How can we use data to improve teaching and learning?

We want to know how we can use data-driven approaches to help refine learning and teaching practice.

As part of our new co-design consultation, we've identified six potential challenges that face people working in education and research.

About this challenge

With both government and students focusing on value for money and a quality student experience, it is essential that universities and colleges are directing their resources appropriately to deliver the best learning experiences to students.

Read more about this challenge

Universities will need to draw on the vast array of data and information available not just to demonstrate the quality of their teaching for the TEF, but to examine what makes the most difference to students’ learning, employability and overall satisfaction with their experience, in order to continuously improve their offer.

Universities already analyse key data sets such as student satisfaction scores, module feedback, patterns in assignment marks, final grade and destination to identify areas for improvement. A more detailed picture of how well learning activities and teaching strategies are working on a given module can be obtained by comparing these traditional measures against the kind of activity data collected for learning analytics.

Giving course teams access to this kind of information would enable them to improve the design and delivery of their courses year on year and track the impact of changes. If there is sector consensus on its usefulness, measures derived from this rich data could become part of an institutional submission for the TEF, potentially saving time and money.

Similarly using this approach across FE could support curriculum teams in developing their courses to meet the needs of both learners and employers. It could increase learners’ ability to progress into higher education or sustainable employment, allow colleges to analyse impact and apply positive changes across faculties.

At a faculty, institutional or even sector level, there is the potential to analyse this rich picture of engagement and attainment against emerging measures of learning gain and teaching intensity. Might this lead to an improved data-driven understanding of the teaching strategies which most improve learning outcomes for students? This would be particularly useful to help universities, colleges and other providers analyse where technology can most effectively enhance the learning experience, and how resource-intensive face-to-face time can best be used.

There are also opportunities for smart data-driven curriculum development, drawing on national and international datasets to identify gaps in the local or UK-wide curriculum offering that individual universities can exploit. By identifying employment trends and associated high-level skills and provision gaps, universities can optimise their curriculum offer for maximum graduate employability, and thereby drive increased recruitment.

Questions for you to consider

How are you currently using data to improve learning, teaching and student outcomes?
What would be the key questions about learning and teaching that you would like to see explored through data-driven approaches?
What data is needed in FE to better understand which aspects of course design and teaching lead to higher success rates for their learners?
How can a data-driven approach lead to improved quality and greater understanding of higher education, without risking losing its richness and diversity?
Should the UK HE offer be more strongly shaped by higher-level skills needs in the national and international economies?

This challenge resulted in engagement from the highest number of individuals, so thank you to all who commented, joined in the tweetchats, blogged, or attended the webinars. Some broad themes emerged from the discussion:

Evidence-informed, not data driven: in most cases, decisions should be made by humans, drawing on evidence derived from quantitative and qualitative data, rather than using algorithmic decision making or uncritical acceptance of data
Ethics: as with learning analytics, the use of data to improve learning and teaching needs to be done within a clearly ethical framework, to avoid unintended consequences which reduce the social responsibility or diversity of post-16 education
There are many useful data points that can inform course teams, both for in-year interventions such as identifying topics which need to be revisited, and for periodic review of the programme or module design to help learners to succeed
Data-driven approaches are currently being used for quality assurance and enhancement in a number of institutions, and these approaches are set to become more widespread. Examples can be seen in this blog post from QAA

The discussion suggested that this topic is still at an emergent stage, so we do not think that now is the right time to explore any specific ideas in this area under this consultation.

The topic was rightly seen as being closely aligned to learning analytics, so we will explore the themes above within our learning analytics work. We plan to form teams who will look at developing dashboards for data-informed decision making in learning and teaching as part of future cohorts of our business intelligence work.

A strong theme that emerged was the need for greater sharing and use of evidence for the impact of technology-enhanced approaches in learning and teaching. We are exploring how Jisc can help with this as part of a technology enhanced learning programme and will be able to share more information on this soon.

The consultation has highlighted that this is likely to be a very important area over the next few years, so we will continue to support discussion on this topic and look out for areas where Jisc could help.

Get in touch

If you want to share something via email contact [email protected].

Watch a recording of our webinar which took place on Monday 21 November
Catch up on the discussion so far on our student experience blog