Writing a literature review in your own ‘voice’

Literature review sections of a paper or thesis are a tricky beast, to be sure. In my writing workshops, and face-to-face work with writers and their texts, this section, next to ‘theory and analysis’ presents the greatest challenge. This stems, in large part, from a struggle to marry what other authors are saying with what the writers want to say: to let your own ‘voice’ come through as you base and inform your argument on and with relevant reading and research.

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Firstly, to be clear, when I say ‘voice’ in academic writing, I mean argument. In a piece of academic text, such as a thesis, paper or book chapter, your ‘voice’ is the argument you are making, and that is driving the text forward. It is your contribution to knowledge in your field.

I have written here and here and here about literature reviews, and Pat Thomson and Inger Mewburn have some useful posts that you should check out too. In this post, I want to look at less conceptual and more ‘nuts and bolts’ issues in actually writing a literature review that makes your ‘voice’ audible, and builds one part of the argument of your paper or thesis. Essentially, this section must make an argument for what the GAP is that your research is addressing, and discuss the ways in which the gap HAS been addressed in other studies, yet point out clearly the shortcomings/blindspots/remaining questions that this research leaves open, which is where YOUR STUDY comes in.

I trialled an approach to thinking about this, and revising drafts of literature reviews in a recent writing workshop, and their feedback gave me the courage to try it here. I call it ‘concepts and claims over author names’. Other have written about literature review sections that are a citation dump, or a laundry list –  essentially as long list of which authors made which claims, and who contradicts who and how, and so on. This shows that you have read, but not that you necessarily understand how to use what you have read to build your own argument (in support of the need for your research or study). Thus, you need to move from the ‘who said what’ approach (author names) to which concepts, claims, findings etc are useful to tell the reader about so that they can position and understand the argument you want to make.

Look at this example, kindly lent from a student’s early draft of a proposal:

The goal of ODL is to widen participation and to overcome geographical, social and economic barriers (Kelly & Mills: 2007, p.149) to education. Learners experience isolation due to separation from their institution, lecturers and fellow students (Rumble: 2000, p.1). Although according to Daniel et al. (2009, p.24), ODL has been identified as an effective way of reaching out to large student numbers, Perraton (2000) observes that ODL institutions have high dropout and low pass rates. While there are many factors that contribute to attrition in distance education programmes, at the top of the list according to Stacy, Ludwig, Hardman and Dunlap (2003) is level of interaction and support. Successful distance learners are driven by intrinsic motivation, and quality personalised and affective learning support (Holmberg, 2003). However McKenna (2004) disagrees with this assertion by saying that student success in higher education environment is not a function of motivation but rather of student investment in his/her studies which agrees with Tinto’s (1975, 1993, 1997) assertion that student success is a function of stunt’s commitment to his/her personal goals and that of the institution.   He further says that this investment is both material and psychological. The greater the input to the provision of student support services, the greater the success rate (Sewart, 1993).

There are three main observations I make that I’d like to highlight here:

The first is the positioning of the references (in green) – throughout, they placed after claims (as indeed they should be) but in such a way as to make the effect of the whole paragraph more a list of these claims, than using the ideas advanced by these authors in support of the student’s own claim. So, this is a little ‘laundry list’-like right now. The second, then, is the student’s own claim: what is it? It could be about the goal of ODL institutions, or challenges they face, or student attrition. It is not yet clear. Each paragraph you write needs to have a claim YOU advance, and that selected claims and evidence from reading can be organised around, before you connect this back to the golden thread you are spinning – what is this information helping the reader to understand about YOUR STUDY? The final observation is this, precisely: the connection between this selected information from the readings with the student’s own project. I have attempted a re-write:

Online and Distance Learning (ODL) faces several key, student-related challenges in addressing its central goal. The goal of ODL is to widen participation and to overcome geographical, social and economic barriers to education (Kelly & Mills, 2007). Yet, many learners experience isolation due to separation from their institution, lecturers and fellow students (Rumble, 2000). This sense of isolation may then result in lower levels of persistence, resulting in ODL institutions having high dropout and low pass rates (Daniel et al., 2009; Perraton, 2000). While there are many factors that contribute to attrition in distance education programmes, at the top of the list is students’ level of interaction and support (Stacy, Ludwig, Hardman and Dunlap, 2003). Holmberg (2003), for example, argues that successful distance learners are driven by intrinsic motivation, and personalized, affective learning support. However McKenna (2004) disagrees, saying that student success in a higher education environment is not primarily a function of motivation per se, but rather of a student’s investment in her studies, both material and psychological and the systems created to enable this. Tinto (1975, 1993, 1997) echoes a call for a more systemic, rather than individualised approach to student support, which should be applied in ODL contexts. What all of this means for ODL institutions, is that increasing student retention and success is a complex challenge with numerous variables. These authors, however, seem to be pointing to a need to begin with addressing student support, to decrease alienation and increase students’ ability and willingness to invest in their education more meaningfully.

What I have tried to do here is address my three concerns. In orange, a point, and an explanation of how this information is all pointing back towards the larger study, which is about creating relevant ODL student support structures to increase student success. It may sound mechanical, but try to be conscious of beginning paragraphs with a claim of your creation – based on your reading, but in your own words, and that advances or builds your argument or voice. Not every paragraph will end with an explanatory note, but you should be conscious of drawing the connections between the research you have done and your own argument: as Pat Thomson points out, all reading you include in your thesis must have relevance to, or be positioned in relation to, your argument.

In pink, I have highlighted connecting phrases that position the authors’ claims in relation to one another, yet enable the voice of the writer to come through more clearly, as you get a sense of the writer choosing where to place the claims and what claims to use in making this small part of the argument. Yet, however, while, although – these kinds of ‘transitional’ words are incredible useful in writing, not just to create more readable text, but chiefly to indicate the position of claims made by other writers in relation to one another, and in relation to the argument you want to make.

loads of reading.jpegPerhaps approaching any ‘review’ of the literature from this kind of starting point – concepts and claims over author names (and lists of their points) – will re-orientate you away from ‘reviewing’ the literature, towards using selected literature to make an argument. The point is not to show your readers everything you have read, and what everyone else thinks about your research; the point is to tell us what you think is relevant, and why, using established research to shore up and solidify the credibility and significance of your claims.

 

 

Putting your theory to work in analysis

You now have generated data – in some form, whether primary or secondary – and now you need to code and make sense of it; you need to put it to the task of answering your research question(s). In other words: analysis. This was the toughest part of my own PhD: I had a mountain of data – how to choose the right pieces? What to say about them? How to make sense of them in relation to my research questions?

This is where theory and concepts come into their own in a PhD or MA. You will have some form of theoretical or conceptual framework (for clarity on theory and concepts, how they differ and work together, please watch this short video). Where students often go off track, though, is not using these concepts or theory to do the work in analysis. The theoretical or conceptual framework ends up standing alone, and some form of thematic description of the data is made, with a rather thin version of analysis. In this situation, it may be difficult to offer a credible answer to your research question.

Analysis is, in essence, an act of sense-making. It requires you to move beyond a common sense, everyday understanding of the world, and your data – the level of the descriptive – to a theorised, non-common sense understanding – the level of the analytical (and critical). Analysis means connecting the specific (your study and its data) with the general (a phenomenon, theory, concept, way of looking at the world) that can help to explain how the specific fits in with, or challenges, or exemplifies the general. If you do not make this move, all you may end up with is a set of data that describe a tiny piece of the world, but with little or no relevance to anyone else’s research except perhaps the few other people researching the same thing you are.

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So, how might you ‘do’ analysis?

Imagine you are doing a study on the role of reflective learning in building students’ capacity to critique and create professional knowledge that encourages ongoing learning and problem-solving. ‘Reflection’, or ‘reflective practice’ would be a key concept, as would ‘professional knowledge’, ‘problem-solving’, and ‘learning’. These have generalised, or conceptual meanings that could apply in a range of ways, depending of the parameters and questions of a specific study. Thus, they can do analytical work, helping you to theorise as you answer your research questions.

Then imagine your data set is assessment tasks completed by students in social work and accounting, as two professional disciplines which require adaptive, ongoing learning and problem-solving. You now need a way of employing your key concepts in analysis. You could look at the intentions of the task questions – how they do, or do not, explicitly or actively enable or encourage problem-solving and reflective thinking and learning, and then look at students’ responses and see the extent to which the desired forms of learning are visible or not. This would yield useful findings to feed back to these disciplines in using assessment more effectively.

To reach theorised findings that go beyond describing what the tasks and the student writing said, and conjecture about what the tasks and written responses mean in relation to your study’s understanding of professional knowledge, learning, problem-solving and reflection, you need to start with questions.

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For example: these tasks seem to be using direction words such as ‘name’, ‘list’, ‘describe’, ‘mention’, which require mainly memorising, or learning the notes in a rote manner. What kind of learning would this encourage? What impact would this have on students’ ability to move on to more analytical tasks? Is there a progression from ‘memorisation’ towards ‘problem-solving’ or using knowledge to reflect on and learn from case studies etc? What kind of progression is there? Is it sensible, or not, and how could this affect students learning? And so on.

You could then present the data: e.g., this is the task, and this is when students work on this task in the semester or progression of the course, and this is the task that follows (show us what these look like by copying them out, or including photographs). This part of the analysis is quite descriptive. But then you pose and answer relevant questions guided by your overall research objectives: if these two disciplines – social work and accounting – require professional learning and knowledge that is built through reflection, and the capacity to USE rather than just KNOW the knowledge in the field so that professional can adapt, continue learning, and solve complex problems, what kinds of assessment tasks are needed in higher education? Do the tasks students are doing in the courses I am studying here do these kinds of tasks? If yes, how are they working to build the rights kinds of knowledge, skills and aptitudes? If no, what might be the outcome for these students when they graduate and move into the professions? You then have to use the concepts you have pulled together to create a theorised understanding of professional reflective learning to pose credible answers, that are substantiated with your data (as evidence). This is the act of analysis.

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In both qualitative and quantitative studies, the theory or concepts you choose, and the data you generate, are informed by your research aims and objectives. And in both kinds of studies, analysis requires moving beyond description to say something useful about what your data means in relation to the general phenomenon you are connecting with, and that informs your theorisation (student learning, climate change, democratic governance, etc). Thus, you need to work – iteratively and in incremental stages – to bring your theory to your data, to make sense of the data in relation to the theory so that your study can make a contribution that speaks both to those within your research space, and those beyond it who can draw useful conclusions and lessons even if their data come from somewhere else.