Embedding digital literacy in the curriculum

Richard de Blacquiere-Clarkson (Lifelong Learning Centre)

Project overview

It's often rightly said that we live in a digital age. The use of digital devices to communicate, create, share and solve problems has become an essential element of many aspects of everyone's life, including personal and professional. More than this, digital technologies and their affordances shape how we interact with the world and each other, and as such how we conceive of our own identities. As such, developing a high standard of digital skills and literacies should be regarded as an essential graduate outcome - not only for employability but as part of well-balanced personal growth - and all universities need to ensure that every student is well-supported to develop their digital skills and identity in ways which meet their goals and aspirations.

It's widely recognised in broad terms that developing digital skills has great value, but there is limited shared understanding of how to do so or what constitutes good practice. The aim of this project is to establish a robust evidence base regarding the most effective ways to support the systematic development of student's digital literacy skills, focusing on embedding skills development in curricula rather than as additional study or support.

The findings will be used to develop a toolkit to support a range of embedding approaches across disciplines and levels of study, supported by case studies of successful practice as exemplars.

Key findings

At a relatively superficial level of descriptive analysis, e.g. by School, ethnicity, age etc the respondents are remarkably homogeneous despite a very large sample size. An example from the combined 2021/22 and 2022/23 data:

There is a clear overall pattern which holds across all Schools (the one outlier had a low response rate in a single year), as well as other demographics.
Dimensionality reduction and clustering techniques such as Principal Component Analysis and K-means Cluster Analysis do reveal statistically significant trends and grouping of students These groupings are intersectional, drawing upon multiple demographic factors. The most significant is a combination of ethnicity, fee status and study level; then age and undergraduate/postgraduate; School and gender; disability status and survey year
Each of these groups has a distinct profile regarding their survey responses, differences are modest but highly statistically significant (in most cases p <0.01)
Generative AI can be a powerful tool for rapidly testing hypotheses, and for enhancing researcher data literacy. Its findings do need careful validation as errors minor and sometimes egregious are presented with confidence as accurate.

Implications for practice

Large-scale Scholarship research is very much achievable on a modest budget and without specialist expertise
Larger, more systematic datasets allow analytic tools that can reveal patterns which would be invisible otherwise
Whatever you think of intersectionality on ideological grounds, it’s visible in data
We need to be identifying the ways in which intersections of factor affect our students’ needs, and planning accordingly – not focusing on individual characteristics in isolation

If you want to find out more details about this fellowship or what the next steps were upon completion please read the full snapshot or contact Richard (R.Clarkson1@leeds.ac.uk).

Project start date: September 2021