The generic-discipline dilemma: learning advising in quantitative literacy

Abstract

Purpose – This paper aims to provide insights into the experiences of and challenges confronting higher degree research students and learning advisors (LAs) regarding data analysis support. The ability to handle data and use numerical evidence systematically is an important transferable skill and essential for the successful completion of a quantitative research thesis. Design/methodology/approach – A combination of qualitative and quantitative data was used, enabling a convergence of findings: the questionnaire and one-on-one advisory sessions feedback gathered information on the student experience, while semi-structured interviews provided data on the LAs’ perspective. Findings – Phenomenographic analysis of interviews revealed many challenges associated with centralised learning support provision. Learning advisors recognised not only different disciplinary needs but also the tensions associated with working centrally and cross-disciplinary. Students identified a need for more practice-orientated training opportunities in data analysis during their postgraduate and doctoral research. Practical implications – Understanding gained from students’ and LAs’ experiences are essential for changes of university-wide teaching and learning strategies. The collection of “bottom-up” data on the student experience combined with data on learning thresholds provided by faculty and student learning support units would allow a coordinated, institution-wide approach to identified learning needs. Originality/value – Developing a community of practice concerned with quantitative literacy means that staff with expert knowledge, regardless of discipline affiliation, can provide an environment in which students are able to develop their analytical skills further and can participate in ongoing discussions on real-life research and data analysis issues.