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Categories of data handling

This piece is aimed at teachers but should prove useful to anyone looking to learn more about the concepts and processes of data handling.
Five images representing questioning and then the four categories of data handling. Questioning - is represented by a question mark. Collecting - by a pencil and a tally chart. Implementing - by a branching database. Analysing - by a bar chart with a magn

This piece is aimed at teachers but should prove useful to anyone looking to learn more about the concepts and processes of data handling

In order to handle data, you will need to develop several different but related skills. Although a single data handling activity will involve using several of these skills, it is useful to highlight one particular skill at a time.

Researchers Dr Andreas Grillenberger and Dr Ralf Romeike have developed a competency-based model for digital literacy for use in school settings (described in this paper), recognising the importance of this area for all learners. They argue that data literacy is essential for everyone, as the applications that we use everyday collect huge amounts of data, and people who create, control and understand data increasingly possess the power in society.

Grillenberger and Romeike developed a model that would reflect the activities undertaken in schools. They identified four process areas of data literacy. At the National Centre for Computing Education, we have interpreted this model to consider the elements of a data handling activity in the primary classroom, and the key skills associated with each area.

Grillenberger and Romeike’s category of ‘data and information’ In primary school language Key data handling skill
N/A What am I finding out? Questioning
Gathering, modelling, and cleansing What will I need? Collecting
Implementing and optimising How will I get it? Implementing
Analysing, visualising, and interpreting What does it tell me? Analysing
Sharing, archiving, and erasing How will I share it? Presenting

Data handling

There are four main categories of data handling. The first area identified by Grillenberger and Romeike is Gathering, modelling, and cleansing. Pupils working in this area are answering the question, “What will I need?” and the key skill we’ve associated with this area is collecting. You should introduce your learners to different strategies for collecting data, such as a tally chart, putting an object in a jar to represent an answer or using sensors connected to a computer to record live data. The aim is to develop learners’ understanding of data collection processes, so that they become able to make decisions about the best method for a particular task.

Implementing data

Your learners will also need to learn to think about how to structure and store data. Grillenberger and Romeike describe this as implementing and optimising, and we describe the key skill as implementing. To help young learners to develop this skill, you can discuss the headings and structure of a spreadsheet, ask learners to create appropriate labels for data in a database, and help learners to devise questions for a branching database.

The next area is where much of the computing element of data handling occurs: analysing, visualising, and interpreting. At this stage, learners will be answering the question “What does it (the data) tell me?” To help learners develop their data analysis skills, you can teach them how to manipulate data to visualise it in different ways to help them answer questions, such as creating a graph from data in a spreadsheet. You can also ask learners questions about data in existing graphs, charts, and infographics, and encourage them to draw conclusions about data that they have collected and visualised.

The final category from the research is sharing, archiving, and erasing data. In this area, learners will develop the skill of presenting, and will be answering the question “How will I share it?” This follows on from how the learners have visualised the data – while that involved thinking about how to show the data to interpret it in the first place, in this stage learners think about how they can best communicate what they have found out with others. You should provide opportunities for learners to share their work with peers, another class, or a wider audience via a class blog.

In addition to these four categories, we have identified a fifth – the skill of questioning, answering the question, “What am I finding out?” This precedes Grillenberger and Romeike’s categories, as they did not consider it part of the computer science process. They instead considered it as the reason for the data analysis in the first place. However, in the primary classroom, we consider this to be an essential part of the progression in learning – children need to understand that the questions we ask determine the data that we collect. This links in with other areas of the curriculum, from maths investigations to science enquiry. Learning how to ask a good question in one area will support learning in another.


It is important that you give learners the opportunity to gain knowledge and experience in all aspects of data handling but you don’t need to do this all at once. To help engage your learners, you should demonstrate how people apply these skills outside the classroom.

This article is from the free online

Teaching Data and Information to 5- to 11-year-olds

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