When setting out, this thesis aimed to challenge assumptions made by the literature surrounding quantitative methods learning-teaching. Policy initiatives attempting to solve the skills deficit present the learning-teaching of QM(s) as an unproblematic transfer of knowledge from staff to students. Whilst the academic literature has challenged this model of the unproblematic transfer of knowledge through identifying a series of
obstacles faced by students and staff, it nevertheless remains routed in a series of its own assumptions. Firstly, frequently the learning-teaching of QM(s) is understood as being an activity involving only human actors, specifically staff and students (as seen in Garfield’s, 1995 principals). Secondly, much of the literature focuses only on the activities occurring within a single module (Strangfeld, 2013; Becker et al., 2006; Folkard, 2004),
characterising these interactions through reference to predominantly abstract space(- time) (i.e. Meletiou-mavrotheris, 2004; Onwuegbuzie & Wilson, 2003; Garfield, 1995). Finally, little cross disciplinary discussion occurs (Parker, 2011; Wagner et al., 2011), despite the literature’s quest for universal best practice (Garfield & Ben-Zvi, 2007). These assumptions are pervasive across the quantitative methods learning-teaching literature, and highlight a gap in the literature for research challenging these assumptions, which offer the potential to provide new insights into quantitative methods learning-teaching.
To begin to challenge these assumptions and address this gap in the literature, an ANT sensibility, supplemented by Harvey’s three spaces, was selected through which to re- examine the nature of QM(s) within Higher Education Social Science disciplines. The thesis sought to answer three specific research questions:
1) What were the actor-networks that made up QM(s)?
2) How were these networks performed, conceptualised, and created by actors? 3) How did these performed actor-networks vary across disciplines?
Providing an initial step towards answering the second and third research questions, Chapter 4 began by presented the conceptions of QM(s) as located within actors’ words, the curriculums, and on participants’ concept maps. Here, in contrast to the literature, a multiplicity of QM(s) character was reported. While QM(s) were often found housed within similar skills modules, the kinds of techniques found within these modules varied across disciplines, particularly after the first year of undergraduate study. Similarly, although most participants did include reference to QM(s) on their concept maps,
variations emerged according to level of study and discipline. Furthermore, when examining the variety of words used in association with QM(s), QM(s) was found to occupy multiple positions of agency, ranging from a passive tool to a mysterious beast. Working with this assumption of QM(s) as having an inherently multiple character, Chapter 5 and 6 mapped out the actor-networks that performed QM(s) within modules and disciplines, together answering the first and second research questions. Within classroom learning-teaching environments, actor-networks were assembled to perform two key beliefs about the nature of QM(s): that QM(s) was a linear and fixed sequence of knowledge and that QM(s) were learnt through doing. Worksheets and handouts were enrolled by staff into these actor-networks to reinforce these beliefs, however these actors, as well as correct answers, served to translate these beliefs into a characterisation of QM(s) as a passive, linear activity of completing tests. Alongside this, the role of software was also examined. Through forming a hybrid actor with software, QM(s) exhibited a mischievous side, fracturing the linear performances found elsewhere into an iterative process of errors, whereby users engaged in a process of tuning to allow QM(s) to speak out through tables of outputs. Unlike the unproblematic transfer of knowledge characterised within policy strategies, the performances of doing QM(s) here were non- linear, interrupted by errors and the hidden knowledge of staff and students.
From these performances, Chapter 6 presented a detailed account of how these actor- networks varied across disciplines – answering the third research question –
supplementing comments made throughout the proceeding chapters. For Criminology, QM(s) was found as trends of crime data, and while valued, was viewed with scepticism and caution. In Geography, along with providing trends of data through space, QM(s) was tied to measures, which were often embodied physically. Working with these
infallible numbers, QM(s) in Physical Geography grew in strength, valued for its ability to provide reliable answers and predictions to environmental questions. Valorising QM(s) for these qualities of fixity and robustness however proved problematic when transferred into Human Geography. While QM(s) was successfully enrolled into sub-disciplines that could provide similarly infallible numbers – such as transport geography – for those where data resembled the surveys used by Criminologists, QM(s) was regarded warily. For Psychology students, QM(s) strong presence was often a surprise; nevertheless QM(s) was highly valued by the discipline given its ability to provide various markers of significance. Although QM(s) occupied a similarly powerful position to that found within
Physical Geography, here this power was drawn not from the numbers used, but from the conventions built up and maintained by the discipline. Furthermore, while QM(s) corresponded amicably with other methods in Geography, in Psychology, QM(s) was expanding, changing the kinds of knowledge valued by the discipline, pushing qualitative research methods to the boundary of the discipline. Finally, in Economics, QM(s) was found to be a blend of mathematics and econometrics, serving a vital role in decision- making. Unlike the other disciplines, here QM(s) was not sold as the best method; instead it was understood as the only method through which their quantified world could be translated. Given this, and the techniques used, although valued, QM(s) was
understood as partial, with optimality, not significant results, being the goal.
Having outlined these actor-networks, Chapter 7 turned to considering QM(s) as an actor responding to change. Across the disciplines studied, QM(s) face a number of threats to its current characterisation, in this chapter new software and new techniques emerging in response to these threats were examined. The growth of R and Bayesian statistics was presented as representing a potential shift in the characterisation of QM(s), from one of fixity to one of fluidity, where the researcher occupied an increasing position of power. In addition, this chapter also outlined two responses to growing student numbers, and the associated mixed abilities/backgrounds of students. In the first, QM(s) moved out of the learning-teaching environments it had been observed within here and colonised new informal space(-times), or repurposed current formal space(-times). In the second, the future of broad QM(s) modules was questioned, with a potential fracturing of modules occurring, with new modules emerging to house different groups of content. Although the extent to which these murmurings of change will come true is unknown, they draw attention to QM(s) position as a changing actor-network, not simply one whose identity remains fixed.
Drawing these characterisations together Chapter 8 reflected on the implications of these characterisations for learning-teaching. In this chapter, it was discussed how a unity to QM(s) character was achieved through a process of othering. Although adopting its own language, notation and module design, served to protect and cultivate QM(s) identity as singular, being boxed up in this way had repercussions when attempting to integrate QM(s) with disciplinary knowledge and theory. For students, QM(s) was understood as having a one directional relationship with theory – passively testing and generating
theory. For staff, however, QM(s) was understood in relation to other methodologies. Together these methods were understood as actively shaping the kinds of knowledges that were valued and produced, and vice versa. Given the actor-networks presented in the proceeding chapters, a missing actor in these representations was identified – Data. This chapter commented that re-packaging QM(s) relied upon restoring the voice of data (whose power was seen in Chapter 6), as well as strengthening the link to theory. It ended by suggesting that the uptake of QM(s) relied not on the selling of the methods, but instead a valuing of the kinds of insights QM(s) could give.
Given the limited use of ANT within educational research, in Chapter 9 a reflection was provided on the process of working with ANT here. This process was characterised by two stages, the first where the researcher becomes enrolled into ANT, and the second where the researcher enrols ANT into their own research setting. In addition to this, the use of concept maps was evaluated, with specific attention given to examining their potential for working with ANT.
While this project has provided a first step in reimagining QM(s) learning-teaching, the actor-networks presented here are partial representations. Further research, both within and outside of a UK setting, is needed to gain an understanding of the role of the
institution and wider cultural conceptions in these actor-networks. In addition, while four Social Science disciplines were investigated here, studying additional disciplines,
particularly across Arts and Sciences, would enable these actor-networks, particularly the enrolment mechanisms and characterisation of QM(s), to be compared. Similarly, while this would give an understanding of QM(s) within Higher Education settings, further research could also use this approach to examine the performances associated with QM(s) at different educational levels – particularly of interest given the introduction of new syllabuses at GCSE and A-Level – allowing the nature of QM(s) to be understood through educational time(-spaces). Equally, further research is needed to identify those concepts, i.e. representativeness or bias, that are easily taken up by students that form their QM(s) mindset, which is transferred onto other kind of research methods. In drawing attention to this, QM(s) power in education and research settings can begin to be traced out. Finally – as touched up on Chapter 8 – by manipulating the actor- networks described here further studies could evaluate the potential for new
configurations of QM(s) learning-teaching, and their abilities to offer new ways of enrolling students in valuing the questions QM(s) are able to assist with.
Overall, this thesis has made an empirical contribution to the field of quantitative methods learning-teaching working with ANT to examine the everyday performances of QM(s) across four Social Science disciplines. By examining these performances, this research has brought to light new non-human actors, such as worksheets, choice diagrams, correct answers, that have been previously overlooked within the literature – see Section 2.2. These actors serve to translate the character of QM(s) into one
associated with passive following and obtaining correct answers, shifting attention away from the process of doing QM(s). Furthermore, through comparing different disciplines, data was found to be a powerful actor in controlling disciplinary narratives of the
character and role of QM(s), with the use of survey data giving rise to a more partial and sceptical characterisation of QM(s). This account of the role of non-human actors within QM(s) learning-teaching provides a contribution to the growing body of research in educational contexts (Gorur, 2013; Fox, 2009; McGregor, 2004) arguing for the importance of studying these often overlooked, non-human actors.
Furthermore, through comparing the performances of QM(s) learning-teaching this research provides a response to calls for greater cross-disciplinary discussion of research methods (Wagner et al., 2011), and exploration of interactions across, and outside of, module boundaries (Parker, 2011). In particular, the account of disciplinary differences in the characterisation of QM(s) presented within this thesis represents a serious challenge to the drive to identify, and implement, a universal best practice of QM(s) learning- teaching pedagogy. Acknowledging that, this thesis provides empirical evidence to support the calls for greater national and international cross-disciplinary discussion.
In addition to tracing through actors and their roles in QM(s) learning-teaching networks, this thesis also began to consider how the character of QM(s) is currently changing – a theme overlooked by the literature - and responding to attitudes towards quantitative material, rising student numbers and the growth of new techniques and data, and how the traced actor-networks may be manipulated to produced new understandings of QM(s), through reinforcing the link between QM(s), data, and theory.
Finally, this thesis contributes to methodological discussions surrounding the doing of ANT, illustrating how ANT can be successfully combined with Harvey’s three spaces. Moreover, this thesis also provides a relatable account of working with ANT in an educational research setting, helping to, somewhat, demystify the process and provide strategies for the novice ANT researcher. On the whole, by challenging the assumptions made by the literature over the actors involved within QM(s) learning-teaching, the characterisation of space, the popularity of quantitative methodologies, and the lack of cross disciplinary analysis, this thesis provides an initial step in getting to know QM(s).