This blog post follows on from Dr Eric Harrison’s, posted here on Oct 21st. Like him I have been reflecting a lot about developing analytical and research skills as part of the undergraduate social science curriculum, in my case at the University of Manchester. My reflections have extended to thinking about what evidence employers want to see in those who graduate from social science degrees and apply for a career in applied social research, or career choices that require them to be competent in using and analysing quantitative data.
I have a book coming out in April next year, entitled ‘Work placements, Internships and Applied Social Research’ in which I devote two chapters to discussing skills. I deal both with analytical and research skills, and professional skills. In each chapter I present a framework on how you can ‘baseline’ (record where you are starting from) your skills, and then I provide worksheets to show you how you can develop and grow your skillsets, and track your learning. The book arose from my experiences of teaching social science students how to do data analysis, and is based on a paid internship programme I developed at the University of Manchester where I am the co-director of the Q-Step Centre.
In six years, we have placed some 250 social science undergraduates into around 60 public, private and voluntary sector organisations. I have witnessed them develop their skills and confidence, with some returning to university to undertake quantitative dissertations, and then many graduating into analytical roles. My expertise therefore is in experiential learning, and I write about this in the book. For this blog post I thought it would be useful to outline and focus on the reflection framework I have developed so that you might consider using it yourself.
First though you have to get behind the idea of reflection, as a practice (I include a chapter on this in the book, too). As a lecturer I observe different attitudes to learning. Carol Dweck (2017) has written a book entitled ‘Mindset: changing the way you think to fulfil your potential’ and I have years of personally-observed experience to support her research that shows there are essentially two types of learners. One has a fixed mindset – is afraid of failure, averse to stretching themselves, and therefore stays within their comfort zone – whereas the other has a growth mindset – is willing to learn from mistakes, open to being challenged and capable of reflecting and changing. This is a little reductive but it serves for the purpose of helping you think about your own predispositions to learning.
I’m not ashamed to admit that as a younger person I definitely did not have what Dweck terms a growth mindset, but there were many other socioeconomic factors at play too (not least being a first-generation university student who had little idea what she was supposed to be doing, and someone who fell between the humanities and the sciences). In time I became a convert to having a more flexible approach to learning, and to embracing and learning from my mistakes. I also learned to embrace being a hybrid – a polymath even. But my career path has been a meandering one and I guess I’m a Late Bloomer (Karlgaard, 2019). My plea to focus on reflection comes to you from my own journey. I wish I had learned to reflect sooner and so regard this my gift to you.
Learning data skills is hard. Opening yourself up to the possibility that it might require perseverance and a certain amount of failing before you succeed will take you a long way. If you can become systematic about capturing and reflecting on your learning you will start to see what you are good at, and which areas you might need to work at a little harder. The rest of this post will show you how you can do that.
Commit to reflecting regularly. For my workplace students I insist they do this at the beginning, middle and end of their two-month long internship. Initially I suggest this is impressionistic. I ask them to write this down and share it with me by email, but equally they could videorecord it or use a platform to record their learning. Here is what I ask them over the course of two months:
1. End of day 1/ week 1: How do they feel about their experience? What has gone well? What questions do they have? Is it shaping up to be what they thought it would be?
2. Halfway through: Here I ask for some more detail. What are they learning and how? What is challenging and what comes easily? What data and methods are they using and how are these helping them with the research project they are working on? Are there any surprises? How do they feel about how they are handling the work placement?
3. Two weeks after they finish their placement, I get them to reflect more deeply using a structured set of questions. These are tailored to focus on the project they were undertaking and ask for a reflection on the data and analytical skills and tools they learned, and also on the professional skills. Essentially, I want to think about what their main takeaways are and whether they would do anything differently as a result.
We recorded some short films in 2019 from five of the selected students – you can see the recordings here. The findings are illuminating. Students embrace the challenge, grow in confidence, realise that they are capable of performing well in the workplace and end up with an appreciation that actually these skills they are being taught do matter to the organisations where they are hosted (anything from small charities, though to government departments, to the World Bank).
Dr Harrison in his post talked about critical numeracy. All of the students were able to develop this AND talk about it after their placements. Indeed we celebrate their placements (rather than assess them), holding a half-day student conference in the autumn when they return to University. This is one of the best days of the academic year for me. Seeing what an undergraduate is capable of achieving in just 8 weeks is phenomenally rewarding. You can take a look at the posters they produce for this event on the website (here's the link for the 2019 posters).
The most important part of reflecting though is arguably that all the students have a record of what they learned. When they start to apply for graduate roles, or postgraduate courses, they can look back over their reflective pieces and extract evidence of what they learned. In my book I develop a whole suite of tools to show you how to do this – but I won’t reproduce those here.
Professor Carter is a professor of statistical literacy at University of Manchester and co-director of the University of Manchester Q-Step Centre.
Comments
Post a Comment