The Degree in Modern Languages

The Sector and the Degree

When diagnosing the ‘crisis’ of Modern Languages (ML) in the UK, the most widespread supporting illustration is the significant reduction in the number of student enrolments on degrees in ML, especially for certain languages. For sure, as a sector of higher education and research in the UK, ML are dependent on the existence of a distinctive kind of UG degree. Whether ML form a ‘discipline’ remains a matter of debate and projection. Yet ML units, are, in the final analysis, built around their degrees. It is not surprising therefore that the ML degree has become such a focus of attention within the sector as it faces ‘crisis’ and then, in response to it, embraces (or braces itself for) ‘reform’. The nature, identity and shape of degrees in ML is one of the few variables in the complex equation that we call ‘the crisis of ML’, over which the sector at least in principle has autonomy and for which it cannot but take responsibility.

But what is a degree in ML, and does it have a clear identity? Traditionally, there was no BA in ML, but a series of BAs in specific languages, though usually delivered by a single overarching unit divided into language-specific subunits, each offering a broadly similar degree model. Over the past decade or two, most institutions in the UK have been looking to bring language-specific degree programmes closer together, both administratively and academically, aligning and interconnecting them in terms of regulations and curriculum. This kind of ‘integration’ (pursued slightly differently at different institutions) is rationalised in various ways but is mostly driven by the fact that individual language subunits, even larger ones, would be inefficient and uneconomical to run on their own. This has also led to various attempts at creating a more integrated curriculum in ML, though such reforms have been slow and have at times produced tensions since there is, in truth, little consensus within the sector regarding the epistemological basis for integration, despite the intensification of a search for it over recent years, precisely through efforts to articulate what ML might mean as a ‘discipline’.

It is doubtful, however, that ML can form a disciplinary entity in a strong sense. Even when belonging to a successfully ‘integrated’ unit, staff in ML usually continue to identify first and foremost with academic networks in their specific language and culture and/or with some narrower disciplinary specialism/area of research, depending on the objects, periods, problems or approaches that they are interested in. Divergences are not just a matter of research focus but also teaching expertise. A particularly important but often unspoken division in ML exists between expertise in teaching language itself and expertise in teaching what is broadly referred to as ‘culture’. Moreover, many members of staff with a base in ML are not restricted to teaching on a ML degree. They often simultaneously teach, have taught, or are able to teach on other degree programmes (Comparative Literature, Film Studies, Visual Culture, Translation Studies, Linguistics, History, Area Studies, Gender Studies, Media Studies, Medieval and Early Modern Studies, to name but some examples).

This absence of a single disciplinary anchor is also characteristic of students enrolling on degrees in ML. Young people who choose to read ML at university do so for a variety of reasons, following different interests, ambitions and motivations – some because they are fascinated with languages and enjoy learning them or understanding how they work; others because they love and want to study literature, film, and other arts; others still, because they are intrigued by a particular part of the world and want to become all-round experts in it; and many choose to study languages as mere complements of something else entirely, on the assumption that this would be useful for their careers. One must also not forget that degrees in ML do not exist in self-sufficient isolation but are part of a network of degrees offered by different units across the humanities, social sciences, and beyond. ML units commonly share students through elective modules, joint degree programmes, flexible Liberal Arts or Combined Honours degrees, as well as specialist interdisciplinary degrees (Comparative Literature, Visual Culture, Area Studies). In other words, even when enrolled on modules based in ML units there is little that is ‘pure’ discipline-wise in what students pursue in their undergraduate studies.

Embracing Diversity – Maintaining Unity

What all of the above means is that insisting on achieving overarching disciplinary consensus across the sector as a precondition for conceptualising the degree in ML is at best unnecessary and distracting, if not counterproductive, since the value of what is studied on degrees in ML, for both students and staff, is not contingent on ML forming a coherently defined discipline in its own right. The disciplinary heterogeneity and openness of ML need not be a weakness but an advantage. Not insisting on patrolling disciplinary boundaries is what allows ML as a sector to bring together, in dynamic and flexible ways, teams of academics and cohorts of students with a variety of backgrounds, interests and expertise, and to foster unique and enriching forms of interdisciplinary interaction across the humanities and social sciences, with ‘language’ – in both broad (metaphorical) and narrow (literal) senses – serving as pivot. Crucially, ML units themselves need not be viewed as entities defined by a common disciplinary identity, but as complex professional collectives that together carry out a set of mutually interconnected and interdependent specialist tasks, each of which requires certain kinds of expertise and skill, and each of which involves specific forms of practice, yet which need not all be shared by every member of the collective.

There is no doubt that at each university, its ML unit (understood as an academic collective) should seek to define in a coherent way its overall identity as a hub of a particular set of professional tasks, forms of expertise, domains of research, and modes of pedagogy. A key part of this should certainly be a clearer articulation of both the epistemology and the pedagogy underpinning the degree programme that the unit offers. However, each institution ought to feel free to shape its own degree in ML in a way that strategically suits it, depending on the kinds of academic expertise it wants to give precedence to and the kinds of students it wants to attract. ML degrees across the nation do not need to be clones of each other and there is no need to define an ‘ideal’ degree shape that all ML units should follow. Universities can strategically develop different combinations of disciplinary and pedagogical strengths. While most ML units would probably look to maintain a spread of different areas and types of expertise, many could decide to prioritise certain overarching frameworks over others. In this context, there is no need to see ML units that, for example, build a degree in ML which leans towards area studies as somehow in disciplinary conflict with analogous units that, in contrast, prioritise comparative approaches to literary production. Similarly, it is as legitimate for a particular ML unit to foreground the study of visual culture as it is for another to place proportionally greater emphasis on, say, linguistics.

Yet as a sector, ML ought to consciously cultivate a sense of itself as a multifaceted and variously interconnected, whole – a ‘broad church’ in other words. In order to ensure unity in diversity – i.e. both foster a firm commitment to ‘ML’ as a common project and, at the same time, embrace the potentially wide variability of approaches within it – it is important to identify a set of unifying elements that characterise a degree in ML, specifically as a way of organising discussion around the different strategic directions in which such a degree could potentially be taken.

Which Languages and How to Teach Them?

Students who enrol on a degree in ML are commonly motivated by a desire to become able communicators in at least one, usually two, and sometimes even three or more, world languages at a reasonably high level of fluency and in a range of registers and functionalities. Because of this, it would seem fundamental for a degree programme in ML to ensure that, by the end of it, the graduate is as strong as possible an all-round communicator in their chosen language(s).

This is, of course, an entirely standard and quite traditional objective of degrees in ML; it is expected by students, employers and society more generally. However, it is something important to bear in mind precisely because a degree in ML is not, in fact, reducible to this objective. Yet among the key questions that each ML unit must answer is which languages to offer with this particular objective in mind and how precisely to integrate the attainment of this objective with other elements of the degree.

A major question for the sector as a whole would also be how to widen the range of languages on offer across the nation and how to fund provision in those languages for which the likely demand will never make the enterprise economical, let alone profitable. More technical questions might include how to manage a widening diversity of pre-existing linguistic competency in future student-recruits. Languages that have never before had to rely on beginners’ streams are likely to need them in the future. At the other end of the spectrum, ML units might want to tap into the growing pool of heritage speakers, whose idiosyncratic linguistic competency will inevitably require adjustments at the level of provision. Such questions will need to be addressed especially by those ML units that find that they must look for student-recruits outside traditional pools, i.e. beyond cohorts studying a language at school.

The Professionalisation of Linguistic and Related Competency

A certain portion of students enrolling on degrees in ML are likely to want to take the development of their language skills a step further in professional terms. Degrees in ML already commonly offer more specialist modules in subjects such as advanced, specialist translation and interpreting, business and professional communication, or language pedagogy. Most ML degree programmes offer this as a handful of final-year electives that only a certain percentage of students takes alongside other types of modules. However, there is no reason why an institution that believes that a more ‘applied’ approach would be strategically beneficial, might not decide to build its entire ML degree or at least a specific pathway within it around this type of programme. Whether an institution decides to go down this route of prioritising the professionalisation of language-based skills and related competencies, to what extent, and how precisely, should, of course, be a matter for individual institutions. It is vital, however, for this to be a strategy that is not imposed from outside the profession (namely, by university managers who lack inside knowledge and understanding of ML as a sector), but is developed by the academic collectives themselves.

The aim of such a more professionally oriented degree or pathway would be to refocus the study of ML onto ‘real life’, practical skills and competencies, and, more importantly, to professionalise it in ways rarely done on traditional ML degrees. While the aim of some institutions might be to train to a high standard specialist translators and interpreters, multilingual professionals (secretaries or executives), and language teachers, there is no reason to assume that this more ‘applied’ approach would automatically be restrictively functionalist, intellectually limited, or indeed, narrowly linguistic. Such a professionalised programme could also include, for example, the study of literature but in a way that is quite serious about preparing students for specific professional careers – say, in literary translation, multilingual publishing, or creative writing. Similarly, such a programme could include the study of film and visual culture, but one focused on working in film and art production, media and journalism, though in ways that require higher-level linguistic and cultural expertise. There are, of course, many other ways of conceptualising such a ‘professionalised’ programme – for example, by combining training in community interpreting with elements of social work; or the study of a language with training in how to teach that language in schools.

It ought to be clear, however, that building this type of programme in a serious and systematic way would undoubtedly require the recruitment of some staff with qualifications and specialisations different to what one typically finds in traditional ML units. It would require other changes too, such as the development of apprenticeship partnerships with relevant non-academic organisations or the organisation of a year abroad with a more consistently developed professional focus. It would no-doubt also prompt the development of different kinds of interdisciplinarity within the university – essentially those that emphasise links between ML and more professionally oriented units (Law, Business, Education, Social Work, Media Studies). There is no doubt that such a more ‘professionalised’ model is likely to suit some institutions better than others, although the precise way in which an institution adopts this approach can remain entirely open and it can thus, in principle at least, be introduced by different types of institutions and not just those with a reputation for ‘applied’ degrees.

What/Where is the Expertise?

Yet pursuing a degree in ML is, of course, never merely about functional communication, whether general or professional, however diversely these might be framed. Students who embark on a degree in ML generally expect that their studies will give them distinctive access to specific social and intellectual worlds associated with a given language – worlds that would otherwise remain obscure, misunderstood, ‘other’. In this context, the study of a ‘language’ becomes a means of reaching particular kinds of knowledge and understanding of specific cultures/communities/societies/geopolitical areas. This suggests that there are two distinct, but fundamentally intertwined, dimensions of expertise built into ML degrees. One is tied to the specificity of a given language and, by extension, of the communities/societies/cultures associated with it. The other has to do with the development of an understanding of how meanings are generated in particular symbolic practices. In ML degrees, these two dimensions are hardwired by virtue of the fact that ML students study the generation of meanings by specific symbolic means not in the abstract, but as always grounded in empirically concrete, historically and linguistically shaped, socio-cultural worlds.

In this context, the actual language studied on a degree in ML is not just a means of access, a ready-made ‘key’ that supposedly lets one through a ‘language barrier’. Learning a language (in the way it is formally taught) is just a starting point in accessing the full complexity of meanings that are produced within specific social and cultural worlds. Using (loosely and provisionally) the generalising language of semiotics, one could say that what students study on degrees in ML is a far broader range of ‘codes’, ‘encodings’ and ‘encoded meanings’, together with the complex processes, means and contexts of encoding and decoding meaning across different sets of symbolic forms and practices, and in a whole variety of socio-cultural contexts. Natural language itself represents a bundle of intertwined codes, but what is studied on a ML degree goes far beyond this and extends to an in principle ever-expandable, though always entirely concrete, range of systems, infrastructures and practices of symbolic work and meaning-creation which are usually brought together under the broad term ‘culture’.

ML have traditionally emphasised the study of texts and discourse, of writing and aesthetic literature, often understood as entailing ‘higher-level’ codes rooted in, but exceeding, linguistic codes themselves. However, ML have long moved also to study practically every other form of cultural production to include visual, audio, performative or syncretic forms of meaning-creation found in theatre, cinema, visual culture, mass media, as well as meaning-creation in everyday culture, political discourse, and many specialist areas of intellectual production. These commonly include and depend on language, but the understanding of meaning-creation here relies in even greater measure on the study of contexts, infrastructures, mechanisms, conventions, logics, norms and processes of encoding and decoding meaning that have little to do with literal language. In ML degrees, the study of meaning-creation invariably includes the study of concrete ‘encodings’ (e.g. texts) as well as of the specific meanings encoded in them. However, what students on a degree in ML also study is how meaning is generated more generally and this includes learning about various formal aspects of ‘encoding’ and ‘decoding’ by using distinctive methods of analysis and interpretation typical of literary, visual or film studies (methods which are themselves, of course, rooted in specific historical, social, cultural and, not least, disciplinary contexts). This also means that the degree in ML includes the theoretical, historical and practical study of how meanings have been encoded and decoded through (always specific) history, and how this has been changing thanks to changing socio-political, media-technological, cultural-historical or epistemic-intellectual contexts.

Emphasis on specificity – of language, history, community, culture and society – as decisive to expertise generated in ML does not mean that this expertise is enclosed in language-bounded ‘silos’. Indeed, critical to understanding the processes of meaning creation in whichever language, culture or society is also the study of what happens when one moves from one ‘code’ to another, whether in translation (inter-lingual or inter-semiotic) or more broadly in the transnational and transcultural circulation of texts, ideas, concepts, values, aesthetic paradigms, social norms, and so forth.

What the above suggests is that what goes by ‘studying a language’ (which is what one does on a degree in ML) is not to be understood as ‘learning a language + studying something else’ (e.g. literature, culture, history, society, politics). This is not to say that language-learning cannot be separated from the study of other forms, means and contexts of meaning creation – it can, with different objectives in mind: for example, for the sake of expediting purely functional communicative proficiency in a given language for a relatively limited purpose. This is precisely what happens in language courses delivered by university language centres catering for students who want to ‘learn a language’ without doing a degree in ML.

But there can be other reasons too. For example, one can imagine an institution deciding that it wants to prioritise the study of cultural forms (literary, visual, cinematic, media) or broader issues and topics (migration, race, gender, sexuality, digital technology, environment) in a way that de-emphasizes linguo-cultural specificity, and instead foregrounds a generalist or globalist perspective on them. Such an institution might decide to take the extreme step of devolving language teaching to a language centre while constructing a unit that focuses on ‘supra-linguistic codes’ across diverse languages simultaneously. While feasible in principle, such a separation would, however, have significant consequences on how meaning-creation is studied since it would inevitably impose certain artificial limits (e.g. the need to study texts in English translation), which go counter to the ‘grounded’ nature of expertise typical of ML, as elaborated above.

Across Aesthetics, Ethics and Politics

Traditionally, the dominant focus of ML degrees has been on aesthetic codes, and this remains the case to this day. However, ML degrees nowadays also readily extend into other realms, especially those of politics and ethics in the broadest of senses. This reflects ML’s place in the larger field of contemporary ‘critical humanities’, which sit astride traditional humanist ideals and a more modern orientation towards enlightened and progressive social critique. These two interlinked orientations are key to understanding what a degree in ML seeks to develop in its graduates beyond turning them into professionals and experts with specific sets of functional skills and specialist knowledge. Indeed, one of the main values of a degree in ML lies in a very specific kind of personal development of the students themselves (as human and social beings) by encouraging them to use the wealth of cultural resources and intellectual perspectives to which they are given access in their studies to develop specific means of understanding the world around them and of themselves in this world.

Old-fashioned as this might sound, it both complements and gives greater purpose to the other key dimensions of the degree discussed above – those of communicative competence, professional skills, specific areas of expertise, and epistemologically distinctive forms of understanding. In other words, it remains important to retain the humanist ideal of ML as a degree that develops students as ‘persons’ in an all-round way – as Classics traditionally used to do, but in ways that are less elitist, explicitly non-universalist, and arguably better adapted to a diverse and rapidly changing world; which is not to say that ML do not have their own inherent biases, such as, say, ‘methodological nationalism’, which they must work harder not to fall prey to.

In Sum

ML should maintain unity of purpose as a sector of higher education and academic research but embrace diversity of approach, with different institutions developing their ML unit and degree(s) in potentially very different ways, possibly targeting entirely different categories of students, while building diverse constellations of expertise and a wider range of educational objectives. This must go hand in hand with being much clearer about the professional and disciplinary complexity of the sector, including the vital epistemological and pedagogical interconnectedness and interdependency of its various aspects and parts.