SOCRMx Week 1 blog assignment
I am a first-generation graduate. Well, according to most definitions, I am. You could make all kinds of assumptions about me based on that label alone. What it has meant for my life and prospects, however, could be wildly different from the experiences of other graduates who have self-identified or otherwise been categorised that way. What interests me is that this facet of my identity, from the day I applied to university, to my graduation and beyond, will have contributed to statistical data used to inform and justify a range of socio-political policies and evaluations. As a contributing datum, I could have appeared in all sorts of policies and papers, even conflicting ones, to justify organisational and national strategies.
Neither of my parents “went to university” as I did, but both had the chance to go to grammar school, which I did not. My father studied an HND part-time at a local college, while training as a manager at a local record player manufacturer, and my mother attended teacher training at a local FE college. Both of them had the education needed to pursue middle-class professions, at least at the time. So, I could not claim to be educationally disadvantaged in the way that some first-generation graduates might be. Of course, I could take a moral stance and tick the other box. The thing is, when I compare my experiences to graduate friends and colleagues whose parents did go to university, a multidimensional spectrum of advantage and disadvantage starts to emerge. It both fascinates and infuriates me.
Having touched on these feelings during my studies on the MSc Digital Education programme, and now that I work in higher education, with a possible PhD ahead, I thought it would be a good starting topic for the journey ahead. As both my work and postgraduate studies specialise in learning technologies, I am also interested in the role that technology plays in relation to social and educational advantage or disadvantage.
Up until now, I have explored learning technology from a pedagogical perspective, looking at ways that it tries to support or enhance learning, mainly in terms of aiding cognitive processes and achieving learning outcomes. But it’s that ‘multidimensional spectrum’ I referred to previously, which I find particularly intriguing now. With emerging technologies like robotics, analytics, machine learning, and artificial intelligence, already established as part of our personal lives, and suggestions that there might be an “explosion” of innovation to come, I’d like to find out what impact this might have on social inequality in higher education. Will it disrupt or reinforce it?
Whenever I meet with proponents of learning analytics, I tend to be disappointed by how focused they are on the business potential of the technology, such as managing drop-out rates by picking up on associated behaviours and characteristics. Such technology could be used to personalise learning experiences and tailor support around student needs, but I’m not sure that’s its biggest selling point. I am concerned about what this might mean for disadvantaged students later on, as all these systems are gradually, or rapidly, starting to link up. Learning analytics captured during primary and secondary school, could potentially combine with social media and other online data, to build a profile of a student before they even consider their next step after school. Again, this could be used to target support and even the playing field, but it could also lead to HE institutions cherry-picking characteristics and targeting potential students to meet some other agenda.
Having narrowed my field of inquiry, I have started to analyse and compare publications to get a feel for established types of research and methodologies in this area. Issue 5-06: Education and Social Mobility, in Volume 34 of The British Journal of Sociology of Education, has proved to be a very helpful starting point. I particularly enjoyed reading A.H. Halsey’s Reflections on education and social mobility, which considers the role of education and social research from the very qualitative perspective of his personal history, despite his overall stance:
‘Quantitative work is, in my view, essential to making advances in social science. It is especially so in studies of social mobility.’ (p.646)
He reconciles that he is among ‘pioneer supporters of quantitative methods but also to have been friendly towards qualitative work’ (ibid).
I feel that qualitative research is important in contextualising and evaluating quantitative research. This is reflected in my feelings that narrow definitions like ‘first-generation graduate’ don’t acknowledge or, therefore, address the whole story. Quantitative data, however, is often needed to transform the research into shared meanings and practical applications.
Reflecting on the video discussion between the authors of Objectivity and subjectivity in social research (Scott, Williams and Letherby, 2014), a few points stood out to me, like the importance of perspective and relativity. I do want to capture a sense of perspective, like the table-electron analogy referred to in the discussion. This I feel necessitates some level of qualitative output. Also, the possibility that some/all methods and methodologies may be subject to their own biases and subjectivity was very interesting to me. When choosing a method/methodology for my own research, I think Friesen’s (2017) breakdown of epistemology into different components will be very useful. For example, where the object of study is the relationship between origins and destinations as a measure of social mobility, I might question the logic, the syllogisms applied. I found ‘grounded theory’ (Charmaz, 2017) somewhat more difficult to grasp, but the ‘coding’ concept could be an interesting way to analyse existing research through a different lens.
Having taken some time to compare spectrum ends of the quantitative/positivist – qualitative/constructivist paradigms (Coe, Waring, Hedges, Arthur, 2017, p.6), I reflected my feelings that quantitative data might be ascribed or subject to qualitative properties as soon as it is analysed, not to mention the subjectivity of every other individual/organisational/cultural interpretation.
At this point, I think discourse analysis and possibly social network analysis will form the basis of my initial research methods. I need to compare examples of different approaches before deciding on the specifics. My bachelors degree was in Writing, so I have a fairly keen, but slightly recreational interest in textual/linguistic analysis. I enjoy finding myself caught up in semantic tensions in the logic or paradigms of academic and other texts, often wanting to re-frame the questions or outcomes using alternative terms and definitions.
I have a bit more reading and consolidation of notes to do, before hopefully summarising them in a follow-up post this week.
Charmaz, K. 2017, An introduction to grounded theory, SAGE Publications Ltd, London
Coe, R., Waring, M., Hedges, L. and Arthur, J. (eds) (2017). Research Methods and Methodologies in Education: 2nd Edition. London, Sage.
Halsey, A.H. 2013, Reflections on education and social mobility, British Journal of Sociology of Education, 34:5-6, p644-659
Friesen, B.K. 2017, What is epistemology?, SAGE Publications Ltd, London
Scott, J, Williams, M & Letherby, G. 2014, Objectivity and subjectivity in social research, SAGE Publications Ltd., London