Time to complete: 20 minutes
What will this topic cover?
This topic forms part of a wider learning pathway and is designed to help you explore fundamental digital skills and review how you can use them to enhance your daily working practices and approaches. This learning topic, within the Intro to Data literacy pathway, introduces you to the concept of data literacy and using key systems within the University.
This topic will focus on explaining what data literacy is and why it is important with in the higher education context. It will help you understand basic terminologies and give you an overview of the different types of data that are commonly used as well as an overview of common sources.
By the end of this topic, you will be able to:
Discuss the importance of data literacy and reflect on its impact within your area
Identify and understand key data terminology
Understand the wide range of sources that data often comes from
How to use this topic page
This topic page is split up into different sections. Each section has a step and an activity to complete. These include scenarios and links off to instructions to try elements for yourself. Each learning unit also has a reflective section to think about how this will be used within your own practice.
Step 1: Understanding Data Literacy
What is Data Literacy?
Data literacy refers to the ability to read, understand, create, and communicate data as information. In your role at the University, it may involve several key skills:
- Understanding data: Knowing the context and the significance of the data, including any sources and any limitations that data may have.
- Creating data: Collecting, storing and organising data so that it can be interpreted in a meaningful way.
- Reading Data: being able to interpret graphs, charts, and tables to understand what the data is showing.
- Communicating Data: Presenting data clearly and effectively using visualisations, reports and analysing the impact of the data to give insights to potential changes or positive ideas.
Activity
Try it yourself
Think through your current role at the University and reflect on the following questions.
- Where do you currently use data within your role?
- How effective is your current way of communicating or sharing data insights?
- What tools/approaches do you use?
- Are you a data holder who works closely with creating and storing data, if so, what wider elements do you consider when creating data?
Step 2: How we use Data Literacy in Higher Education
How is data literacy applied in Higher Education?
At the University we have multiple sources of data which we use for a wide variety of reasons. This data is crucial to our development and by upskilling our Data literacy skills will enable ouselves to progress and support our development as a university.
Enhanced Decision-Making:
At the University we need to make a lot of different choices, and data can help inform our decisions. These data-informed decisions help us to make choices that will benefit student outcomes, academic performance, university direction, and financial breakdowns.
Smarter Working:
Data can help us analyse how well we, or our processes, are performing and if there are gaps within our own approaches. By analysing the efficiency of our processes and approaches, we are able to make a better, smoother experience for all involved, including staff who run the process.
Example: Analysing student feedback to improve services. By collecting and analysing feedback from student surveys, service teams can identify areas for improvement, leading to improved student satisfaction and retention.
Improving the student experience:
Using data effectively, especially student experience data, enables staff to transform their approaches, outcomes and data-driven improvements to enhance the student journey. Leveraging our internal student data can help highlight trends, patterns, and potential gaps to alter our teaching & learning approaches in line with needs and personalised requirements.
Future-proofing:
With fast developments across the higher education sector, we need to use data to inform our future strategies, plans and goals to ensure that we are able to utilise all our resources effectively. By highlighting the use of our own data and having a more informed view of the data we have, will support our future developments as a University.
Activity
Try it yourself
The examples below gives some context behind the idea of data literacy and some of the reasons why it is used. When looking at the following examples, it may be useful to think about this in your own context.
- Do you use any of these elements within your area?
- How well do you feel you and your team understand or use data?
- Are there wider examples here that may be something to consider within your area?
Step 3: Types of Data
Which types of data do I need to know about?
When collecting data, there are two main types that we need to explore. These are useful elements to know in order to decide what type of data you want to collect and the methods that can be used to gather them. The two main types are:
Quantitative Data:
Quantitative data is usually data that is based around numbers. This means it can be measured and quantified. This type of data is useful for:
- Objectivity: Since the measurements are precise and fact-based, it means that facts can be verified and quantified.
- Comparability: Data from two different sets or time periods can easily be compared to see increases or changes, as long as the data asks for the same information.
- Predictable: With multiple data sets, always measured on the same scale, it can make it easier to predict patterns and trends.
Example: Student grades, attendance records, and enrolment numbers.
Qualitative Data:
Qualitative data is useful for understanding motivations, reasons and opinions and is descriptive data that provides insights into qualities and characteristics. It is often used to help provide greater depth to quantitative findings. This type of data is useful for:
- Detailed feedback: This can help give detailed insights into potentially complex issues using a mixture of closed and open questions.
- Contextual understanding: This can help to give wider context to previously gathered quantitative or number-based data.
- Flexibility: The responses for these questions can change at key points throughout the year or via a group, so it can be quite flexible in the data that you receive.
Example: Student feedback, interview transcripts, and open-ended survey responses.
Activity
Scenario
Look at this scenario below and think about the types of data that you would expect to use within this approach.
Karen wants to find out more about how students and staff are using a service within the University. Karen has a very clear outline of the information that they need to gather from the students about which parts of the service are they using and why, as well as if there is any room for improvement.
What types of data would you expect Karen to gather?
Karen seems to have a need for both quantitative and qualitative data within her survey as they are asking for fixed data, i.e. which services did you use? Since this is coming from a fixed list of services, a quantitative approach makes sense. However, since they are also asking for opinions, they may also need to use a qualitative approach. They will have to think carefully for their qualitative questions to ensure the answers are useful and not too varied in order to analyse the data effectively. Quite often when doing surveys, a mixture of both data types is needed to support a full analysis.
Step 4: Common Data Sources
What are some common sources of data in Higher Education?
Higher education institutions can leverage various data sources to gather valuable information. These sources help in making informed decisions to enhance academic programmes, campus facilities, and overall student experience.
Surveys
Surveys use tools such as Microsoft Forms to gather data from a specific group of people by asking targeted questions. Quite often, these have qualitative and quantitative angles to them to enable richer data analysis.
Example: Conducting student satisfaction surveys to gather feedback on academic programmes and campus facilities. These surveys provide direct insights from students, helping institutions understand their needs and areas for improvement.
Databases
Databases are organised collections of information that can be accessed, managed, and updated to help support a wide range of information.
Example: Using institutional databases to access student records, financial data, and academic performance metrics. These databases offer a comprehensive view of student progress and institutional efficiency, aiding in strategic planning and resource allocation.
Institutional Records
The University has its own systems in place (OneUni, Dashboards etc.) to keep track of student records and requirements at the University. These internal records systems are important to manage historical data and can provide evidence for future developments.
As an institution, we do have to occasionally report to external companies, such as Higher Education Statistics Agency (HESA), to submit and collate data which is open to the public. We also can look at wider data sets which are open access, for example from the government.
Example: We use data from HESA to compare institutional performance against national trends.
Activity
Scenario
Look at this scenario and consider the question below.
A university department wants to improve its student-facing services based on feedback from students. By leveraging the data sources mentioned in Step 4, the department can identify key areas for enhancement and implement targeted improvements to better meet student needs.
Which data sources can you think of across the University that may help or can you think of a way to gather data to gain this information?
To improve student-facing services, the university department can gather feedback through student surveys, course evaluations, focus groups, and social media monitoring. They can also analyse data from student support services, academic performance, and library usage. By combining these insights, the department can identify key areas for enhancement and implement targeted improvements to better meet student needs.
Step 5: Reflection
What have I discovered from this learning topic?
This step is designed to help you think about what you have learned and how this applies to your own practice and context. This steps activity will ask you some questions to help you with this reflection.
Activity
Reflect
Use the following questions to help you think about your own practice.
- What data do you frequently access within your own area?
- What type of data is it and has this page made you think of potential wider data streams you can use to gather this information?
- What elements of data do you think would be useful to look at in more detail within your area?