dataset is original idea.

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“…While Hill and colleagues' [38] approach provided a flexible set of skills and tools, it also came with a steep learning curve cost. D'Ignazio and Bhargava [39] approached this problem from a more learning-centered angle. They created a set of learning tools for data literacy, designed to avoid programming explicitly, and targeted data skill acquisition through tailored, single-purpose data tools -DataBasic.…”

Section: Supporting Non-experts Learning and Using Datamentioning

confidence: 99%

Developing a Design Inquiry Method for Data Exploration

Kun¹,

Mulder²,

Kortuem³

2020

IxD&A

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The increasing availability of large-scale datasets such as sensor data or social media data and increasingly accessible data science tools create unique opportunities for design. However, the relationship between data science practices and design methods is still underdeveloped. In this paper, we propose that data exploration activities can be effectively embedded within a broader design inquiry framework and define a new design method, coined Data Exploration for Design, to support methodical designerly data exploration. The design method addresses the novice’s learning curve and supporting developing a data exploration inquiry mindset with procedures and curated tools. The empirical evaluation highlights support for producing exploration outcomes that are worth the additional technical effort. We close the paper by positioning the findings in design methodology literature and motivating data exploration principles for design inquiry.The principles urge to acknowledge biases in data collection, spending time with the data, using visualizations as a means-to-an-end, and designers being part of the data collection.

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DataBasic: Design Principles, Tools and Activities for Data Literacy Learners

Cited by 51 publications

References 20 publications

From Data Literacy to Co-design Environmental Monitoring Innovations and Civic Action

From Data Literacy to Co-design Environmental Monitoring Innovations and Civic Action

How “accessible” is open data?

Developing a Design Inquiry Method for Data Exploration

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