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Five challenges for policy research on higher education

by William Locke

Higher education research has grown in recent decades. For example, the number of journal articles published on higher education has increased five-fold in the last twenty years (Seeber, 2023). More is known about higher education than ever before, but there does not seem to be a corresponding growth in higher education policymaking being influenced by this expanding evidence base (Schendel and Knobel, 2024). Indeed, higher education seems to be more or less in crisis – and even under attack – in democratic as well as authoritarian systems, and in rich as well as middle- and low-income countries. Here, I offer five interrelated challenges for policy research on higher education. No doubt, there is more to be said about each of these, and there are other challenges that could be added. Perhaps readers might like to make suggestions in the comments.

1. How to expand international connection and collaboration in an increasingly fragmented and divided world?

Schendel and Knobel (2024) argue for greater collaboration among higher education scholars in different countries and regions, and between researchers and policymakers. They also argue for the translation of knowledge from academic discourse to more accessible forms and between languages, especially to and from the lingua franca of English. They call for connections to be made between different evidence bases, contexts and ways of understanding. These collaborations and connections should not just be among scholars from countries in the ‘West’, but arise from the formation of equitable partnerships between researchers based in the Global South and North, and among those based in expanding and emerging systems. These would allow an exchange of non-Western perspectives and indigenous knowledges within the higher education research and policy communities and “a more global, multi-national, transnational or cosmopolitan optic” (Brooks, 2023, Brooks & Waters, 2022).  Journal editors, for example, should be more sensitive to the location of researchers and the substantive focus of their research (Brooks, 2023).

2. How to place local and national research in regional and international contexts for a potentially global readership?

There is also evidence that higher education research has become more international in scope in recent years (Brooks, 2023; Daenekindt & Huisman, 2020; Kehm, 2015; Kwiek, 2021; Tight, 2021). However, it is important to distinguish between an increasing number of studies of the internationalisation of higher education – often limited to student mobility between nations – and an international or comparative perspective, which may focus on national, or even local, issues but place these within a broader context. Clearly, the collaboration of researchers from different countries and regions already mentioned can help this comparative and contextualised approach, provided there is a well-developed understanding of the differences between the objects of study as well as the similarities. Loosening the dominance of the English language in international higher education research networks will help to achieve this contextualised comparison.

3. How to encourage contributions from a wider range of disciplines and theoretical approaches?

Higher education is not just a topic of study for higher education researchers. As an ‘open access’ field (Harland, 2012), it is also a focus for disciplines such as sociology, economics, business studies, political science, psychology and even geography.  As in other porous areas of the social sciences, this is to be welcomed as a way of incorporating new perspectives, concepts, theories and methodologies into the field.  While this may have led to a certain lack of interaction and integration initially (Macfarlane, 2012; Tight, 2014), the trend towards interdisciplinary scholarship can bring these different perspectives together in creative ways. After all, it is likely that more holistic, multi-disciplinary and innovative approaches will be needed to understand and address current and future challenges, such as artificial intelligence, decolonising the curriculum, academic precarity and populist critiques of universities. Topic-based networks of scholars – where the focus of study rather than the discipline is prime – can encourage this. A major task, however, is to provide space for indigenous knowledges and new ways of knowing that may challenge traditional disciplinary hierarchies and ‘Western’ epistemologies.

4. How to focus on deeper, longer-term issues without losing immediate relevance for policymaking?

On the one hand, we might ask how evidence-based higher education research is? Some of what is submitted for publication is description, commentary, impressionistic interpretation and, even, polemic. We might also ask what form might an acceptable evidence base take? Policymakers tend to look for unambiguous findings that provide clear-cut guidance for decision-making – hard data of a quantitative rather than a qualitative kind. Yet, in education, meaningful quantitative studies are harder to accomplish than in some other areas of public policy. Systematic reviews of literature and randomised control trials do not work as well in education as in medicine, for example. Do we actively seek to build the evidence base? Or do we risk creating fragmented knowledge produced by a series of short-term, small-scale, barely connected projects, based on different and incompatible theoretical and conceptual foundations or employing methodologies that cannot be scaled up?

We should also acknowledge that higher education researchers are investigating their own world. They are interested parties in the object of their studies. Their research agendas are likely to be influenced by these ‘interests’. (Many readers will be familiar with doctoral students researching areas relating to their own experiences, for example, as students or higher education employees). Researchers are often on the receiving end of many of the policies and their impacts. As a result, too much research on higher education has been based on the assumption of a golden age which is being dismantled, rather than from a forward-looking perspective that seeks to meet broader, emerging societal needs.

So, we should recognise the limitations of this expanding higher education research. To date, much of it has been small scale, short-term and dependent on consultancy-style funding. It has had a fragmented and weak institutional base. It has tended to focus on the ‘public life’ of higher education, on strategic issues and their impact, rather than the ‘up close and personal’ issues that can be uncomfortable to investigate. Some of it is only just becoming “disinterested research on reasonably long timescales, with open agendas and based on reflective and critical intellectual values and practices” (Scott, 2000: 124).

On the other hand, how realistic (or idealised) are our conceptions of policymaking and implementation? It is rarely feasible simply to extrapolate the policy implications from a given set of findings, which may simply analyse a problem rather than propose a solution. “It is not a linear, rational-analytical process of examining all the evidence, ‘reading off’ the policy implications of this and then formulating well-designed interventions guaranteed to achieve the outcomes desired” (Locke, 2009: 124). If we are to understand policymaking, and the place of research evidence within it, we have to acknowledge “…the messy realities of influence, pressure, dogma, expediency, conflict, compromise, intransigence, resistance, error, opposition and pragmatism in the policy process” (Ball, 1990: 9). There are many other factors than research findings to take into account in the world of policymaking, not least politics and political expediency (for example, unifying the party, ideology, public opinion and budgets). “A better understanding of the policy-making process and the factors that facilitate or inhibit the take-up of research findings is needed, including the role of the commissioners of research and how findings are presented to, and understood by, policymakers” (Locke, 2009: 125). 

We should also be thinking about the relations between research, policy and practice.  After all, there are gaps between policy and practice: the infamous unintended consequences of policy implementation. However, there is also the danger of slipping into a utilitarian, ‘what works’ frame. The relations between research, policy and practice are empirical matters, themselves open to research and investigation. Studies of policymaking and implementation can enlighten us about the successes and failures of particular policies in specific contexts, and the factors that influence these.  Perhaps we should be aiming for policymaking that is influenced and informed, rather than driven, by evidence? 

5. How to explore the policy implications of research findings in ways that can be useful to policymakers?

The higher education research and policy communities are not always so separate. Research commissioned by policy bodies makes up quite a large proportion of funded higher education research. Most higher education researchers want their research to have impact, and policymakers (at least below ministerial level) want evidence on which to base their policymaking. There is some movement between these worlds, but there should be more and think tanks play a critical role in mediating between the worlds of research and policy. Finally, networked governance suggests we should be looking at audiences beyond government and parliament, to include wider public engagement with research findings, which is essential to democracies.

But how far should this constructive engagement go? It is naïve to think that educational research can solve problems on its own. The relationship between research and policy has been characterized as indirect and more about ‘sensitising’ policymakers to problems than solving them. Research might be seen more as a means of helping policymakers reconsider issues, think differently, reconceptualise what the problem is, and challenge old assumptions. This suggests a more serendipitous and loose relationship between research and policy. So, perhaps we need to be more modest in our aspirations for evidence-informed policy and practice and adopt a greater degree of realism about what can be achieved.

This is an abridged version of the editorial from the latest issue of Policy Reviews in Higher Education.

William Locke is a founding Joint Editor of the journal and a recovering academic. Recently retired, he is a former Director of the Centre for the Study of Higher Education (CSHE) at the University of Melbourne, Director of the Centre for Higher Education Studies (CHES) at the UCL Institute of Education and Deputy Director of the Centre for Global Higher Education (CGHE). He has also had senior policy roles at the Higher Education Funding Council for England (HEFCE) and Universities UK.


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It is not just about evaluation: the role of research in supporting widening participation

by Neil Raven

Much attention is now directed towards the role of evaluation in efforts to widening higher education participation. Indeed, the evaluation of interventions aimed at broadening HE access, and ensuring the success of those from under-represented groups once they are at university, is considered a priority by the Office for Students [OfS], (Blake 2022; OfS, 2023a; OfS, 2024).

Various guidance documents prepared by the HE regulator, both for individual higher education providers (OfS, 2023b), and Uni Connect, the government funded collaborative outreach programme (OfS, 2022), set out a rationale for this focus. ‘High quality evaluation,’ it is argued, ‘allows universities and colleges to understand the impact of their work to support students’, and to improve the effectiveness of this work (OfS, 2023a), and through sharing the findings of such investigations to ‘contribute to the wider evidence base’ (OfS, 2022, 12). In contrast, the role of research in widening participation [WP] receives far less attention and appears to be considered of less significance. Whilst ‘evaluation’ accumulates 42 references in the OfS’s advice to HE providers’ on their access and participation plans, research is mentioned 16 times (OfS, 2023b). More pointedly, the regulator’s guidance to Uni Connect partnerships states that ‘research is not one of the main aims or expected outcomes of funding’ (OfS, 2022, 13).

Yet, arguably, research has an equally important role to play in supporting and advancing the WP agenda. To appreciate this it is helpful to draw out the distinctions between research and evaluation. Perhaps the most fundamental of these relates to the broader remit that research has in ‘seeking new insights’ and making ‘new discoveries.’ Often this involves testing theories or hypotheses, and generating findings that have wider application (Anon, nd, np; Rogers, 2014). For some, research is viewed as ‘active and proactive’, whereas evaluation is ‘reactive’, in the sense of responding to an activity or practice (Anon, 2024, np).

Arguably, these distinct qualities mean that research has the potential to address a number of key WP challenges that remain unexplored in evaluations. For instance, providing insights into the reasons for the comparatively low rates of HE participation amongst particular groups of learners. These include young men from white, working class backgrounds, along with those taking vocational (applied general) courses and studying in further education colleges. All three are areas of current concern for those engaged in widening participation (Atherton and Mazhari, 2019; Raven, 2022; Raven, 2021).

In addition, it is by means of research that we can learn more about the longer-term impact of the pandemic and the cost of living crisis on the progression plans of those from more deprived neighbourhoods, as well as the reasons why some WP students appear more likely to drop out of university-level study. Research can also tell us about the post-graduation experiences of under-represented backgrounds – insights that are of central importance in presenting the case for HE.  Indeed, research can be viewed as complementary to evaluation. Research can provide insights and guidance on how a particular WP challenge can be addressed, with evaluation deployed to assess the effectiveness of the measures taken.

Whilst the OfS’s limited support (and funding) may explain some of the comparative neglect of research, the way much WP-related research (admittedly, this includes my own) has tended to be conducted and disseminated may also act as an impediment to recognising its true value. Therefore, besides calling on the OfS to acknowledge the role that research can play in supporting efforts to widen participation, I have five suggestions aimed at fellow researchers for enhancing the accessibility – and impact – of their work:

  • Share research findings with WP practitioners through talks and presentations, as well as more interactive workshops
  • Publish work in journals whose readership includes WP practitioners and policy makers
  • Explore opportunities to circulate findings in more widely read newsletters and e-bulletins
  • Engage directly with practitioners in exploring potential research topics
  • Consider the practical, real world application of research findings.

I would welcome readers’ views on these suggestions.

Neil Raven is an educational consultant and researcher in widening access. Contact him at neil.d.raven@gmail.com.

Acknowledgements – Thank you to Tony Hudson, Lewis Mates, Jessica Benson-Egglenton, Robin Webber-Jones and John Baldwin.


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The potential of automated text analysis for higher education research

by Stijn Daenekindt

Together with Jeroen Huisman, I recently published an article in which we mapped the field of research on higher education. In a previous blogpost we reflected on some key findings, but only briefly mentioned the method we used to analyze the abstracts of 16,928 research articles (which totals to over 2 million words). Obviously we did not read all these texts ourselves. Instead, we applied automated text analysis. In the current blogpost, I will discuss this method to highlight its potential for higher education research.

Automated text analysis holds tremendous potential for research into higher education. This because, higher education institutions—ie our research subjects— ‘live’ in a world that is dominated by the written word. Much of what happens in and around higher education institutions eventually gets documented. Indeed, higher education institutions produce an enormous amount and variety of texts, eg grant proposals, peer reviews and rejection letters, academic articles and books, course descriptions, mission statements, commission reports, evaluations of departments and universities, policy reports, etc. Obviously, higher education researchers are aware of the value of these documents and they have offered a lot of insightful case studies by closely reading such documents. However, for some types of research questions, analysing a small sample of texts just doesn’t do the job. When we want to analyse huge amounts of text data, which are unfeasible for close reading by humans, automated text analysis can help us.

There are various forms of automated text analysis. One of the most popular techniques is topic modelling. This machine learning technique is able to automatically extract clusters of words (ie topics). A topic model analyses patterns of word co-occurrence in documents to reveal latent themes. Two basic principles underlie a topic model. The first is that each document consists of a mixture of topics. So, imagine that we have a topic model that differentiates two topics, then document A could consist of 20% topic 1 and 80% topic 2, while document B might consist of 50% topic 1 and 50% topic 2. The second principle of topic modelling is that every topic is a mixture of words. Imagine that we fit a topic model on every edition of a newspaper over the last ten years. A first possible topic could include words such as ‘goal’, ‘score, ‘match’, ‘competition’ and ‘injury’. A second topic, then, could include words such as ‘stock’, ‘dow_jones, ‘investment, ‘stock_market’ and ‘wall_street’. The model can identify these clusters of words, because they often co-occur in texts. That is, it is far more likely that the word ‘goal’ co-occurs with the word ‘match’ in a document, then it is to co-occur with the word ‘dow_jones’.

Topic models allow us to reveal the structure of large amounts of textual data by identifying topics. Topics are basically a set of words. More formally, topics are expressed as a set of word probabilities. To learn what the latent theme is about we can order all the words in decreasing probability. The two illustrative topics (see previous paragraph) clearly deal with the general themes ‘sports’ and ‘financial investments’. In this way, what topic models do with texts actually closely resembles what exploratory factor analysis does with survey data, ie revealing latent dimensions that structure the data. But how is the model able to find interpretable topics? As David Blei explains, and this may help to get a more intuitive understanding of the method, topic models trade off two goals: (a) the model tries to assign the words of each document to as few topics as possible, and (b) the model tries, in each topic, to assign high probability to as few words as possible. These goals are at odds. For example, if the model allocates all the words of one document to one single topic, then (b) becomes unrealistic. If, on the other hand, every topic consists of just a few words, then (a) becomes unrealistic. It is by trading off both goals that the topic model is able to find interpretable sets of tightly co-occurring words.

Topic models focus on the co-occurrence of words in texts. That is, they model the probability that a word co-occurs with another word anywherein a document. To the model, it does not matter if ‘score’ and ‘match’ are used in the same sentence in a document or if one is used in the beginning of the document while the other one is used at the end. This puts topic modelling in the larger group of ‘bag-of-words approaches’, a group of methods that treat documents as …well … bags of words. Ignoring word order is a way to simplify and reduce the text, which yields various nice statistical properties. On the other hand, this approach may result in the loss of meaning. For example, the sentences ‘I love teaching, but I hate grading papers’ and ‘I hate teaching, but I love grading papers’ obviously have different meanings, but this is ignored by bag-of-words techniques.

So, while bag-of-word techniques are very useful to classify texts and to understand what the texts are about, the results will not tell us much about how topics are discussed. Other methods from the larger set of methods of automated text analysis are better equipped for this. For example, sentiment analysis allows one to analyze opinions, evaluations and emotions. Another method, word embedding, focusses on the context in which a word is embedded. More specifically, the method finds words that share similar contexts. By subsequently inspecting a words’ nearest neighbors — ie which are the words often occurring in the neighborhood of our word of interest — we get an idea of what that word means in the text. These are just a few examples of the wide range of existing methods of automated text analysis and each of them has its pros and cons. Choosing between them ultimately comes down to finding the optimal match between a research question and a specific method.

More collections of electronic text are becoming available every day. These massive collections of texts present massive opportunities for research on higher education, but at the same time they present us with a problem: how can we analyze these? Methods of automated text analysis can help us to understand these large collections of documents. These techniques, however, do not replace humans and close reading. Rather, these methods are, as aptly phrased by Justin Grimmer and Brandon Stewart, ‘best thought of as amplifying and augmenting careful reading and thoughtful analysis’. When using automated text analysis in this way, the opportunities are endless and I hope to see higher education researchers embrace these opportunities (more) in the future.

Stijn Daenekindt is a Postdoctoral Researcher at Ghent University (Department of Sociology). He has a background in sociology and in statistics and has published in various fields of research. Currently, he works at the Centre for Higher Education Governance Ghent. You can find an overview of his work at his Google Scholar page.


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The (future) state of higher education research?

by Stijn Daenekindt and Jeroen Huisman

  • Stijn Daenekindt
  • Jeroen Huisman
    Jeroen Huisman

Parallel to the exponential growth of research on higher education, we see an increasing number of scientific contributions aiming to take stock of our field of research. Such stock-taking activities range from reflective and possibly somewhat impressionistic thoughts of seasoned scholars to in-depth reviews of salient higher education themes. Technological advancements (such as easy electronic access to research output and an increasingly broader set of analytical tools) obviously have made life easier for analysts. We recently embarked upon a project to explore the thematic diversity in the field of research in higher education. The results have recently been published in Higher Education. Our aim was to thematically map the field of research on higher education and to analyse how our field has evolved over time.

For this endeavour, we wanted our analysis to be large-scale. We aimed at including a number of articles that would do justice to the presumed variety in research into higher education. We did not, however, want the scale of our analysis to jeopardize the depth of our analysis. Therefore, we decided not to limit our analyses to, for example, an analysis of citation patterns or of keywords. Finally, to forestall bias (stemming from our personal knowledge about and experience in the field), we applied an inductive approach. These criteria led us to collect 16,928 journal articles on higher education published between 1991 and 2018 and to analyse each article’s abstract by applying topic modelling. Topic modelling is a method of automated text analysis and a follow-up blogpost (also on srheblog.com) will address the method. For now, it suffices to know that topic modelling is a machine learning technique that automatically analyses the co-occurrence of words to detect themes/topics and to find structure in a large collection of text.

In this blogpost, we present a glimpse of our findings and some additional thoughts for further discussion. In our analysis, we differentiate 31 research topics which inductively emerged from the data. For example, we found topics dealing with university ranking and performance, sustainability, substance use of college students, research ethics, etc. The bulk of these research topics were studied at the individual level (16 topics), with far fewer at the organisational (5) and system level (3). A final set of topics related either clearly to disciplines (eg teaching psychology) or to more generic themes (methods, academic writing, ethics). This evidences the richness of research into higher education. Indeed, our field of research certainly is not limited in terms of perspectives and unleashes “the whole shebang” of possible perspectives to gain new insights into higher education.

The existence of different perspectives also comprises potential dangers, however. Studies applying a certain approach on higher education — say, a system-level approach — may suffer from tunnel vision and lose sight of individual- and organization-level aspects of higher education. This may be problematic as processes on the different levels are obviously related to one another. In our analysis we find that studies indeed tend to focus on one level. For example, system-level topics tend to be exclusively combined with other system-level topics. This should not come as a big surprise, but there is potential danger in this and it may hamper the development of a more integrated field of research on higher education.

In our analysis, we also find a certain restraint to combine topics which are located at the same level. For example, topics on teaching practices are very rarely combined with topics on racial and ethnic minorities — even though both topics are situated at the individual level. To us, this was surprising as the combination of ethnicity and educational experiences is a blossoming field in the sociology of education. The fact that topics at the same level are only rarely combined is less understandable then the fact that topics on different levels are rarely combined. We hope that our analysis aids others researchers to identify gaps in the literature and that it motivates them to address these gaps.

A second finding we wish to address here relates to specialisation. Our analysis suggests that there is a trend of specialisation in our field of research. We looked at the number of topics combined in articles and we see that topic diversity declines over time. This is, on the one hand, not that surprising. Back in 1962, Kuhn already argued that the system of modern science encourages researchers towards further specialisation. So, it makes sense that over time, and parallel to the growth of the field of research on higher education, researchers specialise more and demarcate their own topic of expertise. On the other hand, it may be considered a problematic evolution as it can hamper our field of research to develop towards further maturity.

But what should we think of the balance between healthy expansion and specialization, on the one hand, and inefficient fragmentation, on the other? We lean towards evaluating the current state of higher education research as moving towards fragmentation. Other researchers, such as Malcom Tight, Bruce Macfarlane and Sue Clegg have similarly lamented the fragmented nature of our field of research. Our analysis adds to this by showing the trends over time: we observe more specialisation (not necessarily bad), but there are also signs of disintegration over time (not good). Other analyses we are currently carrying out also indicate thematic disintegration and suggest clear methodological boundaries. It looks like many researchers focusing on the same topic remain in their “comfort zone” and use a limited set of methods. For sure, many methodological choices are functional (as in fit-for-purpose), but the lack of diversity is striking. Moreover, we see that many higher education researchers stick to rather traditional techniques (survey, interviews, case studies) and that new methods hardly get picked up in our field. A final observation is that we hardly see methodological debates in our field. In related disciplines we often see healthy methodological discussions that improve the available “toolkit” (for example here). In our field, it appears that scholars shy away from such discussions and it suggests methodological conservatism and/or methodological tunnel vision.

There are still many things to investigate to arrive at a full assessment of the state of the art. One important question is how our field compares to other fields or disciplines. But if we were to accept the idea of fragmentation, it is pertinent to start thinking how to combat this. Reversing this trend is obviously not straightforward. But here are a few ideas. Individual scholars could try to get out of their comfort zone by applying other perspectives to their favourite research object and/or by applying their favourite perspective to new research topics. Related, researchers should be encouraged to use techniques less commonly used in our field and see whether they yield different outcomes (vignettes, experimental designs, network analysis, QCA/fuzzy logic, [auto-]ethnography and – of course – topic models). In addition, journal editors could be more flexible and inclusive in terms of the format of the submissions they consider. For example, they could explicitly welcome submissions in the format of ‘commentaries/ a reply to’. This would stimulate debate and open up the floor for increased cross-fertilisation of research into higher education and, in general, signal the maturity of research into higher education. Finally, there is scope for alternative peer review processes. Currently, only editors (and sometimes peer reviewers seeing the outcome of a peer review process) gain full insight in feedback offered by peers. If we would make these processes more visible to a broader readership – e.g. through open peer review, which still can be double-blind – we would gain much more insight in methodological and theoretical debates, that would definitely support the healthy growth of our field.  

This post is based on the article: Daenekindt, S and Huisman, J (2020) ‘Mapping the scattered field of research on higher education. A correlated topic model of 17,000 articles, 1991–2018’ Higher Education, 1-17. Stijn Daenekindt is a Postdoctoral Researcher at Ghent University (Department of Sociology). SRHE Fellow Jeroen Huisman is a Full Professor at Ghent University (Department of Sociology).

MarciaDevlin


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Australian HE reform could leave students worse off

By Marcia Devlin

Australia is in full election campaign mode. What a returned conservative government means for higher education is a little worrying, although what a change of government means is worrying for different reasons.

Two years ago, the then federal Minister for Education, Christopher Pyne, proposed a radical set of changes for higher education funding including, among other things, a 20% cut to funding and full fee deregulation. While the latter received support from some institutions and Vice-Chancellors, there were very few supporters of the whole package. Among those who did not support it were the ‘cross-benchers’ – the independent and minor party members of the Parliament of Australia who have held the balance of power since elected in 2014 – and so the proposals were not passed.

The government have since introduced Senate voting reforms which means the minor parties will not be able to swap preferences in order to secure Senate seats as they have done in the past, and there is less likelihood of a future cross bench like this one. Which is a shame for higher education in my view as these folk actually listened to the sector and public and responded accordingly. Mr Pyne has now moved onto other responsibilities. But just before he moved, this actually happened: https://www.youtube.com/watch?v=Hc9NRwp6fiI

The new and current Education Minister, Simon Birmingham has released a discussion paper in lieu of budget measures: Continue reading →

Simon Marginson


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Equality of opportunity: the first fifty years

By Simon Marginson

The article below is abridged from the keynote address given at the SRHE’s 50th Anniversary Colloquium at Church House, London on June 26th 2015.  The full text of this keynote address is available via www.srhe.ac.uk/downloads/SimonMarginsonKeynote.pdf 

Thomas Piketty’s Capital in the Twenty-first Century (2014) clarifies  the distinction between (1)  societies in which incomes are relatively equal and/or there is a high degree of middle class growth and social mobility, which includes (albeit in different ways and for rather different reasons) both the Scandinavian countries and emerging East Asia; and (2) societies like the United States or the UK that are relatively closed in character, with highly unequal wage structures, growing capital concentrations, and static middle classes that are under considerable pressure to defend their past-gained economic and status positions. Continue reading →