How to Start Your Data Literacy Journey

Educators hold a treasure trove of data waiting to be deciphered for the benefit of students. In this data-informed era, the ability to navigate and interpret this information is key and improving your data literacy will allow you to make data-informed decisions that can directly impact student experiences and outcomes.

"A teacher might discover that a student's reading difficulties are based on a vision problem rather than a cognitive issue. A teacher might find out that a student is being bullied, and that is impacting the ability to focus on classroom activities… these are examples of how various sources of data can inform how teachers interact with students and seek remedial steps for cognitive, motivational and other issues."

Data Literacy for Educators: Making it Count in Teacher Preparation and Practice
- Ellen B. Mandinach, Edith S. Gummer

Let's delve into why data literacy is not just a buzzword but a powerful tool that can elevate your teaching game.

What is Data Literacy?

To quote data-expert and former secondary-school teacher Dr Selena Fisk in her book, I'm Not a Numbers Person: "You need to understand the numbers and their meaning to be able to use and engage with them effectively." Fisk defines good data literacy as understanding what the numbers mean and the context around them, and having (at least) a basic understanding of how they are calculated. Data literacy is not just a fancy skill; it's a tool for making informed decisions. By unravelling the story of student learning, educators can confidently communicate and justify their decisions to students, parents, and colleagues. It's about transforming intuition-based decisions into data-backed strategies.

Okay, but where to start? Data literacy is a broad topic and data analytics is an entire field of study in its own right, and getting started on your data-literacy journey can be a daunting endeavour. Following Dr Fisk’s guide from her book, we'll break down core aspects of data literacy into bite-sized pieces to get you started.

Before you approach the data, you have to understand the different types of data that you will encounter, these can be categorised into two groups: quantitative and qualitative data. We'll dive into these deeper, but if you're new to data these terms can be confusing. In the most simple terms, quantitative data measures quantity, and qualitative data measures the quality of a subject. Now let's explore what this means for data literate educators.

Understanding Quantitative Data

Quantitative data is what likely first comes to mind when someone says "data"; it's the numbers. The number of students in a subject, assessment scores, attendance records, etc. Collecting quantitative data is relatively easy, but you can end up with a lot, sometimes too much to know where to start! Collaborating with your data-savvy colleagues is an excellent idea to learn which data sets are important or valuable when you're starting. Being numbers-based, quantitative data is easily visualised in graphs and charts, which can give you a great top-level view of student performance, for example.

Deciphering Qualitative Data

The other type of data type you'll need to understand is Qualitative data, which, as put by Dr Fisk, is 'not based on numbers, and you can't easily and definitively summarise the information and build graphs and charts based on it'.

Qualitative information such as well-being, behaviour, and attitude are critically important metrics for understanding where a student is at compared to their cohort, as one example. Qualitative data can come in many forms, the main ones to note being ordinal, categorical, and text responses. Ordinal data has an intrinsic order and a hierarchy such as ranking "strongly agree to strongly disagree". Categorical data fits into categories without hierarchy, such as extra-curricular activities available. Finally, text responses are data such as student feedback, which often can't be categorised as ordinal or categorical. Qualitative data can provide useful context to your quantitative data. However, it is harder to visualise and can take significantly longer to analyse and use than numbers.

We have our different types of data, but what do we do with them?

Three Sources is Better than One.

You wouldn't trust that a restaurant was a good choice if it only had one review, even if it was a 5-star rating, and likewise, you shouldn't rely on a single data set to inform your decision-making.

Triangulating your data should be included in your analysis whenever possible. To re-quote researcher Veronica Thurmond from Dr Fisk's book,

'Triangulation means consulting three or more data sources to identify trends and guide your decision-making, rather than just relying on a single data source'.

In her book, Dr Fisk gives three great reasons why you should consult three or more points of data wherever possible:

• ability to see trends across data sets, meaning you won't jump to a conclusion based on a single
• greater likelihood of trends being reliable.
• you can trust most of the data and ignore outliers – without additional pieces, you may not have been aware of outliers in the first place.

Be aware of Correlation versus Causation.

When building our data literacy, there is the big hope that putting data sets together in a graph will reveal the answers to questions that we have been missing. But we must be careful that the story we are getting from our data is accurate and not coincidental.

For example, we may measure a student's homework completion against days of the week and find that they are consistently not completing homework on a Tuesday night. Would this mean that this particular day of the week is the cause of incomplete homework? Probably not, but we could assume that something else may be occurring on Tuesdays which is affecting our student’s homework output. In this example of ‘incomplete homework’, Tuesday is correlating data, and the mystery factor happening on Tuesday would be causal data. The Australian Bureau of Statistics explains the difference well:

"Correlation is a statistical measure that describes the size and direction of a relationship between two or more variables. Causation indicates that one event is the result of the occurrence of the other event. This is also referred to as cause and effect".

Choosing the right data for you.

Schools can capture so much data, but how do we decide which data is going to be useful? Dr Selena Fisk provides a comprehensive step-by-step guide that you will be able to follow, no matter your level of data literacy. You can find the full list in her book, I'm Not a Numbers Person, and we'll summarise it here to get you started:

1. Brainstorm a list of all the data you have available; consider qualitative and quantitative data.
2. Think about the types of data that are necessary for you to do your job well and know you are making an impact.
3. Rank the highlighted data types in order of importance from highest to lowest.
4. When you have your list, explain the use/necessity of each data type. How do you use it, why is it important, what can you do to change it, etc.
5. Consult a colleague. Share your list with them and the process you have gone through to get there, listen to their suggestions and make changes to your list if you need to.
6. Use this list to focus your work on data literacy. Do you know what each metric means and how it's calculated? Do you understand the context of your results, and do you know what results you are aiming for?

Different Levels of Data Literacy

Becoming data literate is a massive undertaking when you get started, but it is an essential skill for every educator engaged with school data. There will be faculty who are more data literate than you are right now, and those whom you surpass in data literacy.

Every data user will develop from being an Unconscious User who is unaware of the data that surrounds them, to a Reflective User who actively participates in interpreting data to produce change. You can read more about this journey and how to assess the data literacy at your school in our blog.

Harnessing the Power of Data

In the ever-evolving landscape of education, the role of data literacy becomes increasingly paramount. With schools capturing an array of data, ranging from student grades to well-being and extra curricular activities, educators are presented with unprecedented opportunities to make more informed decisions.

While data-informed decision-making cannot replace the valuable soft skills cultivated through years of teaching, it does provide a robust foundation for justifying decisions and fostering positive changes in the classroom.

Data literacy is your tool for understanding students, classes, and curriculum at a deeper level. It helps you ask the right questions, making numbers less intimidating and more user-friendly as you become more data literate. To assist with harnessing the power of data at your school, implementing a dedicated solution (such as TrackOne Studio's Learning Analytics Suite) to bridge the data literacy gap can have lasting benefits.