This research project used data collected from over 30,000 students across over 60 Higher and Further Education institutions during a UK-wide Jisc-supported survey project to investigate students’ digital experiences.
Our aim was to group students in terms of their digital behaviours and opinions rather than classifying them via the more-commonly used demographic variables such as sex, age, type or stage of course.
Personas are traditionally created from relatively small amounts of qualitative data, e.g. via interviews with individuals. However, this process can be hampered both by small sample sizes and by the interviewers’ expectations and prior knowledge of the system in question. It can be easy to categorise people according to their position in a system rather than by the way they function and behave. In order to avoid this problem we employed multivariate statistical clustering methods that are more commonly used to identify persona groups in the field of user-centred design and market research.
We hypothesised that the generation of personas using this approach might provide novel insight into digital behaviour typologies, and prove to be more actionable to organisations, for example in terms of identifying suitable support methods and mechanisms than traditional personas.
The data collection instrument was the Jisc ‘Student digital experience’ survey, developed by the authors from an earlier research project and through three cycles of piloting and validation. The survey is short, locally administered, and customisable. It does not assume that digital technology is ‘good’ for education (Selwyn 2016), but asks a range of questions about how students respond to the digital environment, how they use their own devices and services for learning, and how they respond to digital aspects of the curriculum.
We used the large volume of student survey data within a multiple correspondence analysis (MCA) to objectively segment the data into functional groups. We then used qualitative data to test, refine and validate these, and so create our student personas.
The results of both analyses will be of scholarly interest and of practical value to ALT-C participants because:
• The digital environment and digital curriculum are of increasing significance to the overall student experience.
• Student-facing staff will benefit from considering these functional persona types when designing e.g. support services
• Organisational representatives will be interested in the validity of the primary research instrument as a tool for investigating the student digital experience and in how to apply its results to effective interventions at a local level.
• Scholarly work on student outcomes of digital provision has focused separately on pedagogical aspects (e.g. Higgins et al 2012, Caird & Lane 2013, Kirkwood & Price 2014, Limniou et al 2015, Henderson et al 2015) and on the digital environment for learning (e.g. Magen-Nagar & Steinberger 2017, Savin-Baden et al. 2017). In contrast our analysis explores the relationships among variables relating to both digital pedagogy and the digital environment and will therefore be of interest to researchers in both schools.
Caird, S. & A. Lane (2013) Conceptualising the role of information and communication technologies in the design of higher education teaching models used in the UK. British Journal of Educational Technology 46 (1).
Henderson, M., N Selwyn & R. Aston (2015) What works and why? Student perceptions of ‘useful’ digital technology in university teaching and learning. Studies in Higher Education 42 (8).
Higgins , S., Z. Xiao & M. Katsipataki (2010) The Impact of Digital Technology on Learning: A Summary for the Education Endowment Foundation: EEF and Durham University.
Kirkwood, A. & L. Price (2014) Technology-enhanced learning and teaching in higher education: what is ‘enhanced’ and how do we know? A critical literature review. Learning, Media and Technology 39 (1).
Limniou, L., J.J. Downes & S. Maskell (2015) Datasets reflecting students’ and teachers’ views on the use of learning technology in a UK university. British Journal of Educational Technology 46 (5).
Magen-Nagar, N. & P. Steinberger (2017) Characteristics of an innovative learning environment according to students’ perceptions. Learning Environments Research 20 (3).
Selwyn, N. (2016) Is Technology Good for education? Cambridge: Polity Books.