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Enhancing Learning in the Social Sciences

ISSN: (Print) 1756-848X (Online) Journal homepage: http://www.tandfonline.com/loi/rhep16

‘DiscoverQuants’: Integrating QuantitativeMethods (QM) and Substantive Teaching for FirstYear Undergraduate Sociology Students

Karen Bullock, Robert Meadows & Ian Brunton-Smith

To cite this article: Karen Bullock, Robert Meadows & Ian Brunton-Smith (2014)‘DiscoverQuants’: Integrating Quantitative Methods (QM) and Substantive Teaching for FirstYear Undergraduate Sociology Students, Enhancing Learning in the Social Sciences, 6:2, 6-20

To link to this article: http://dx.doi.org/10.11120/elss.2014.00033

© 2014 A. Rosie, The Higher EducationAcademy

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RESEARCH ARTICLE

‘DiscoverQuants’: Integrating QuantitativeMethods (QM) and Substantive Teaching forFirst Year Undergraduate Sociology Students

Karen Bullock, Robert Meadows & Ian Brunton-Smith

Department of Sociology, University of Surrey, Surrey, UK

Corresponding author:

Dr Karen Bullock, Department of Sociology, University of Surrey, Guildford, Surrey GU2 7XH, UK

Email: k.bullock@surrey.ac.uk, Phone: +44 (0) 1483 686979

Abstract

This paper considers the rationale for, design and outputs of a project, based at the

University of Surrey UK and funded by the Economic and Social Research Council (ESRC),

which sought to integrate aspects of teaching substantive and Quantitative Methods (QM)

teaching across first year sociology undergraduate programmes using a blended approach.

The paper considers the nature of concerns regarding teaching QM within social science

undergraduate programmes. It goes on to describe the rationale for this project, its

design and its primary outputs. We consider a range of data related to student attitudes

towards studying QM at university as well as their perspectives on the project and the

implications for practice.

Keywords: Quantitative methods, integration, blended learning

Introduction

This paper considers the theoretical basis, design and implementation of a series of

curriculum innovations aimed at integrating substantive teaching and quantitative methods

(QM) for first year undergraduate students. It was developed and implemented by the

Sociology Department, University of Surrey, UK and formed part of a wider Economic and

Social Research Council (ESRC) ‘curriculum development initiative’ which sought to build

capacity in QM across the social sciences at the undergraduate level (ESRC 2012). Our

experiences relate primarily to QM integration within sociology. However, our findings

have implications for QM integration across other social science disciplines. This initiative

responded to concerns that social science programmes have failed to prepare students in

QM. Such concerns are neither new nor confined to the social sciences (Williams et al.

2008). Indeed, the teaching of research methodology in British sociology has been under

scrutiny since the expansion of the discipline during the1960s (Burgess & Bulmer 1981).

The ESRC, founded in 1965, expressed the need for a higher level of numeracy within the

social sciences as early as 1969 (Platt 2012). Government anxiety about the (apparently)

weak numerical skills of social scientists should be understood as part of a wider concern

about the number of scientifically qualified personnel and a shortage of students interested

© 2014 A. Rosie, ELiSS, Vol 6, Issue 2 (July 2014)

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in studying mathematics and the physical sciences (Williams et al. 2008). However,

concerns have perhaps been expressed more commonly – and more forcefully – since the

turn of the century (MacInnes 2009).

This paper begins by setting out the nature of concerns pertaining to the teaching of QM

within social science undergraduate programmes and some of the consequences for

students. We then set out the rationale for our project in light of these debates along with

the project aims and objectives. We then turn to project outputs and outcomes. Our aim is

for others to learn from our experiences. Through examining a range of qualitative and

quantitative data (focus groups, pre- and post-questionnaires, and web usage figures) and

linking our findings to those of others we also hope to contribute to the knowledge base of

students’ attitudes to and experiences of studying QM. In considering the outputs and

findings from our project, we hope to modestly advance pedagogical understanding of

innovation in teaching QM.

Social science, QM and the undergraduate curriculum

A number of problems have routinely been identified in undergraduate QM provision for

social scientists. In a ‘baseline study’ funded by the Higher Education Academy (HEA)

Williams et al. (2004a) found that whilst QM was widely taught, standards were mixed

and barriers to effective teaching – including level of language, the nature of data

used, expectations of staff, quality of teaching and a shortage of qualified, motivated

teachers – were identified. In his review of British QM teaching MacInnes (2009) drew

attention to the low priority and low status which has been accorded to teaching QM; the

routinised and basic nature of provision; and, the lack of integration into the wider

curriculum. Ultimately, he surmises, social science students in the UK graduate with only

a narrow base of QM skills, little confidence in applying them and few go on to make use

of their skills. This situation, as Markham (1991, p464) succinctly noted some time ago,

“is good for neither students nor society”. It is not good for students Markham argued

because it limits their intellectual, social and career development, themes which resonate

through recent reports by the UK government, funding bodies and learned societies

(MacInnes 2009, British Academy 2012, Nuffield Foundation 2012). Many social science

students, suggests the Nuffield Foundation (2012, p1), “leave university with inadequate

quantitative skills and expertise in their applications to their chosen areas

of study”.

There are two types of explanation regarding why social science has ‘failed’ to prepare

students in QM. The first set emanates from factors linked to student predisposition and

preparation and the second from factors linked to university resources, culture and

traditions. Student experience of QM before they commence their degrees is somewhat

mixed, but generally limited; many have not confronted maths for some time; and,

they may have had poor experiences of learning it at school (Murtonen & Lehtinen 2003,

Murtonen 2005, Williams et al. 2008, Falkingham et al. 2009, MacInnes 2009,

Falkingham & McGowan, 2011). This situation will inevitably affect students’ confidence

to engage effectively with QM at university. Indeed, students may well select social

science subjects precisely to avoid dealing with numbers altogether (Markham 1991,

Williams et al. 2004a, Williams et al. 2008, Falkingham & McGowan 2011, Nuffield 2012).

This situation may be compounded by discipline culture, traditions and resources. The

marginalisation of QM teaching is in part explained by demographic and intellectual trends

associated with the development of the discipline in the second part of the twentieth century

(see Burgess & Bulmer 1981, Bulmer 1989, Williams 2000, Williams et al. 2004b, Platt 2012).

Early cohorts of sociology lecturers had themselves little training in QM, a preference for

theoretical (or qualitative) work and (perhaps) animosity towards QM. This situation

Bullock et al.

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inevitably interacts with the resources made available for QM. In addition, requiring higher

staff–student ratios and bespoke teaching material, good QM teaching is expensive and

vulnerable to cost-cutting exercises (MacInnes 2009). Taken together the result is, as

MacInnes (2009, p44) put it, that “the teaching base is fragile”.

The response then has been to seek to underpin the foundations of QM teaching. This is

where our project fits into the landscape of QM teaching. In partnership with the Higher

Education Funding Council for England (HEFCE) and the British Academy, the ESRC

funded twenty ‘curriculum innovation’ projects in early 2012. Our project sought to

integrate aspects of QM and substantive teaching for year one sociology undergraduates

using a ‘blended learning’ approach. The following sections explain the theoretical basis

of and the rationale for our project in more detail.

Project rationale, aims and objectives

The Department of Sociology at Surrey has a long history of teaching QM which is firmly

embedded in its undergraduate programmes. However, our project was informed by the

observation that increasing the amount of QM modules and skills taught to social

science students does not guarantee that they will develop and apply them in practice

(Markham 1991, Atkinson et al. 2006). Instead, research has revealed that teaching QM in

substantive courses helps students to learn QM skills (Bridges et al. 1998, Atkinson et al.

2006, Howery & Rodriguez 2006, Wilder 2009). Atkinson et al. (2006) for example,

reported on the effectiveness of a research module taught in a large introductory

sociology class. Integration helped students learn to interpret sociological data and

increased their awareness of the substantive issues which in their exemplar was race

and gender inequality (Atkinson et al. 2006). Integration of this sort is rare in practice

(Sweet & Strand 2006, MacInnes 2009, Wilder 2010) and concerns have been raised that

QM skills are not reinforced across the curriculum, are nearly always confined to specialist

courses or components of wider methods courses, and that the amount of time spent on

honing QM skills compared to other areas of the curricula is limited (MacInnes 2009). This

gives students the impression that social science is about essay writing and evaluating

arguments and that QM is neither important nor relevant to the discipline (Bulmer 1989,

Falkingham et al. 2009, MacInnes 2009). In designing the project we further drew on the

observation that sociology – which asks questions about the social world and engages

with issues that are relevant, memorable and meaningful for students (Atkinson et al. 2006,

Sweet & Strand 2006, Wilder 2010) – is a ‘natural’ subject for blending QM and substantive

teaching and that ideally QM and substantive teaching would be integrated early on

(Atkinson et al. 2006, Wilder 2010). Our project also built upon previous studies which

had prioritised and attempted ‘integration’. Previous initiatives – which in some instances

have involved a separate department providing statistical training to sociology students

and staff (e.g. Falkingham et al. 2009) – have interpreted ‘integration’ loosely. Integration,

we suggest, must be conceived as more than implementing a series of strategies linked

by a common goal. In its truest sense, integration involves weaving and combining QM

and substantive teaching. It might involve redesigning modules to, for example,

incorporate quantitative exemplars into substantive teaching and vice versa, using data to

structure sessions or enabling students to conduct quantitative analysis as part of their

assessment or the development of new modules and courses such as those that embed

enquiry-based learning into the curriculum. Our project also acknowledged the need to

cope with diversity within the student body. Some students might be confident in

substantive issues but anxious about QM, or vice versa. Indeed, many integration efforts

to date have assumed students are a homogenous group, yet students come from

different backgrounds, have different levels of experiences of QM and employ different

learning styles.

‘DiscoverQuants’

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Developing the integrated curricula

The curriculum innovation focused (primarily) on the first semester of the first year and

was built around grounding ‘exemplars’ – an existing point of contact between a

substantive module and QM – from substantive modules into QM teaching. These

exemplars were determined on the basis of discussion between QM lecturers and those

delivering substantive content. We identified six exemplars topics (Durkheim and suicide;

Weber and Protestant ethic; stop and search; inequalities in health; poverty; age and crime).

This enabled the 12-week QM module to spend up to two weeks on each exemplar, as

appropriate. Once these exemplars were identified, two further changes were made: first,

the QM module (which is compulsory for all students on all our degrees) was adapted so

that it was delivered using those exemplars; second, the substantive courses incorporated

material taken directly from the QM module.

To illustrate, principally discussing his notion of ‘social facts’ and using his (quantitative)

study of suicide as a way of exploring further his approach to sociology, our sociological

theory module has two hours dedicated to Durkheim’s rules of sociological method. The

sociological theory lecturer made two changes to the session. First, the existing slides were

supplemented with materials from the online resource (see below). Second, the lecturer

made reference to how Durkheim’s analysis will be explored further in the QM module.

The relevant QM session then used data on suicide, and Durkheim’s analysis, to meet one

or more of its learning objectives.

Implementing a ‘blended learning’

environment – ‘DiscoverQuants’

Reflecting the observed need to account for the differing backgrounds, experiences and

learning styles of students we also developed a ‘blended learning’ environment. Working

with a professional web designer we created an interactive online tool. Designed to

provide students with an intuitive framework to structure their learning, the online tool

was organised around the revised QM sessions and included material which covered both

the QM and substantive area. For each QM topic we incorporated a set of revision notes,

worked examples (using the relevant exemplars from the substantive courses), definitions

of key terms, ‘quizzes’ (at different levels of difficulty), data sets and a range of other

resources including relevant readings and videos. To engender greater engagement with

the online tool and add a degree of feedback to students on their progress, a number

of related strategies were used. Aspects of ‘gamification’ – to visually display to users

the extent to which they had used the tool and whether revision questions had been

answered correctly – were incorporated throughout the resource. Students were

encouraged to ‘discuss’ any issues they had related to the quantitative methods on an

online forum integrated within the resource which was monitored by a nominated

member of staff who could ‘intervene’ to answer queries posed by students as necessary.

Students could also personalise the resource with avatars. The tool can be seen at

www.discoverquants4all.surrey.ac.uk/.

To further emphasise to students how QM is integrated throughout their degree,

materials from the online tool were incorporated in the lecture slides in both substantive

and QM lecture/tutorial sessions. Students were encouraged to use the online tool but

our approach did not rely on the students accessing the blended learning environment as

the same material was included in the lectures and students were able to meet the

learning objectives of both substantive and QM modules without ever accessing the

online materials.

Bullock et al.

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Generating commitment from staff

Since previous research has demonstrated that resistance of teaching staff – who have

different perspectives, preferences and priorities – can be a major barrier to attempts to

integrate QM and substantive teaching (Howery & Rodriguez 2006, Wilder 2010) it was

recognised that commitment from all relevant lecturers was essential. Our approach was

to stress that the curriculum innovation required minimal changes to the teaching of

substantive modules: we were principally looking to adapt existing lecture slides so that

they reflected screenshots/materials from the online learning resource; module learning

outcomes, content, assessment and feedback would all be unchanged; and lectures would

not have to be significantly re-written. The project team were also available to assist

colleagues in making the necessary adjustments to further reduce the burden on their

time, if necessary. All year one teaching staff supported the aims of the project and agreed

to coordinate the first semester teaching in accordance with the plan. As noted, this

sympathy should not be assumed, a point we return to in the conclusion. Even so, like any

significant curriculum restructuring, the project was not without challenges. During the

project the QM lecturer who was involved in the preparation of the bid left their post as did

their replacement, with obvious implications for ensuring continuity of provision and

motivation to incorporate the curriculum changes. There continue to be challenges

engendered by the ‘fragility’ of the QM teaching base and a buoyant labour market and

opportunities for QM skilled staff. Nevertheless, the project was enacted in full for the first

semester of the 2012/13 academic year, with students on our undergraduate Sociology,

Criminology and Sociology, and Sociology Culture and Media degrees all exposed to the

new integrated curriculum.

Student perceptions of QM, teaching and the project

In order to inform its development and monitor implementation, understand outputs and

outcomes and elicit lessons learnt, a range of data were collected in the course of the

delivery of the project. First, a series of focus groups were conducted with different

cohorts of students and at different stages of the implementation of the curriculum

innovation. One focus group was conducted with third year students as part of the

development of the proposal to the ESRC. Two focus groups were carried out with the first

year cohort prior to teaching and two focus groups were carried out with the same cohort

post teaching. These facilitated in-depth discussions of student attitudes to QM, their

experiences of learning it, views on the online (and other) resources (as relevant) and

views about the relationship between substantive and QM sociology. Focus groups were

transcribed and analysed thematically by the project team. Second, students filled in

attitude questionnaires before and after completing the course. These questionnaires

were distributed to students during the QM lecture and covered issues including attitudes

to studying QM, self-assessed ability and relevance of QM to their studies and career

plans. Fifty students completed the pre-questionnaire and 32 the post-questionnaire. The

post-questionnaire also asked for free text feedback on the DiscoverQuants website and

how it was used by students. Third, we examined website usage data to inform discussion

on how the students were using it within their studies.

The observational nature of the data sources means we cannot make direct claims about

whether the project led to changes in students’ perceptions of QM, their confidence and

competence or their assessments of the linkages between QM and substantive teaching.

Instead, taken together we use these data to descriptively consider how our students

perceive QM, how they understood the relationship between QM and substantive modules

and how the online tools were utilised. Since we are not using these data to consider

‘DiscoverQuants’

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change over time the focus group responses have been merged together and we do not

distinguish between pre and post in the discussion that follows.

Student attitudes towards QM

The pre-course questionnaires asked students a number of questions to reveal their

attitudes towards QM. To start we summarise student responses to questions related to

their overall interest in studying QM at university and their confidence with statistics. A

mixed picture was evident when considering whether students had a favourable view of

statistics, with similar proportions stating that they agree, disagree and neither agree nor

disagree with the statement ‘I will like statistics’. However, only 3 in 10 students disagreed

with the statement ‘I am interested in learning statistics’. We have seen that interest and

confidence in studying QM at university is shaped by limited exposure to mathematics

before students commence university (Murtonen & Lehtinen 2003, Murtonen 2005,

Williams et al. 2008, Falkingham et al. 2009, Falkingham & McGowan, 2011) and our

students were asked to self-assess their competence in mathematics in the pre-course

questionnaire. Only a minority of students were either very confident or very

unconfident in their ability, with the majority reporting what might be seen as average

levels of confidence.

Our students were also asked questions related to fear of QM, following extant research

on the subject which has found that many students are fearful (Williams et al. 2008,

Falkingham et al. 2009, Falkingham & McGowan 2011). Near equal numbers of students

agreed and disagreed with the statement ‘I feel insecure when I have to do statistics

problems’ and just under half agreed with the statement that ‘I am scared by statistics’

indicating a certain degree of apprehension amongst our sample. However, we also find

that there are clear minorities of students who are confident and many who are somewhat

ambivalent. With only half of students agreeing with the statements ‘statistics is a

complicated subject’ and ‘I find it difficult to understand statistical concepts’ and just over

half agreeing that ‘I will make a lot of errors in statistics’ many students (though again

by no means all) do not find QM all that challenging. The challenge of QM was also a

theme that arose in the focus group data. Indeed, noting that many had not studied

mathematics recently respondents certainly expressed concerns about studying QM

at university. That said some were not put off by the challenge, one focus group

respondent stated:

It’s a little bit intimidating but I think it will be like a good kind of challenge,

like for me it’s one of my most challenging areas . . . I’m not so strong with

numbers but I think it will be a good kind of challenge rather than scary, a little

bit intimidating.

Some of this might link to the nature of QM teaching. Reflecting Falkingham & McGowan

(2011), who found students saw lectures as the least favourite method of delivery, our

focus groups revealed something of a preference for small group teaching, discussion and

practice of QM. One student noted “I prefer working in a smaller group ‘cos I think if

you’re into, sorry, interacting with people then, then there’s like a conversation that sticks

in my head a bit more than someone just telling me”. Our experience demonstrates that

discussion and practice can be facilitated via online tools. Workshops, online workbooks

and lecture material could all be easily embedded into such resources giving students

greater opportunity to practice.

A further set of issues that might shape student attitudes towards studying QM at

university is student perceptions of the nature of the discipline that they are studying and

its content. As noted, students may well select social science programmes because of their

Bullock et al.

© 2014 A. Rosie, ELiSS, Vol 6, Issue 2 (July 2014)

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dislike of numbers and preference for essays based content which they believe will

characterise them (Markham 1991, Williams et al. 2004a, Williams et al. 2008,

Falkingham & McGowan 2011, Nuffield 2012). For example, Williams et al. (2004a) found

that three quarters of sociology staff who completed their survey thought students

selected sociology degrees precisely to avoid numbers. In the pre-course questionnaire

our students were asked ‘If you had a choice, how likely is it that you would have

chosen to take any course in statistics’. Whilst some students clearly were keen the overall

message from our study is that most were unlikely to have proactively selected it.

Some have argued that students may not see the value of competence in QM – especially

in terms of the career opportunities (MacInnes 2009). Our pre-questionnaire data (set out

in Table 1) suggest that students generally did see the value of QM though in some areas

more than others.

Few students agree that ‘statistics is worthless’, only 1 in 10 students disagree with the

statement ‘statistics should be required as part of my professional training’, fewer than

1 in 10 agree that ‘statistics is not useful to the typical profession’, whilst more than three

quarters agree that ‘statistics will make me more employable’. There was therefore a

particularly strong sense that QM was important for employability, something that also

came out in the focus groups (c.f. Williams et al. 2008). This casts some doubt on

MacInnes’ (2009) claim that students are unaware of the career advantages that flow

from QM skills. Instead, it might be that students do not intend to pursue careers that

Table 1 Value of statistics.

Statistics is worthless % N

Agree 8 4

Disagree 68 34

Neither agree nor disagree 24 12

Statistics should be required as part of my professional training

Agree 36 18

Disagree 12 6

Neither agree nor disagree 52 26

Statistics will make me more employable

Agree 76 38

Disagree 4 2

Neither agree nor disagree 20 10

Statistics is not useful to the typical profession

Agree 8 4

Disagree 66 33

Neither agree nor disagree 26 13

I use statistics in my everyday life

Agree 20 10

Disagree 34 17

Neither agree nor disagree 46 23

Statistics is irrelevant to my life

Agree 16 8

Disagree 54 27

Neither agree nor disagree 30 15

‘DiscoverQuants’

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involve QM. This is supported by Table 2, which shows the results to the question ‘In the

field in which you hope to be employed when you finish university, how much will you

use statistics?’

There is a clear minority of students who believed it likely that they would use QM in their

career but the majority seem to think it unlikely or at least are unsure.

To summarise the above, it is generally accepted within sociology that students have

limited skills in mathematics, little interest in QM and are fearful of learning it. We found

quite mixed results, with clear ‘ambivalence’ towards QM. Students do see the potential

value of QM. However, whilst there was certainly evidence of competence and enthusiasm

for QM amongst some, many were not looking forward to studying QM, probably

wouldn’t have selected it and do not seem to see their future careers in jobs that call for

QM. Perhaps it follows that students do not necessarily see the links between substantive

courses and QM, which we now turn to.

Student understanding of the links between QM and

substantive social science

Primarily considered through the focus group data, the overriding message is that students

saw the substantive and QM modules as quite separate and distinct. There were no

differences before or after the course. That said it isn’t the case that all students fail to see

the connections between QM and substantive teaching. There were isolated examples

of students seeing the links. One focus group attendee stated “I do think quantitative

methods are relevant to the theory courses and modules I attended, both sociological

and media, because quantitative methods provide you with an insight of how you could

conduct research that would produce data to agree or disagree with these theories”.

The same student went on to state that “I do remember the lecturer giving some real life

examples of how this could be used, which was useful to help see the connection”.

Another student, part of the third year focus group, also revealed that she saw connections

between the substantive and research content developing as she came to the end of

the programme.

In first and second year, I think, it was very divided, because we were mainly

looking at grand theories [. . .] in the final year when we were looking at

up-to-date data and we read articles and we look at their methods, it definitely

becomes, ‘ah, research methods, I did that’. And during revising, ‘ah I

remember that’. You do feel it’s finally coming together.

Table 2 Future directions.

In the field in which you hope to be employed

when you finish University, how much will you use statistics% N

1 not at all

2 6 3

3 25 12

4 43 21

5 16 8

6 6 3

7 a great deal 4 2

Bullock et al.

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Of course, this did not reflect on our curriculum innovation. If on the whole students did not

see clear links between the substantive and QM teaching some certainly did see links

between sociologists – and sociology as a discipline – integrating substantive content and

QM. One student stating “I suppose the point of research methods is to test the theories”.

However, even here we see a clear disconnect between perceptions of substantive theories

and QM, with an implicit suggestion from the student that research methods and empirical

data are distinct from theory generation. This artificial separation of QM (and research

methods more generally) from broader theoretical development in the eyes of students

is a worrying reflection of the broader schisms that remain in the discipline of sociology,

and demonstrate that more substantial integration is still needed, a point we return to in

the conclusion.

Usage of and understanding of the online tool

We asked students, in the survey and focus groups conducted after the project, their

views on the online tools. Student feedback and the usage data indicated that the

resources were well used and that the students found the website to be useful.

Table 3 demonstrates that there have been 1662 visits to the website, with a maximum of

219 unique visitors per month and each visit lasting on average just over ten minutes.

This suggests a significant degree of engagement with the web resource, and that the

majority of students visited the site at least once (students who had access to the resource

in October 2012 retained access to the resource in their second year as a revision tool

which explains the high number of unique visitors recorded in November 2013). We

should stress that the number of visits and length of visits were shaped by the point in the

academic year, something we will return to. As well as the extent of student usage of the

website we were also interested in the ‘depth’ of their usage, revealed in Table 4.

We can see that although there was a great deal of variance, some students were certainly

making extensive use of the website visiting multiple pages.

Table 3 Visits to the DisoverQuants website.

Month Number of individual visits Average visit duration (minutes) Number of unique visitors

Oct-12 312 14.67 170

Nov-12 266 13.52 103

Dec-12 100 14.19 65

Jan-13 308 30.54 92

Feb-13 4 4.22 3

Mar-13 1 0.00 1

Apr-13 8 27.71 2

May-13 5 4.69 5

Jun-13 3 3.61 3

Jul-13 3 0.07 3

Aug-13 26 20.29 15

Sep-13 79 6.85 47

Oct-13 156 12.25 120

Nov-13 360 16.16 219

Dec-13 31 3.87 28

Total/Mean 1662 11.51

‘DiscoverQuants’

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Students made positive comments on its functionality, looks and the nature and depth of

the content assembled. A number of student comments linked to the ‘quizzes’ embedded

in the tools, generally seen as useful for practice, especially at exam time. Some students,

albeit a minority, reported that the questions were too basic and would have welcomed

more advanced questions. Others stated that they would have liked greater feedback on

their answers on the quizzes on the online tool, rather than simply informing them whether

they were ‘right or wrong’. We suggest that solutions to this need to be considered early

on. This might be achieved through technology or through engagement from staff – via

online discussion forums, or face-to-face discussions in seminars, for example.

Whilst the online tool was viewed to be useful, the student responses in focus groups

and the free text parts of the post-questionnaire indicated they were using the website in

‘instrumental’ ways. First, they were using it when they were directed to in seminars

under supervision of tutors. Second, they were using it specifically to aid revision and

coursework. Analysis of the website usage data showed very clear peaks in usage in the

period immediately before deadlines. Indeed, Table 3 demonstrates peaks at the beginning

and end of the semester which indicates usage as the students are being introduced to

the courses and then at the end when they are being assessed. Whilst it was not intended

that the online tool should be primarily used in this way, some students certainly found the

website useful for this purpose. Third, some students saw it as a repository for lecture

notes. In a free text response to the post-course survey one student explained that it was

“useful to help understand some of the material covered in the lectures as it explained it in

a different way”. Others felt the opposite. One stated it “was sometimes a bit confusing as

Table 4 ‘Depth’ of visit.

Page Depth Visits (N) Visits (%)

<1 46 2.8

1 293 17.6

2 530 31.9

3 107 6.4

4 181 10.9

5 58 3.5

6 93 5.6

7 55 3.3

8 63 3.8

9 25 1.5

10 40 2.4

11 25 1.5

12 34 2.0

13 19 1.1

14 25 1.5

15 7 0.4

16 11 0.7

17 4 0.2

18 3 0.2

19 3 0.2

20+ 40 2.4

Total 1662 100

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the descriptions and explanations were sometimes worded slightly differently to the

lectures and the lecture notes”. It may be very difficult to shift this instrumental mindset

and orient the students towards a more rounded usage of online tools. However, raising

awareness and exposure to the online tools and what they offer in terms of breadth and

depth of context and the ways that students could use them might help in this regard.

Implications for understanding students’ attitudes to and

experiences of studying QM

We hoped to contribute to the existing knowledge base of students’ attitudes to and

experiences of studying QM. Whilst it is generally accepted that sociology students have

limited skills in mathematics, little interest in QM and are fearful of learning it, we found

mixed results. Whilst minorities of students were highly positive and confident or

negative and lacking in confidence, most students exhibited what might be best termed

as ‘ambivalence’ towards QM. Students do see the potential value of QM, most clearly for

their careers. However, most were not looking forward to studying QM, probably would

not have selected it and do not seem to see their future careers in jobs that call for QM

skills. Whilst not denying that some students lack skills and confidence, we are drawn to

concur with Williams et al. (2008) that the issue for sociology might well be less to do with

an overall numeric deficit amongst cohorts of sociology students, but more to do with a

lack of student interest in QM. Some of this ‘ambivalence’ might be to do with their

experiences of studying mathematics prior to university study and expectations of their

programmes. As noted, it is generally taken that student experience of QM before they

commence their degrees is mixed but limited; students may not have confronted maths for

some time or had poor experiences of learning it at school and students may well select a

social science degree programme precisely because of their dislike of numbers and

preference for essays based subjects which they believe will characterise undergraduate

study in sociology (and social science more generally) (Markham 1991, Williams et al.

2004a, Williams et al. 2008, Falkingham & McGowan 2011, Nuffield 2012). It seems quite

likely that current cohorts of students believe sociology is not a numeric discipline and

nearer an ‘art’ than a ‘science’ (Murtonen & Lehtinen 2003, Murtonen 2005, Williams et al.

2008, Nuffield 2012). Indeed, there may be a perception amongst schools, guidance

teachers and school students, note the Nuffield Foundation (2012, p10), that the social

sciences are an unattractive destination for students with good QM skills. Whilst

Williams et al. (2008, p1003) suggest that “the views held by present undergraduates do

not augur well for a methodologically pluralist discipline in the future, or more generally

for key numeric and analytic skills sociology graduates can bring to other professions and

occupations”, it may be that student perceptions of the subject and attitudes can be

changed in order to facilitate student engagement with QM content once they arrive at

university or to attract students who are numerically oriented in the first place. A couple

of themes are worth noting in the current context.

It is clear that some of the problem starts in schools. Indeed, there have been wider

expressions of concern about poor skills in mathematics amongst British school leavers

(Hodgen et al. 2010, ACME 2011, Vorderman et al. 2011) and (amongst other things) the

implications for developing QM skills within the university sector (MacInnes 2009, Nuffield

Foundation 2012, British Academy 2012). For the British Academy (2012, p3):

The problem starts in schools. Too many students enter higher education with

poor numerical skills, little confidence in their mathematical abilities or an

appreciation of their relevance. This is a severe handicap in developing

quantitative skills more generally.

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It may be that wider government initiatives to raise the numeracy of the whole student

population will have positive implications for attempts to raise QM capacity in

undergraduate programmes. Much more specifically, the prospect of developing links

with schools to reinforce the status of sociology as a discipline with QM content and so

suitable for students with numerical skills has been mooted (MacInnes 2009, Nuffield

Foundation 2012). This is a theme evident in the Quantitative Methods Programme

(hereafter ‘QM Programme’), an investment of some £15.5 million from the Nuffield

Foundation, the Economic and Social Research Council (ESRC) and the Higher Education

Funding Council for England (HEFCE), which aims to promote a ‘step-change’ in

quantitative methods training for UK social science undergraduates. The QM Programme

promotes institutional change, the production of quantitatively skilled undergraduates

and the creation of links between undergraduate and postgraduate training to benefit

academic research and meet the needs of the wider labour market (Nuffield Foundation

2012, p2). It aims to support QM capacity in a small number of universities with

pre-existing strengths in order to signal the importance of quantitative skills to schools,

school students, and undergraduates and to promote work with schools so students better

appreciate the role of QM in social science degrees, thus creating pathways to system

change (Nuffield Foundation 2012).

The current economic climate may start to shift student attitudes, expectations and choices.

It has been suggested that students may start to become savvier about developing skills

transferable into the job market whilst they are at university and in so doing they may

select QM, especially if the benefits of doing so are made clear to them (MacInnes 2009).

In fact, comparatively little research has unpicked students’ perceptions of the skills that

they generate from their university studies, the factors that influence their perceptions of

employability or the efficacy of methodologies in promoting optimal skills development

(Tibby 2012). However, we do know that students lack understanding of the skills

employers want (Tibby 2012) and find it difficult to translate their skills and experiences

into the language that employers value (Knight & Yorke 2003). This may represent a

particular challenge for social science students. Williams et al. (2008) found that 50% of

sociology students were unsure about whether employers thought sociology was a ‘good

degree’ and a further 22 per cent believed they did not. However, as Platt (2012) notes, any

student move towards making decisions on the basis of perceived employability outcomes

may well alter student choices away from sociology as well as module choices within it.

At the time of writing we do not know much about prospective student understanding of

the discipline of sociology, their future career aspirations and how these will shape

university choices. In considering this matter and its impact on the development of QM

capacity within sociology teaching, we concur with Williams et al. (2008) that we need to

know more about who chooses to read sociology at university and “to what extent

attitudes toward the status of sociology as a ‘science’ or ‘arts’ subject change through

the undergraduate career as a result of exposure to methods or styles of sociology”

(Williams et al. 2008, p1016). This is something that the QM Programme may help

to unpick.

Implications for advancing pedagogical understanding of

innovation in teaching QM

In considering the outputs and findings from our project, we hoped to modestly advance

pedagogical understanding of innovation in teaching QM. Our students differed in the

extent to which they made links between substantive sociology and QM, but we do know

that the perception persisted that QM and substantive sociology were separate. This

raises the question of why? One issue was no doubt ‘dosage’. Our project consisted of

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drawing together six exemplars where QM and substantive courses overlap. Although we

aimed to embed greater integration between QM and substantive teaching, the structure

of our programmes still treat QM and substantive teaching separately. Crucially,

assessment remained distinct. Reflecting the observations of MacInnes (2009) (and others)

in one (post-project) focus group students discussed at some length how QM was used

neither in substantive teaching nor in assessment. This, they stated, fuelled their

perception that they had little in common. Taken together the threads of our innovation

may not have been enough for the students to see the links between the QM and

substantive teaching, and we believe that more needs to be done to reinforce the linkages

over time. It is worth bearing in mind that these students had only completed one

semester at university, with data from our final year focus groups suggesting that the

linkages between substantive subjects and QM become more visible over the course of

the degree.

Certainly, we do not advocate abandoning the approach given the weight of evidence on

the matter, quite the opposite. The problem of ‘compartmentalising’ QM teaching is being

considered by the QM Programme. It acknowledges that “addressing this shortage of social

science researchers will not be met by developing additional ‘stand-alone’ modules of

statistical training” and stresses the integration of QM at all stages of degree programmes,

deeper and more frequent exposure to QM and greater embedding of QM techniques

within substantive fields (Nuffield Foundation 2012, p14). A welcome perspective, it is

nevertheless clear that any attempt to diffuse QM teaching through the undergraduate

curriculum represents a challenge. As well as a practical challenge (as demonstrated

by the outcomes of our project) the views of staff teaching substantive modules

(who have different perspectives, preferences and priorities) can be a major barrier

(Howery & Rodriguez 2006, Wilder 2010). Indeed, there has been enduring tension

between theory and methods, ambivalence to the need to empirically test theoretical

ideas and resistance to the acquisition of skills with which to do so within the culture of

sociology (Bulmer 1989). The lack of quantitative research, poor skills and even a positive

hostility towards QM within British sociology has often been remarked upon (Platt 2012).

Readers will not be surprised to hear us suggest that the integration of QM and

substantive teaching will be difficult to achieve in isolation of wider cultural changes

within sociology. As Bulmer & Burgess (1981, p589) put it “methodology teaching will

continue to languish as long as empirical inquiry is not adequately integrated into the

main substance of core sociology”.

Concluding comment

Mooted as a promising approach to generating positive outcomes in QM teaching, there

remain questions about the theory and practice of linking substantive and QM teaching.

We have described how our approach to integration stressed points of commonality

between QM and substantive sociology and the use of exemplars in a blended learning

environment. The project gave us an excellent opportunity to (re)consider the focus of our

QM and substantive teaching, develop new tools and to engage in research on students’

attitudes towards and expectations of QM. Whilst we have seen that the project did not

have all of the outcomes that we expected, we have to be realistic. The nature of

contemporary QM teaching is embedded in the interaction of historical and cultural

traditions within the discipline, the resources that have been made available and student

perceptions, attitudes and experiences. Since QM teaching cannot be understood as

phenomenon that operates independently of the discipline as a whole, any response to

the perceived problems within the capacity of QM teaching needs to be understood within

the wider disciplinary context which produces and reproduces it. As Bulmer & Burgess

(1981, p589) put it many years ago “the future of methodology teaching will be determined

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within the discipline as a whole”. Nevertheless, this innovation and others like it provide

useful insights and lessons on which we can draw in taking forward QM teaching into the

twenty-first century.

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