Directed Motivational Currents and Language Education. Christine Muir
1 Key Threads in the Field of L2 Motivation Research and the Emergence of Directed Motivational Currents
In the 60 years since the emergence of the new field of second language (L2) learner motivation (cf. Gardner & Lambert, 1959), the field has amassed a rich and diverse history. Multiple new perspectives and ideas have been translated into the field from other disciplines. Each has shed new light and offered fresh perspectives on our understanding of L2 motivation, and some have even pushed us to re-evaluate existing knowledge in light of these new contributions. Dominant perspectives have risen to the forefront and have driven the direction of the field throughout clear periods of its history (see Dörnyei & Ushioda, 2011), yet, instead of overriding that which went before, each has opened new spheres of investigation and contributed unique insight.
The field of L2 motivation research currently finds itself at an exciting juncture. Over the past two decades it has been revitalised with the broad adoption of a complexity approach to research and understanding, and this has posed possibilities equally as captivating as the magnitude of the challenges that have arisen alongside. Complex dynamic systems theory (CDST) has forced us to re-evaluate what it is we think we know about L2 motivation, and to revisit the methods we employed to reach these conclusions. Concurrent with this has been the emergence of several new strands of research, the reconceptualisation of our understanding of L2 motivation rooted in ideas of the self being key among them. It is no exaggeration to say that the challenge for beginning PhD candidates aiming to become familiar with the full history of the field is becoming increasingly daunting: Research output is continuing to grow year on year (Boo et al., 2015).
Presenting a full overview of the field of L2 motivation research within a single introductory chapter – such is the space I have available here – is quite simply an impossible task. Happily, it is also a redundant one, as excellent overviews detailing the development of the field as a whole can already be found elsewhere (see e.g. Boo et al., 2015; Dörnyei, 2019a; Dörnyei & Ryan, 2015; Dörnyei & Ushioda, 2011; Lamb, 2017). Instead of offering a chronological overview of the field’s development, in this chapter I focus instead on key ideas and research strands currently dominant, included because they are best able to situate understanding relating to the emergence of directed motivational currents (DMCs) and the findings that I explore throughout this book. This has naturally led to a highly selective narrative, which will doubtless exclude areas that some readers would argue to be of critical importance. I defend myself against any potential criticism in this regard by foregrounding this primary purpose.
I begin the chapter by introducing in more detail the ‘complexity turn’ the field continues to experience, before going on to highlight the impact that this has had on our methodological choices and decisions. I then discuss the reconceptualisation within the field towards an understanding of self, in doing so reviewing research on possible selves, the L2 motivational self system (Dörnyei, 2005, 2009b) and vision. I go on to overview key findings and ideas in the literature investigating language learner self-concept, learner emotions and several aspects of group-level investigation. I conclude the chapter by tracing the emergence of directed motivational currents (DMCs) and by highlighting their wider significance.
From Macro to Micro Perspectives: Unavoidable Complexity
In nearly all respects, the ideas in this section underpin all those that I subsequently go on to explore in this chapter (and, indeed, throughout this book). Throughout the 1990s, concurrent with the absorption into the field of a broad range of cognitive theories (including, for example, self-determination theory, Deci & Ryan, 1985, and the notion of self-efficacy, Bandura, 1977a, 1997; for further see Dörnyei & Ushioda, 2011), the field began to adopt an increasingly situated approach to research. No longer was research interest dominated by the investigation of the motives and attitudes of collective groups of language learners: a newly emerging focus was concerned with understanding the motivations of specific learners, in specific classroom contexts.
If one were to observe a learner in any classroom, for any length of time – even if only over the course of a single lesson – it would not be possible to do otherwise than acknowledge the norm of motivational change: of ‘motivational flux rather than stability’ (Ushioda, 1996: 241). This narrowing focus brought out from the shadows a level of complexity which – while it had, of course, always been there – could now not be ignored (Larsen-Freeman & Cameron, 2008). What followed was an inescapable acknowledgement, awareness and focus not only on motivational change but on the complexity of the innumerable, interlinked factors affecting language learning and teaching. Larsen-Freeman described this recognition in a seminal paper:
Progress in understanding SLA will not be made simply by identifying more and more variables that are thought to influence language learners. We have certainly witnessed the lengthening of taxonomies of language-learner characteristics over the years, and we doubtless will continue to add to the lists. Schumann (1976) mentions 4+ factors, by 1989, Spolsky notes 74. However, it is not clear that we have come any closer to unraveling the mysteries of SLA now than before. If SLA is indeed a complex nonlinear process, we will never be able to identify, let alone measure, all of the factors accurately. And even if we could, we would still be unable to predict the outcome of their combination. (Larsen-Freeman, 1997: 156–157)
The emerging tradition developed understanding of motivation for the first time beyond that of a stable individual difference factor (Dörnyei & Ushioda, 2011; see also Dörnyei, 2017). In this current book, exploring aspects of long-term L2 learner motivation, this recognition is key: not only are different individuals guided by different motives, but these motives should likewise be expected to evolve over time (Ushioda, 1998, 2001). Several new motivational frameworks were proposed during the decade prior to the new millennium (e.g. Dörnyei & Ottó, 1998; Williams & Burden, 1997); however, none gained widespread prominence. Even their most complex iterations failed to capture the full complexity of the classroom experience and the dynamicity of the motivational factors affecting any L2 classroom context (Dörnyei, 2005, 2009a). An even more radical reframing was needed.
The emerging complexity perspective (de Bot et al., 2007; Larsen-Freeman, 1997, 2002; Larsen-Freeman & Cameron, 2008) provided exactly this. Having already exerted a considerable effect in the natural sciences (as Larsen-Freeman reports, some describe it as having ‘shaken science to its foundation’, see Larsen-Freeman, 1997: 142), the scene was set for it to evoke a similar effect on the field of SLA. As Hiver and Al-Hoorie (2016: 743) have argued, not only has a recognition of complex dynamic systems theory (CDST) become indispensable for furthering our understanding of L2 motivation, it is inescapable: CDST has become ‘an integral part of empirical research’, having reached ‘critical mass’ across multiple strands of SLA. Indeed, CDST has even been positioned as marking the ‘coming of age of SLA research’ (Ellis, 2007: 23).
To provide a fuller basis for discussion throughout this book, it is worth briefly overviewing some key concepts. As Larsen-Freeman and Cameron (2008: 25) explain, ‘an important feature, perhaps the most important feature, of complex systems is change’. All elements of a complex system are continually in flux, moment-by-moment change occurring in tandem with semester-by-semester and decade-by-decade change. Change is continually occurring, therefore, over multiple different timescales (cf. de Bot, 2015). A key implication is that traditional notions of linear cause and effect cease to offer up any inroads. As Larsen-Freeman and Cameron describe – and as Larsen-Freeman’s quote at the start of this section also alludes – ‘To be able to predict behavior, we would need to know absolutely accurately every small detail of the starting state, called its “initial conditions”’ (2008: 57). Yet, the complexity of not only the classroom environment but also the rich tapestry of experiences each learner brings with them into the classroom, of course relegates this to the impossible.
Having emphasised the centrality of change, complex systems can nevertheless settle into attractor states and experience periods of relative stability. As Hiver describes, systems are not ‘attracted’ towards attractor states in the traditional sense of the word. Rather, attractor states ‘are critical outcomes that a system evolves toward