TY - BOOK

T1 - Enabling educators to teach children about data with physical computing

AU - Underwood, Lorraine

PY - 2025

Y1 - 2025

N2 - Data has become the foundation of our digital world. We create and consume mass amounts of data in our everyday lives and both this creation and consumption is increasing with the popular growth of topics such as machine learning and artificial intelligence. These growing areas rely on vast amounts of data. How reliable and accurate an artificial intelligence is depends on not only the type of machine learning algorithm it uses, but the data it consumes. Data is used to justify government policy, commercial and environmental decisions that affect the world we live in. Data can have a real impact on our lives. Society needs to have a better understanding of what data is, how it is collected, stored and how it can be analysed and visualised.Many educators can feel overwhelmed and underprepared trying to add new topics into the already full curriculum. Combining the teaching of data with an existing, engaging and creative topic such as physical computing could be the answer to adding more and fulfilling existing data science education in the curriculum.This thesis commences with a further explanation of why we need better data science education in schools and how physical computing can support educators. The background section delves further into both areas giving examples of tools and methods. A related work section depicts the BBC micro:bit as a potential tool for data collection in schools. It analyses current research with the micro:bit before the next section details a case study of a project using the micro:bit as a data collection tool in 20 schools. These findings inform the design of a set of tools to support educators to teach data science. The tool is detailed and evaluated in the final sections of this thesis.The contributions of this these include (1) an understanding of how physical computing can become engaging for primary school aged children in the context of data science learning, (2) a new approach for working with teachers in a technology design process and (3) the design process and set of requirements synthesised from research and user interactions. The ultimate aim is to guide future research toward designing pragmatic, engaging and educationally sound physical computing tools to support educators to teach data science.

AB - Data has become the foundation of our digital world. We create and consume mass amounts of data in our everyday lives and both this creation and consumption is increasing with the popular growth of topics such as machine learning and artificial intelligence. These growing areas rely on vast amounts of data. How reliable and accurate an artificial intelligence is depends on not only the type of machine learning algorithm it uses, but the data it consumes. Data is used to justify government policy, commercial and environmental decisions that affect the world we live in. Data can have a real impact on our lives. Society needs to have a better understanding of what data is, how it is collected, stored and how it can be analysed and visualised.Many educators can feel overwhelmed and underprepared trying to add new topics into the already full curriculum. Combining the teaching of data with an existing, engaging and creative topic such as physical computing could be the answer to adding more and fulfilling existing data science education in the curriculum.This thesis commences with a further explanation of why we need better data science education in schools and how physical computing can support educators. The background section delves further into both areas giving examples of tools and methods. A related work section depicts the BBC micro:bit as a potential tool for data collection in schools. It analyses current research with the micro:bit before the next section details a case study of a project using the micro:bit as a data collection tool in 20 schools. These findings inform the design of a set of tools to support educators to teach data science. The tool is detailed and evaluated in the final sections of this thesis.The contributions of this these include (1) an understanding of how physical computing can become engaging for primary school aged children in the context of data science learning, (2) a new approach for working with teachers in a technology design process and (3) the design process and set of requirements synthesised from research and user interactions. The ultimate aim is to guide future research toward designing pragmatic, engaging and educationally sound physical computing tools to support educators to teach data science.

U2 - 10.17635/lancaster/thesis/2873

DO - 10.17635/lancaster/thesis/2873

M3 - Doctoral Thesis

PB - Lancaster University

ER -