Evolving Patterns of Working

7/28/2019 Evolving Patterns of Working

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Evolving patterns of working: do they matter?

Abstract

We consider how changing attitudes to computer use may alter the habits of students in theuse of location and times of the day. We discover for one community the extent to which

they absent themselves from the physical university and see some trends. We suggest that

this is a deep change of habit, influenced by the 'digital generation', and seek qualitativeresults on what determines the modern student's keyboard behaviour. We present early

evidence that, while students are indeed absenting themselves from formal computer

facilities, some understanding of the benefits of the physical university remains intact.

Keywords: engineering education; ICT in education; management of technology

1. Introduction

Prensky(2001)notes that modern students areDigital Natives, while the majority of staffareDigital Immigrants. They arrive with behaviours that can present us with issues:

Natives are 'used to receiving information really fast', 'prefer random access', 'thrive on

instant gratification and frequent rewards'. These traits might be viewed as one aspect of amuch broader sociological effect: aspects of post-modernism that have been well

documented by, for example, Bauman(2000)asLiquid Modernity, and by Beck(1996)as

Second Modernity, who describe characteristics of societies in the late twentieth and early

twenty-first centuries: 'inhabitants live in a perpetual present', 'social networks are not beingadded on to the national container; they are changing its nature', 'a society preoccupied with

the future'.

While there is literature on the spread of technology among users, there are few conclusions

on its impact on education. Many authors evidence the routine possession of digital

technology (Aun2009; Salaway et al.2008), but comment on the impact on the mode ofstudy is less well documented and contradictory. Debate has centred around managerial and

performance issues. Hanson(2009)notes the absence of consideration of broader issues

relating to academic identity; Temple(2008)noted that even recently the spread of laptop

and wireless use has been unanticipated, while Selwyn(2008)noted that the Digital Native

has a different perspective on issues such as plagiarism.

The ubiquitous VLE (virtual learning environement) is relevant to these issues since itpermits students to access their work from anywhere. VLEs provide audit capabilities to

allow administrators and teachers to discover on a per-student basis what has been viewed

and when, and work on exploiting these logs has been reported: Hung and Zhang(2008)discussed the opportunities for data mining in VLE logs to derive performance predictors

and produce interesting results in a narrow domain.
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We are unaware, however, of research into physical locality of students using VLEs or of

intentions to derive longitudinal statistics on a programme-wide basis.

'How' students spend their time has been of interest for some years since one of the benefits

of higher education is developing the self-motivated learner. Kemberet al.(1995)

considered how time is spent and noted that hours expended do not necessarily correlatewith performance; Schwartz et al.(1997)demonstrated that problem-based learning

encourages devotion of more time to self-study; Sandstr m and Daniels(2000)evidenced

that collecting accurate data on how students spend time is not easy. 'Where' students work,

however, has come in for much less scrutiny: Forsyth et al.(2010)have recently pointedout that 'campus-focused' universities have special requirements in respect of quality

control for distance education but do not consider the different quality of student

experience. In the traditional university, the assumption persists that the physical academy

is where most things happen.

In education, rapid developments in IT change student workplace options as VLEs inparticular are available 'anytime, anyplace'. An immediate institutional reaction to this is

approval: bulk laboratories are expensive to procure and need regular upgrades. But there

may be drawbacks: historically, while 'private study' has been an expectation of students,

there has been an assumption that it is conducted in and around departments and librarieswith academic staff within reach.

The nature of the use of student study time can be critical to the quality of learning

(Carrington1998; Kemberet al.1995; Kolari et al.2006), but beyond this, a major benefit

of higher education is the enculturation of the student into his/her chosen discipline

(Wenger1998), and this comes most easily from physical interaction with peers andacademics. Seely Brown et al.(1989)observe 'students are too often asked to use the tools

of a discipline without being able to adopt its culture' and Boaler(2002)writes 'students do

not only learn knowledge in the classroom, they learn a set of practices'.

Acquiring community membership has historically been semi-automatic as successful

students live and work physically within a department among some of that community'sstrongest exponents. While physical participation in the academy is not essential, it is

customary, and the consequences of its loss if students spend less time 'inside' deserve

caution.

We consider a scenario in which we see major changes in the patterns of work of our

students that may give cause for concern, both for their curricular experience and for their

disciplinary induction.

We consider one specific aspect of these behaviours which might impact on the use of

computers in education: does the availability of instant access impact on academic workhabits of time and place, and if it does, is it consequential?
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We pose two research questions which have received scant attention in the literature to

date.

When students conduct their work with or via computers, where do they prefer tobe, physically?

If the answer to the foregoing is changing with the years, what are the drivers instudents' choice of environment, and to what extent does this matter?

The first question is quantitative and we are able to give a good answer for a well-definedsubset of students in one institution for a particular year. We believe the techniques we use

are new, certainly in the scale which we have been able to deploy. The second is more

qualitative and we present preliminary results and observations that will guide furtherinvestigation in this area.

We consider that our observations provide important knowledge both for academic

resource planners and for teachers addressing the behaviours of Digital Natives and thatthey are representative of many or most institutions.

2. Material and methods

The host institution has contracted Blackboard(2008)to provide for a presentation of

teaching activity for all schools and faculties into the indefinite future, with all modules

required to develop a presence within the VLE. Although a great deal of student computeruse remains outside the VLE, a measure of Blackboard use provides a good index of

students habits of academic computer use.

We have developed a software that marries: Blackboard's own internal audit information,

institutional web-logs, institutional student record data and IP data indicating point of

access. Analysis of the first of these is commonplace and inconclusive. Our aim was to

discern patterns of use per-year and per-programme on time of the day and physical placeof access. We were not following the behaviour of individuals.

Details of the software are not recorded here, but it is able to log for an individual: times of

access for a given session; programme and year of study for that individual; physical place

of access. The last item requires description - many points of access are university

laboratories with fixed IP addresses. Historically, these dominated usage but it is the

migration away from them that interests us. The institution is a large single-campus site inthe north of England, with a number of student residences at distances up to 10 km. Of the

order of 33% of students occupy these residences, the remainder being part of a privatehousing market. As usual, near-ubiquitous wireless access exists on campus, permitting

laptop owners to be within the institution but not part of its hardware facility.
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Most student residences have some form of high-bandwidth access to the campus network.

Robust software permits simple VPN access from any Internet-connected machine, so

private-residence occupiers have the same quality of connection as laboratory users.

We thus consider three categories of access point.

Campus: Machines hard-wired into the university network, probably a teachinglaboratory.

Off-campus: Machine in a residence or private housing or out of town. We go someway to determining where such a machine is but dynamic IP allocation makes

precision difficult.

In-between: Laptops roving on campus and a variety of other miscellaneouscategories such as library provision.

The data supply is gigabytes per day and much is possible with what we have. We are

confident that patterns of behaviour will vary across faculties and have evidenced this, butthis prototype study limited itself to students of the School of Computing. This was an

expedient since the authors are based within that School, but history suggests that patterns

of use of these students are good predictors for other students' usage. We therefore reasonthat this is a useful subset to focus upon.

Some aspects of our observations we expected and some surprised us. We therefore movedon to gather focus groups of students to attempt to probe motivations for behaviour

according to the method of McNamara(2006). We conjecture that habits of use evolve

during an individual's time at the university and so we separated out first-year studentsfrom those in higher years.

The cohort studied is traditional - students attending university straight from high school. Inresponse to an open invitation. we attracted 10 first-year students and 11 from higher years

(the annual intake is 70-75). Each group had two females, marginally exceeding the

proportion in the broader population and were all native British.

All were full-time students of single honours computing or computer science. It is possible

that these groups were not fully representative of the population, having perhaps a higherdegree of motivation or sense of participation. The purpose they served, however, was as an

initial sounding board, and for this, the participants were suitable.

Participants were probed on their understanding of what choices existed, where they

preferred to work and on what importance they attached to this choice and whether the time

of day was a factor. Purely technical issues (e.g. printers and Internet access) were asked

about, and their own perception of how IT provision had affected their study. They werealso asked to estimate the proportion of their keyboard time spent on academic activity.
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3. Results

3.1 Quantitative results

3.1.1 Location

Statistics were collected, which indicated time of day, duration of session and geographical

point of access via IP.Figure 1illustrates location data and shows a clear dominance of off-

campus use whose extent surprised us. Interestingly, we noted a greater dominance in other,non-computational, schools.

[Enlarge Image]Figure 1. Session locations for computing students during the academic year for each

semester and each year. Semester 1 (top) and semester 2 (bottom); across, years 1, 2 and 3.

HR, hall of residence; wireless, implies on-campus roaming.

It is important to note that nearly all first-year students reside in a hall ('HR'), while

negligibly few senior year students do - for these students, HR is 'home'. This suggests anappreciable increase in the proportion of on-campus activity among second and third years:

aggregate percentage figures are inTable 1.
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Table 1. Percentage of hits from a campus computer.

Semester 1 Semester 2

Year 1 30.11% 33.19%

Year 2 40.62% 42.09%

Year 3 47.97% 45.05%

Finer granularity is given inFigure 2, where unsurprising weekly rhythms are in evidence,

and surges of activity are visible during the mid-January and May/June examinationseasons.

[Enlarge Image]Figure 2. Daily number of hits from computing students. Top, from laboratories; middle,

from the university campus; bottom, from off-campus and halls of residence.
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3.1.2 Time of day

Figure 3illustrates the times at which sessions commenced. Off-campus use has verydifferent diurnal rhythms to on-campus use - again, perhaps unsurprising, but the degree of

difference was more than expected.

One possible conclusion is that students leave the campus as soon as they feel able:

expensive institutional facilities are in demand for a narrow window of time.

[Enlarge Image]

Figure 3. Time of day usage. Top, dedicated school of computing labs; middle, clusters and

computers within the university; bottom, home and university hall of residence.
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3.1.3 Mobile devices

One unforeseen aspect of the work has been the ability to monitor access from mobiledevices: a range of these were detected via manual verification of user agent strings

identified in web requests. This was of interest since the VLE software was unconfigured

for such devices and is accordingly unwelcoming.Figure 4illustrates the proportion ofsuch accesses over an 18 month period.

[Enlarge Image]

Figure 4. Percentage of total number of web server hits from mobile devices.

The academic year 2008/2009 exhibits an upward trend, with two peaks in February and

June when exam results are posted. The first half of the year 2009/2010 is striking: despitebeing a narrow snapshot, we see a threefold increase in this access. For some, these devices

are becoming the preferred access mechanism.
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3.2 Qualitative results

Both groups were interviewed in a board room setting over a period of 60 min. Students

were articulate and ready to respond: this may be attributable to them being motivatedvolunteers.

3.2.1 First-year students

With a single exception, all had high quality domestic Internet access - we are aware that

this is the norm. Distance from the university ranged from 500 m to 15 miles, with anaverage journey time of over 30 min.

Such machines were well used and configured: 'I have the VPN at home \ldots I was told by

friend' 'It is all there in the wiki'. Issues such as bandwidth were relevant but not critical: 'I

don't mind if it's a bit slower'.

Domestic access was used when the formal day was perceived to be over: 'after university

is finished', 'for coursework, I'll work all night'. It was seen as providing all that was

necessary in a preferable environment: '\ldots can mainly get everything.', 'Home is morerelaxing'.

Significantly, most reported that their domestic work was solitary. In contradiction, therewas an agreement that home working was fraught with distractions (TV, radio, food):

'things take your concentration at home'. Group work, on the other hand, was always

conducted in the university laboratories, with a single exception provoked by a deadline.

Preference for the time of day seemed very variable: 'Get it out of the way, before evening',

'I like time off in the afternoon', 'I like to start later about 7ish', 'I like to have a break before

working', 'I like to get it finished', '\ldots night owl', '2-7pm is most productive', 'I neverworked in dead of night and now I do'.

Attitudes to the Internet were ambivalent - it was recognised that work did not always

necessitate it: 'the Internet is a distraction', but 'If a computer did not have Internet, I would

expect it to'.

VLE work was just one part of academic work, which in turn was just one part of keyboard

use. The mean student estimate of time percentage spent weekly on academic work was43% (=18).


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3.2.2 Second/third-year students

All had a well-specified computer with Internet connection, configured for university work:'specialising on Linux boxes'. Computers were now seen more as a tool and less for social,

IT and play: 'worse at computer usage - IT use is worse', 'learnt to use computers properly',

'easy to get back to Windows', '\LaTeX\ is amazing - forget how to use Word'.

There was more preference for working in laboratories and agreement that working from

home did not lend itself to working well: 'procrastinating at home', 'lack of concentration',the main reasons cited were related to having access to both staff and peers: 'interaction is

important and that is why I work more in Labs', '\ldots tech groups and programming

groups'. There was also a large motivation factor to complete their study: 'to get a job'.

Use had evolved since first year - all agreed they were now very fluent users: '\ldots know

how to find answers', '\ldots not a problem'. '\ldots tech support for family and friends - if I

don't know the answer, I can diagnose the problem and find a solution'. It was recognisedthat they did not rely on the Internet for academic answers and used more traditional

means: 'a lot more reading', 'no more Google'.

Diurnal patterns remained variable, with half preferring to work in the late afternoon, a

quarter preferring the morning and the rest the evenings. Most reported they did work

'when they need to' and not when they would prefer to: 'lectures impact when work is done','pressure makes me work well', 're-train yourself - adapt to new hours, body gets use to it'.

The estimate of keyboard time spent 'academically' was 59% (=21).

4. Discussion

Figures presented are viewed through a 2-fold filter:

they measure only VLE use and not academic work conducted outside thatenvironment;

they do not measure non-academic use.The first restriction - for the institution under examination - will become less of a

consideration as policy migrates most student work into the VLE. Nevertheless, we are

confident that what we see here is sufficient and enough of an indicator of habits to beuseful.

The second issue is perhaps not relevant to the issues we raise here, but it seems fair toassume that social use would not represent a lower proportion of off-campus access than

academic use. We are confident that the patterns and proportions we observe are accurate

enough from which to draw conclusions.


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That conclusion is to note how much time students spend working outside the institution.

Should we be surprised at this proportion? Perhaps not: we know the majority are equipped

with their own machines, and their fluency in the configuration and the use of them

improves during their study. Most first-year use is away from university facilities, butsenior students seem to do less work 'at home'. Of course, when we add in non-VLE use,

the amount of home computing students are doing becomes very pronounced.

Evident per-semester patterns do not surprise but we should also recognise that the time-of-

day patterns we present (Figure 2) are something we long suspected: the stereotypical

computer scientist slaving over a keyboard into the evening and night. Nevertheless, theconfirmation of a diurnal rhythm that sees such a narrow window of on-campus activity and

such a broad window of home use gives an insight into 'average' student behaviour that we

did not guess at.

Of surprise to us were the patterns of use developing in the utilisation of mobile devices.

The VLE is not configured to use these and is in many respects unfriendly: nevertheless, weconfidently expect the 3-fold increase in use in one year to continue; prima facie evidence

that such devices are becoming part of the broader patterns of use is of note - the users are

migrating. Perhaps, this is not a surprise, but such a direct index into rapidly changing

behaviour is rarely seen and shows the Digital Native marching ahead of the institution'sequipment provision.

What have we learned about today's Digital Natives? Probing students revealed young

people spending a great deal of time at the keyboard, much of which was non-academic.

There was no denial that much of this was 'at home', but a simultaneous acknowledgement

that the home environment was fraught with distraction. There is clear evidence that thismode of use has grown significantly in recent years and we conclude that changing

availability has indeed changed behaviour.

Positively, the responses of senior students indicate that they are acquiring a maturity as

scientists and learners: responses indicated that they were aware of good study habits, and

they displayed growing maturity as computer scientists, measurably spending morekeyboard time in the university. Of course, we are in a time of transition of behaviour, and

we suggest that the acquisition of these traits is something to monitor with care.

Is changing behaviour impairing cultural absorption? It is important to recall that the

groups we gathered were possibly not representative of the broader community: while they

were not the high-scoring students, they were prepared to volunteer in response to a public

request.

The remarks of senior students suggest a well-developed self-awareness of growing

maturity as computer scientists,1and an understanding that while their Native habit ofremote working was slightly diluted, a purpose of physically occupying the university was

to consciously interact.
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This is not a trait that is easy to measure since it is not in any explicit sense assessed, but

we see it as a key issue - academic maturity may have been fairly assumed. One sense in

which it may be triangulated is in the conduct of final-year project work. Fincheret al.

(2001)(and others) noted that this usually requires students to exercise a range ofprofessional skills in addition to technical ability. T

he cohorts under study have not displayed any detectable deterioration of such skills to

date, but this could develop into an interesting litmus of whether isolated, domestic learning

is impacting on broader fluency.

What messages do we bring to academic staff? First that they ought to be aware of location

habits: few individuals would guess with precision the on/of-campus split of time weobserve or time-of-day patterns. More broadly, teaching staff ought to open the debate of

what they would prefer.

If they do not mind whether or not students spend time in laboratories, that is fine, butteaching materials and techniques ought consciously to recognise this and work to the

advantage of the Native's habits - in many cases, perfectly reasonable momentum and

expectation presumes that students are 'in and around' during the working day.

Alternatively, if we wish fairly to persist with the expectation of attendance, then these

results represent an alarm bell - observed preference is to be elsewhere, and observed datatell us the bird, if not already flown, will be flying soon.

More opportunistically, we confirm suspicions of a fast-declining use of bulk institutional

facilities and reduced need for their provision. Resource planners often adjust to such

changes after the event, but we can signal that, while campus laboratories will always have

a role, investment in them can come down.

However, the debate on how properly to support the domestic user has not been opened in

many universities, and this is likely to become the problem of the future. It should beremembered that the students spoken to here were best-equipped to look after themselves:

we cannot comment on how adept the Natives of other schools and faculties are going to be

in attending to their own technical well-being.
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5. Conclusions

We have set out to discover trends in where students work and to ask whether changes in

these habits might have broader academic consequences, by exploring where users aresitting when they use a VLE which is the primary academic access point in a major

university. It is demonstrated that student patterns of behaviour are very different from thatwhich they once were and are continuing to change rapidly. This provokes severalsuggestions for action and further work.

(1) Teachers in universities should ensure they are aware of the behaviour of theirstudents. Most university teachers will quite reasonably project onto their students

their own behaviours and might expect them to be 'in and around' academic

facilities: if physical absence is the norm, perhaps with students working and

interacting on mobile devices, this might well impact on how we construct anddistribute teaching and assessment materials and engage in tutorials and other

physical meetings. We would need to construct teaching in such a manner that it can

be pursued in absentia. More fundamentally, university teachers could open the debate on whether such a

change is desirable ifit impacts on the sense of community. We offer no conclusion

on this, but strategically, if we wish to live in a world in which our students are, by

default, present in the academy, then we need to give immediate attention to theconduct of our teaching and procedures, since the evidence is to the contrary. We

consider opening the issue of whether the change in behaviour might prejudice

student enculturation. Our snapshot of students is limited, and we expect trends in

behaviour change to continue. We therefore suggest that university teachers watchthis aspect of student development with care, and suggest that student performance

in project and dissertation activity might be a good illustration of maturity.

Pragmatically, we observe the bulk of student computer access being away frominstitutional provision. This has implications for IT support services: while they will

continue to need to provide an infrastructure, it will increasingly be virtual. It is not

suggested that physical laboratories will disappear, but the Digital Native demandfor 'here and now' access will mean support of a different, more labour and costly

kind.

We have been surprised by the extent of change that we have seen in studentbehaviour and will continue monitoring it. This will inform the issues we raiseabove and will allow pictures to be built across all faculties and degree programmes.

We suggest such data are of more than passing interest and that all universities

would benefit from more accurate pictures of where there students are working, in

addition to how well.

We have presented preliminary results which we will continue to use and monitor. Hanson(2009)has noted the academic staff view that 'the campus experience \ldots was the

primary reason students came to university', and the strong belief that face-to-face contact

is essential to the creation of academic identity - 'they are not yet prepared to embrace thedisembodiment or repositioning required by e-learning'.
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We suggest that the campus experience, while not under threat, is likely to change its

character fundamentally as students are facilitated (or encouraged) to work 'anywhere' and

that teachers and academic planners need to construct teaching and learning strategies in

that light.

Acknowledgements

Acknowledgements are due to Jeremy Harmer and Bo Middleton, without whose help the

data for this paper could not have been gathered. Thanks are also due to the many students

who freely gave their time in assisting us to understand the data.

Notes

One student told us how he used to shy away from friends and relatives 'asking him aboutcomputers'. Now, he knew that while not knowing the answer, he would know exactly howto locate it. We see this as one hallmark of community membership.

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List of Figures
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[Enlarge Image]Figure 1. Session locations for computing students during the academic year for each

semester and each year. Semester 1 (top) and semester 2 (bottom); across, years 1, 2 and 3.

HR, hall of residence; wireless, implies on-campus roaming.

[Enlarge Image]

Figure 2. Daily number of hits from computing students. Top, from laboratories; middle,

from the university campus; bottom, from off-campus and halls of residence.
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[Enlarge Image]

Figure 3. Time of day usage. Top, dedicated school of computing labs; middle, clusters and

computers within the university; bottom, home and university hall of residence.

[Enlarge Image]
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Figure 4. Percentage of total number of web server hits from mobile devices.

List of Tables

Table 1. Percentage of hits from a campus computer.

Semester 1 Semester 2

Year 1 30.11% 33.19%

Year 2 40.62% 42.09%

Year 3 47.97% 45.05%

Date post:	03-Apr-2018
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Evolving Patterns of Working

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