Paper published in Computers and Education Journal.

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Computers & Education xxx (2005) xxx–xxx
www.elsevier.com/locate/compedu
Our flexible friend: The implications of individual
differences for information technology teaching
a,*
S.J. Waite , S. Wheeler a, C. Bromfield b
a
University of Plymouth, Douglas Avenue, Exmouth, Devon EX8 2AT, UK
b
University of the West of England, Frenchay Campus, Coldharbour Lane, Bristol, BS16 1QY, UK
Received 2 August 2004; accepted 26 November 2004
Abstract
In this article, we report the observed differential uptake and use of computer programs and activities of
seven boys and girls of high, medium and low attainment in a classroom in the UK where over 40 children
aged 10 and 11 have a networked PC on their desk all day and every day. We observed the detail of what
happened in the small space between the pupil and the screen over the period of 1 year in the social and
instructional context of the classroom. We found interesting individual differences superseding the expected
variation based on gender and attainment. We suggest some possible Ôwithin childÕ and external factors
which may contribute to these differences and consider some of the implications for teaching and learning
through ICT and the need for further research to investigate the nature of these differences.
Ó 2005 Elsevier Ltd. All rights reserved.
Keywords: Elementary education; Human–computer interface; Multimedia/hypermedia systems; Pedagogical issues;
Teaching and learning strategies
1. Introduction
The importance of information and communication (ICT) skills for the future has been asserted
(DfES, 2002; DfEE, 1997) and substantial UK government investment has been made to support
*
Corresponding author.
0360-1315/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved.
doi:10.1016/j.compedu.2005.01.001

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2 S.J. Waite et al. / Computers & Education xxx (2005) xxx–xxx
the development of these skills in schools. Some studies into the impact of ICT in education have
been large scale and have commented on broad trends (McFarlane et al., 2000; Smeets & Mooij,
2001; Selwyn & Bullon, 2000; Robertson, 2002), while others have looked at affective components
in small scale studies (for example, Cooper & Brna, 2002). There have been studies which have
looked at ICT as the means of presenting learning material (Kemmis, Atkin, & Wright, 1977; Rid-
ing & Grimley, 1999) and others that have addressed ICTÕs facilitative role for learning (Ander-
son, McAteer, Tolmie, & Demissie, 1999; Burley, 1998). In the context of calls for greater
personalisation of learning;
Information and communication technology (ICT) is a powerful tool for learning, helping teach-
ers explain difficult concepts, giving access to a huge range of examples and resources, and
engaging pupils easily. It is also a vital tool for personalisation – giving the opportunity to tailor
tasks to children (DfES, 2004, Chapter 5, para 17)
the examination of individual differences in the use of ICT becomes increasingly important. Some
studies comment on the differential use of ICT by individual students, for example through home
computer ownership (e.g. Hayward, Alty, Pearson & Martin, 2003) and gender has long been sug-
gested as influencing the uptake of ICT (e.g. Joiner, Messer, Littleton, & Light, 1996). The rela-
tionship between ICT and attainment appears to be seen mainly in terms of the effect of ICT on
attainment (Cox et al., 2003). The converse has rarely been examined; how higher attainment or
ability levels may affect studentsÕ use of ICT. Harrison et al. (2002), for example, found no evi-
dence that pupils at any one ability level were advantaged or disadvantaged by high use of
ICT. Therefore, in designing our study, although we only observed a small group of pupils in de-
tail, we sought to control for gender and attainment to monitor possible effects. This article seeks
to explore the individual differences which may shape childrenÕs use of ICT in the light of a lon-
gitudinal study of a primary school class of 10 and 11 year olds.
2. Sources of individual differences
Individual differences are self-evident in our daily experience but the source of these differences,
their stability over time and their contribution to differences in performance are much more com-
plex considerations. Intelligence, cognitive styles, learning approaches and personality types are
some of the psychological constructs that have been associated with the study of individual dif-
ferences and their influence upon patterns of behaviour. In addition to these internal constructs,
past experience is clearly a source of individual differences and will interact with internal factors to
engender different responses.
Cognitive style has been defined as Ôan individualÕs preferred and habitual approach to organ-
ising and representing informationÕ (Riding & Rayner, 1998, p. 8). There has been a great deal
written about learning styles, preferences and cognitive styles (see CassidyÕs review, 2004). Cassidy
(2004) distinguishes between traits, which are relatively stable and fixed (cognitive and learning
styles) and states, which are more responsive and fluid (learning strategies). Both may impact
on how particular learning contexts work for different individuals, but ÔstatesÕ may be more flex-
ible to them. There is considerable debate about the reliability, validity and usefulness of some
tests of cognitive and learning style (Coffield, Moseley, Hall, & Ecclestone, 2004; Leutner & Plass,

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1998; Smith, 2002). Apart from issues of reliability and validity, some tests are inappropriate for
children under 11, for example where self report requires a level of self awareness of learning pref-
erences which many young children do not possess. GardnerÕs Theory of Multiple Intelligences
(1983), RidingÕs Cognitive Styles Analysis approach (1991) and Dunn, Dunn and PriceÕs Learning
Styles Inventory are probably the most commonly used with primary aged learners. They offer
possible frameworks for factors accounting for differential use of ICT by young children. Desmedt
and Valcke (2004) argue that citation analysis of learning styles indicates the impact of different
learning style orientations and Dunn and Riding are both frequently cited. However, Dunn, Dunn
and Price have been criticised because of a lack of independent evidence of their testÕs effective-
ness, both as a reliable psychometric measure and as a useful tool for tailoring pedagogy for indi-
viduals (Coffield et al., 2004). GardnerÕs Multiple Intelligences (1983) were not easily deduced
from the activities we observed in our study.
The field of learning styles and cognitive styles is complex with different labels and concepts
overlapping (Cassidy, 2004). Some researchers believe them to be correlated with personality
traits (Jackson & Lawty-Jones, 1996), while others claim that personality has only a moderating
effect on learning style (Riding & Wigley, 1997). Personality itself may have an impact on the dif-
ferential use of ICT (Benyon, 2002). For example, it might be supposed that extravert personal-
ities would be drawn by the communicative and multimedia possibilities that offer high
stimulation. Conversely, ICTÕs potential for reduction of classroom noise by electronic communi-
cation and for independent study might be more attractive to introverts. High levels of anxiety
may also impede childrenÕs capacity to remember and hence their ability to process information
(Elliman, Green, Rogers, & Finch, 1997).
Deep and surface approaches to learning (Marton, Hounsell, & Entwistle, 1997) offer another
way to consider the individual differences which impinge on studentsÕ use of ICT. Those students
motivated to understand the material they are learning will tend to adopt a deeper approach to
learning and look beyond the surface features required to satisfactorily complete tasks. An
emphasis on presentational features of ICT may suggest a more surface approach. However, Valle
et al. (2003) argue that in practice many students have multiple goals including learning and per-
formance goals (akin to deep and surface approaches) which offer more flexibility to adapt in dif-
ferent learning situations. They distinguish between approach–avoidance tendencies to either
promote favourable judgments of competence or avoid negative assessment of competence. An
anxiety about perceived competence may encourage surface approaches and a narrower focus
on requirements for task completion. Whether because of anxiety about competence or a lack
of understanding of broader learning opportunities within tasks, lower levels of ability may influ-
ence studentsÕ tendency to adopt surface learning approaches.
What is clear is that individual differences are likely to impinge on observable actions in terms
of childrenÕs learning in a complex and interrelated way that makes reliance on one measure alone
inadequate as a source of guidance in how to personalise learning.
3. Our research
Our study aimed to investigate the impact of ICT on learning in a primary school class-
room over several years. We took baseline measures in Year 5 (children aged 9 and 10) in

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a classroom with three PCs. We then examined a Year 6 (children aged 10 and 11) context
where every child had access to his/her own personal networked computer all day and every
day, and considered the implications of this for teaching and learning from both teacher and
pupil perspectives. Individuals were tracked through from Year 5 to Year 8 (secondary
school), to study the impact of ICT in differing educational environments by interviewing
them in Year 5, 6, 7 and 8. The headteacher, class teacher and teaching assistants were also
interviewed.
We looked at the detail of what happened in the small operating space between the child, the
computer and its immediate surroundings. We looked at the teacherÕs interventions, the presence
of different staff and the social interaction of the children. We noted their individual activity and
use of computer programs. We tried to understand how these interacted to create unique learning
experiences for the children in our study. We listened to the ideas of the children and staff about
what they thought had been happening in their classroom.
Our study adopted an ecological approach, as proposed by Kemmis et al. (1977, p. 321) recogn-
ising the importance of context and the environment within which learning takes place. We found
that the computers were only actually used 49% of the time; maths took up about 25% of the ob-
served time, using handwritten work books (13.6%) and that 28% of observed time was taken up
by organisational issues and teacher talking. We observed very little music in class; this seemed to
take place with a teaching assistant on a PC or singing by groups of pupils in their free time in the
hall. We did not see any art lessons;
We were supposed to do it every Thursday . . .but that didnÕt quite work out.
I think the whole year we only drew one thing, it was our shoes!
(Pupils, follow up interview, Year 7, secondary school).
The curriculum in year 6, the year of observations, was principally composed of literacy, and
humanities mediated by literacy practices, and mathematics, with less emphasis on other aspects,
including science.
While this emphasis on the core subjects is probably widespread in primary schools in the
wake of standard assessment tests (SATs), the school in which the study takes place is unusual
in that it has frequently appeared in the press because of its high level of investment in ICT for
its oldest children. It also accepts a higher than usual proportion of children with special edu-
cational needs.
The headteacher and class teacher, who is also Deputy Head, are energetic and entrepreneurial
with powerful personalities. The teacher gave the pupils in our study a great deal of freedom
about how and when they did their work. They would have a number of tasks to complete, which
the teacher usually sent to them via email, having initially introduced each new topic in a whole
class session.
The two teaching assistants seemed to fulfil two distinct roles; Mr. Pratt is Ôalways on his com-
puterÕ, while Mrs. Marks Ôcomes round and looks at your work and checks your spelling is all right
and that your work makes sense and she helps you in Maths and spelling and thatÕ (pupil, year 6,
primary school). The class receives many visitors: teachers, press and ministers, who come to ob-
serve their unusually high use of ICT. The children were accustomed to being watched and
answering questions about their use of computers, which made very close observation of their ac-
tions easier than it might be in other classrooms.

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One of the dilemmas we faced in this research is a tension between trying to measure and quan-
tify the use of ICT by the children for different purposes and to understand the processes under-
lying their observed behaviour. This has guided our methodology and directed the mixed methods
of data collection we used, which are explained in more detail below.
4. The methodology
We selected six children as ÔrepresentativesÕ to explore the effect of gender and attainment levels.
Previous research (for example, Clegg, 2001; Joiner et al., 1996) led us to think that gender might
explain differential uptake of ICT. Joiner (1998) compared performance on software that was
structurally identical but either male or female stereotyped and found that girls performed worse
than boys on both versions of the software even taking into account computer experience. Passey,
Rogers, Machell, McHugh, and Allaway (2003) have suggested that ICT has a more positive effect
on boys extending their interest span. However, Harrison et al. (2002) found that this did not
translate into an advantage with learning over girls.
We also considered attainment might be a major factor, so we included a male and female of
high, medium and low attainment, as assessed by their class teacher, in our observational target
group. The head teacher also added a seventh child, representative of high attainment and
female.
Since characteristics and the attitude of teacher would clearly be influential on their develop-
ment during the year and, with two other staff as Teaching Assistants present, it was considered
vital to keep fieldnotes of the overall picture in the classroom. Some background features were
also noted in the observation schedule. These two sources enabled us to take account of some
of these background characteristics. However, pilot observation showed that it was impossible
to determine how individuals were working from a single advantage point and the layout of
the room meant that a group working together and geographically co-located to enable group
observation was rare (Wheeler, Waite, & Bromfield, 2002). We decided to observe the target
group children for periods of 16 min each, noting their actions every minute, and then moving
on to another child to repeat the process. The observations included a running record of activity,
which was coded under the following categories:
date,
time,
whether the child was in the main classroom or annexe,
what curriculum area being covered,
what principal software was used,
the nature of the activity the child was engaged in,
whether they were on or off task, quiet or talking,
what sort of contribution the teacher or teaching assistants were making at that point,
what staff were present in the room.
Efforts were made to ensure balance in the number and timing of the observations so that each
child was observed for approximately the same length of time and at different times of the day

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and week. Over 200 individual records were taken of each child during 22 visits to the school
throughout the school year and at different times of the day. While this enabled a spread of dif-
ferent activities to be sampled, because only one observer was present for most of the time, con-
temporaneous observations were uncommon. As part of our original design, this was not
especially problematic as the observations were going to be grouped for analyses, and the smallest
units of analysis we intended to look at were gender and attainment levels. However, the method
of data collection precludes comment on the different responses of individual children to the same
set of teaching and environmental characteristics. As we continued to collect data and spend time
in the classroom, we began to suspect that individual differences might be playing a bigger role
than the variables we had chosen. A further study in which several researchers observe target chil-
dren during the same periods of time would therefore be helpful to examine how whole class
instructions are translated by individuals into activities in that narrow interface between computer
screen and pupil.
We interviewed and observed the target children in the summer term before they joined the
Year 6 class to get a baseline measurement for how they worked and an insight into their attitudes
towards computers. They were then observed on an almost weekly basis, varying the day of obser-
vation, for the whole of their final year in primary school. They were interviewed at the end of this
final year, after a term at secondary school and again after a year and a term at secondary school.
An examination of their changing views of ICT will be the subject of another article. They also
completed the NFER-Nelson Non-Verbal Reasoning Test 10 and 11 (Smith Hagues, 1993).
The school operated a computer based assessment system whereby children were assessed annu-
ally by their class teacher as high, medium or low within National Curriculum levels. Learning
gain was derived from these assessments. Proficiency with ICT was derived from observations
and self report.
5. Data analysis
The coded observational data was entered in Excel and analysed using SPSS. During the col-
lection and analysis of the data, it became clear that individual pupilÕs responses to and uses of
the ICT varied considerably. We became interested in what might be the source of these indi-
vidual differences. It did not seem appropriate for us to use GardnerÕs multiple intelligences
model as we had limited opportunities to observe the children in music or art and so would
be unable to infer ability in these areas. These subjects rarely featured as part of the regular
school day in the class studied. Subjects were mostly taught through topic and project work
for the majority of the year, with a period focused on practice papers as SATS preparation.
Dunn, Dunn, and Price (1989) have been criticised for the theoretical underpinning of their
model (Coffield et al., 2004) so this model was rejected. RidingÕs categorisation, on the other
hand, offered a means of exploring learning style as a source of the individual differences ob-
served, by inferring from observation and interviews where the individual children lay within
his schema. It would also be possible to tentatively compare this very small sample with a wider
range of other studies, despite the fact that cognitive style had not been identified as a variable
at the outset of our project.

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6. Inferring learning styles
No formal assessment had been made of intelligence, learning preferences, cognitive style or
personality, as these were not principal foci of our research at the outset. This technique of deter-
mining learning styles may not in itself be detrimental to the research. Leutner and Plass (1998)
found that their observational tool was more successful in predicting learning outcome than ques-
tionnaires about learning preferences. Observation may be particularly important as a tool for
assessing learning style when children are unaware of their preferences and where an ethnographic
approach attempts to discover how real learning occurs in a real classroom situation. Using tests
in artificial situations is of limited value when our methodology was designed to provide a rich
contextual background of other factors operating in this primary school classroom. The main
problem in the use of the following framework as a tool to organise the data stems from the post
hoc nature of the categorisation. Issues of circularity arise in that it is their behaviour which places
them in a construct defined by those very behaviours rather than as a result of an independent pre-
test.
Riding (2002, pp. 25–28), describes two cognitive style dimensions:
wholist (tends to see material as a whole, and have overarching view and appreciate context) to
analytic (tends to see elements of material and be good at comparing and contrasting parts but
may struggle to see bigger picture).
verbaliser (tends to consider material as words and more externally, socially oriented) to imager
(tends to consider material in pictures and more internally oriented).
The interaction of these dimensions may complement and moderate or intensify these features.
Using these descriptions and applying them to the ÔportraitÕ of the children built up from our
data, an inference was made about where the target children lay in RidingÕs framework with
two researchers independently categorising the pupils on the basis of questionnaires about
their computer use, interviews and observations. These are illustrated by quotations from
these sources in the following table, although these cannot adequately convey the fuller knowl-
edge of the children gained through weekly contact over the period of a school year (see
Table 1).
7. Non-verbal reasoning and learning gain
The children took the NFER-Nelson Non-Verbal Reasoning test 10 and 11 (Smith Hagues,
1993) which measures the ability to recognise similarities and patterns in unfamiliar designs. It is
believed to be related to the ability to understand and assimilate new ideas and information
(Smith Hagues, 1993) and we thought it might distinguish which of them were more ÔsuitedÕ
to a discovery way of learning through exploration that this class used. We used the non-verbal
test as it is assumed to free the assessment from language bias but intuitively one might expect
there to be a link to RidingÕs concepts of imager and verbaliser, whereby imagers would perform
better on the non verbal and verbalisers on the verbal test. The test is considered likely to identify
those with strengths in maths, science, design and technology.

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Table 1
Inferred personality and cognitive style
Target Inferred Illustrative quotations
child cognitive style
1 AI ÔYou can get Clipart to put in your work and you can change coloursÕ. ÔIÕve got a pet on
the Internet, a Neo pet, and I go on to download songs and go on Pop Star search. I did a
project on S Club 7 before I went to see themÕ. ÔYou can get pictures of the car and make
your own pictures of the car and that. And take pictures of your groupÕ
2 AI ÔThe Internet, making my web page, looking at picturesÕ. Ô I look for golden retrievers and
other kinds of dogs. I look at wrestling pagesÕ. ÔIÕve written Pokemon dessert and put the
pictures on. I need to still do the writing partÕ. ÔIf youÕve got to imagine something and
then you go on the Internet theyÕll be lots of things like youÕve imagined but it will be in
different kinds of waysÕ
3 WV ÔThe best thing is the technology really and the information you can get from themÕ. ÔAt
home, I probably spend about 25% on games, 25% on work and another 50% on other
whatever, like email, something like thatÕ. ÔI can read the documents. . .and then use it for
work, I want to be an individual, so I do my own thingÕ
4 WI ÔInternet, Pokemon, GodzillaÕ. ÔWe did do quite a bit of art on my home computer. ItÕs
just a lot more fun than a book and sitting on a chair and writing in your bookÕ. ÔI donÕt
do a lot of emailingÕ. ÔIf I get bored I can sometimes put a sound on. . .and then go back to
my work and it can help me keep awake for a lot longerÕ
5 AI Ô You donÕt know which programme does what and youÕve got to find it out, work it out
which programme does what by playing around with thingsÕ. ÔI like writing stories, just to
let your imagination go wild, you can make up anything you wantÕ
6 AV ÔWrite work down, go on internet, send emailsÕ ÔAt home for writing invitations and thank
you letters and to play gamesÕ
7 AB ÔIÕm making Internet pages. . .there are special drawing programmesÕ ÔIÕve got this painting
thing and you can download popstars, movies and when friends come over we quite enjoy
doing thatÕ. ÔThereÕs all sorts of things, like I can make sounds. . .the way you can change
the computer . . .I can design my own background if I wanted toÕ. ÔWeÕve discussed it in
our groups and I understand it a lot more now. . .writing in your books is boring but if
you do it on the computer on Textease you can. . .just put a few pictures on them and
change the writing, colours and thingsÕ
The usefulness of these Ôwithin childÕ categorisations needs careful thought in educational set-
tings rich with other social and contextual influences, and caution is needed as other aspects of
individual difference were also operating, such as experience of computers outside of the school
environment. The post hoc designation may also have been subject to researcher bias, although
two colleagues had independently categorised the children to mitigate any risk of this kind of bias.
8. Research findings
Our initial analyses showed how gender and attainment organised some of the data. For exam-
ple, some gender variation in activity was marked with the girls having nearly twice the number of
socially interactive activities (asking questions, helping others with work, seeking help, discussion,
answering questions, instant messaging) observed in comparison with the boys. Although male

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S.J. Waite et al. / Computers Education xxx (2005) xxx–xxx 9
60 High
No. of observations Medium
50 Low
40
30
20
10
0
Research Reading Writing Interactive Demo Thinking
Fig. 1. Activities by attainment levels.
and female were equally represented in the top users of programs, the sorts of programs they fa-
voured differed. Typically, boys engaged in Starspell, database, file manipulation, CD roms and
Internet; while girls used Media Player, Photoshop, communication software, Publisher, Power-
Point and the Intranet to a greater extent. This echoes CleggÕ findings (2001) about more social
uses of computers by females. However, in a study by Joiner (1998) which looked at gender ste-
reotyped software, he found that while boys preferred gender stereotyped computer based prob-
lem solving, girls showed equal preferences for all. The generic nature of the software used may
have circumvented a gender issue for boys but points up a possible gender difference in the appeal
of even generic programs.
The top users of programs were predominantly (53%) from the high attainment group. The pro-
grams they were seen to use to a greater extent tended to be directed towards an end product, such
as Word, PowerPoint, Photoshop and databases. The medium attainment group were observed
using process programs, such as the Intranet and file manipulation to a greater extent. The low
attainment group favoured CD roms, communication software and Media Player. This pro-
cess/product divide is reinforced by the sorts of activities engaged in; writing was more common
in the high attainment group and socially interactive activities in the lower attainment groups (see
Fig. 1).
However, Fig. 2 groups the pupils into low, medium and high attainment groups as individuals
and shows the variation between the individuals clearly. The variation is smoothed and masked by
analysis at attainment level. Analysis at this level only might inhibit our ability to understand how
ICT use and individual differences may interact.
A further effect was seen in the way individuals engaged in learning. Fig. 3 shows the social
behaviour of individuals, categorised within on and off task behaviour into ÔquietÕ, Ôtalking to
peersÕ, and Ôtalking to adultsÕ. Some individuals had very low levels of interaction with adults
in the class, for example, target child 6 and target child 2 especially, regardless of whether they
were on or off task.
The observations showed that target child 6 (a high attainer) was the quietest, least off task and
had least interaction with adults. Target child 4 (a low attainer) was the most off task and talked a
lot to peers. Although target child 7 spoke the most to peers, a high proportion of the talk was

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10 S.J. Waite et al. / Computers Education xxx (2005) xxx–xxx
80
No. of observations
Research
Reading
60 Writing
Interactive
Demo
40 Thinking
20
0
1 4 2 5 3 6 7
Students
Fig. 2. Individual activity profiles of students.
140
No. of observations
OT quiet
120 OT peers
100 OT adult
80
60 OFT quiet
40 OFT peers
20 OFT adult
0
1 4 2 5 3 6 7
Students
Fig. 3. On and off task behaviour.
task related. Target child 1 was the least quiet but spoke least to peers and most to adults. These
social observations helped to fill out the pictures of how the children tended to approach learning
and provide insight into how independent in their learning the pupils are and the extent to which
they seek peer or adult support. Frank, Reich, and Humphreys (2003) reported how students of
this age seek personal contact, although we found that, for some students, the distancing of elec-
tronic communication removed inter-personal conflict and thereby supported group learning
(Bromfield, Waite, Wheeler, 2003). Riding (2002) suggests that in addition to being more reliant
on text for learning, verbalisers tend to use talk more in their learning (p. 29) and that wholists
prefer to learn in groups (Riding Read, 1996). This, however, illustrates a difficulty of circular-
ity in post hoc categorisation of pupils, whose behaviour places them in a construct defined by the
behaviour they have exhibited. Personality characteristics such as introversion might also explain
this difference.
The use of programs also varied between individuals, as different programs might be more com-
patible with their preferred way of learning (Riding Grimley, 1999). Those who sought more
social contact took up the communication opportunities of email and instant messaging (amal-
gamated under ÔOutlookÕ in Fig. 4).
The advantage of this was that, whether the child was on or off task, communication was less
disruptive to others working independently around them. However, it was also more difficult for
the staff to detect if the communication was off task. This lack of awareness of individual response

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Internet
No. of observations
50 Intranet
40 Cdrom
Word
30 Publisher
Powerpoint
20
Outlook
10 Photoshop
Media player
0
File manip
1 4 2 5 3 6 7 Database
Students Starspell
Fig. 4. Individual variation in use of programme types.
was a disadvantage to facilitating teaching and learning through common open access to pro-
grams as support for their learning. As differentiation was by outcome, the potential to find suit-
able learning activities and to stretch children was in each childÕs own hands rather than
controlled by the teacher and this suited some but not all students. Sometimes pupils had to stay
in at break if they did not achieve certain amounts of work within sessions due to a perceived lack
of effort.
Drill and practice software like Starspell was supposed to be used by all at the start of the after-
noon but some pupils preferred to go on the Internet or listen to music. Their participation and
success in this spelling practice was self-monitored and was not related to attainment level. This
blanket use of drill and practice software for the whole class is in contrast to the findings of a
questionnaire about computers and literacy sent to West Country schools (Waite, 2004). In this,
teaching staff described drill and practice as more likely to be used for children with special edu-
cational needs.
One of the interesting individual differences in activity was in Ôfile manipulationÕ, where the pu-
pil was moving between programs. Although this could be interpreted as familiarity with the pro-
grams and a facility for ICT, in practice, it often appeared to the observer to be a new
technological equivalent of unnecessary sharpening of pencils, a time wasting activity, perhaps
to afford Ôdown timeÕ from work and indicating an uncertainty about how to approach a task.
From observation of target children 2 and 4, they seemed to find difficulty in settling to tasks; they
both have high rates of file manipulation.
There were also marked differences in the amount of time the pupils used their computers as
shown in Fig. 5.
Four of the children used the computer for around 40% of the time but target child 5 used the
computer much less than this (21.8%) and target children 1 and 3 used it much more (68.3% and
75%). This would seem to indicate that for target child 5, the class reliance on the computer as
their main tool for learning may have had detrimental effects on her learning. Her use of the com-
puter was largely as a result of direct instruction rather than choice.
Fig. 6 shows how the two least off task were target children 3 and 6 and that 1, 2, 5 and 7 had
roughly equal amounts of off task observed, while 4 was off task for about 44% of the time he was
observed.
Riding (2002) suggests male wholists are most likely to have behavioural problems (p. 63). In
fact, in this study, the level of disruptive behaviour in the class as a whole was very low, despite
there being a number of previously excluded pupils in the class. However, the type of off task

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1
2
3
4
5
6
7
Fig. 5. Relative proportion of time spent on computer.
1
2
3
4
5
6
7
Fig. 6. Relative time spent off task.
behaviour more at risk in this setting was quiet, withdrawn passive behaviour, which prevented
engagement in the learning tasks but did not interfere with othersÕ learning.
9. Individual differences in ICT use and learning gain
Table 2 illustrates the patterns of ICT behaviour and the childrenÕs rating on a number of
measures of individual difference (see Table 2). The overall use of ICT does not seem to ac-
Table 2
Students ranked by learning gaina
Students 7 3 6 4 5 2 1
Gender F F M M F M F
Inferred cognitive style AB WV WV WI AI WI AI
Attainment High High High Low Medium Medium Low
Learning gaina 7 6 5 3 3 3 2
Relative use of programmes 93 155 94 120 56 96 152
Home computer? 1+ 1+ 1 1 1+ 1+ 1
Used for games plus Yes Yes Yes Yes
Used for homework only Yes Yes Yes
Standardised score on NFER NV Test 121 107 118 96 94 78 86
a
Learning gain is a numerical representation of the difference between the teacher assessments at Year 5 and Year 6.

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S.J. Waite et al. / Computers Education xxx (2005) xxx–xxx 13
count for the learning gain of the pupils, which is largely in keeping with the attainment levels
the teacher initially assigned. The higher attaining students seem to be widening the gap be-
tween themselves and low attaining students in this ICT-rich and open-ended task environment.
Neither does there seem to be an association between particular programs or activities and
learning gain. The children were all fairly skilled in their use of ICT, unsurprisingly in view
of the level of exposure they had to computers in school, but some appeared to have more pro-
ficiency and enjoy the use of them more. All of the students had at least one home computer
but there is an interesting difference in the reported sort of use pupils made of the computers in
that using it for more than just homework seemed to be linked to higher learning gain. This
might indicate that their preference for computers, using them beyond functional necessity,
made them more attuned to the opportunities in the ICT-rich classroom. It may also indicate
that their ÔplayÕ and exploratory approach to learning made them more effective learners in this
particular classroom, which relied on personal responsibility for learning. Obviously, this is too
small a study to draw conclusions, but this area may be worth exploration through further
research.
The way in which the computers are used may be qualitatively different depending on the stu-
dentÕs innate approach and the meaning they attach to tasks. The more narrowly instrumental
pupils are in their use, the less computers may offer a scaffold to learning. It may be that it is
understanding of computers as a tool amongst others and the matching of this to tasks and learn-
ing style that is important, rather than skills in using the capacity of the computer for improved
presentation.
10. ÔWithin childÕ individual differences
As previously noted, some caution must be exercised in using labels of learning style, person-
ality type or approach to learning as they are merely inferred in this study. It would be necessary
to test for these to see if the inferred types are valid in order to draw any firmer conclusions about
their role in using ICT for learning. Furthermore, the usefulness and appropriateness of this cat-
egorisation has to be carefully considered. The danger that an externally imposed categorisation
places on individual differences is one noted by Kemmis et al. (1977).
The recognition of difference depends on the drawing of boundaries, if the boundary is
imposed by the observer he may fail to recognise the boundaries that emerge from the obser-
vations, that is, the qualitative differences between the actions of the students themselves
(Ibid. p. 234).
Our grounded approach to the consideration of these difficulties circumvents some of this prob-
lem but leaves us with another: how can the differential use of ICT be explained?
There are other individual differences which may also contribute to the differential uptake of
ICT and the variance in learning outcomes. Applying these theoretical frameworks of individual
difference has helped us in an exploration of what may underpin these individual differences but
has also highlighted their incompleteness. Target children 1 and 5, for example, are both inferred
Analytic Imagers and Extraverts, yet their patterns of computer use are very different. Target chil-
dren 7 and 4 have similar patterns of activities but they are polar opposites in terms of RidingÕs

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14 S.J. Waite et al. / Computers Education xxx (2005) xxx–xxx
cognitive style and their use of programs and their learning gain are very different. In their study
of students in special schools, Riding and Craig (1999) found that adverse home circumstances
interacted with cognitive style in contributing to the likelihood of problem behaviours. The Ôwith-
in childÕ differences are thus clearly modified by external factors, such as the practical and social
context of learning (Cronbach, 1975).
11. Context for learning
Kemmis et al. (1977) set out three different curriculum paradigms: Instructional (drill and
practice), Revelatory (discovery, playing) and Conjectural (solving problems). He also postu-
lated a fourth, Emancipatory, associated with each of the above, which essentially took away
the tedious elements of work. In the classroom we studied, the children had tasks set by the
teacher which fell within these categories. They were expected to begin the afternoon session
with Starspell Plus, a drill and practice spelling programme. They also had Eggy day chal-
lenges where they designed and built a parachute and a car to convey an egg safely, which
built up their group problem solving skills. They were given a free hand to use and explore
the possibilities of software for research and presentation in a discovery mode. Some of the
children clearly enjoyed the playing and exploratory nature of their access to ICT; others were
using it principally as an information source for personal or educational purposes. As for the
emancipatory dimension, all the children commented in interview how the computers freed
them from the tedium of writing and rewriting. They also valued the presentational improve-
ment in their work, although they did not always seem to be as aware of the re-drafting pos-
sibilities. Despite comments such as
ÔIÕm reading it through now, because I never used to read it through. I used to do my work but
I never used to read it through after but now I do. I find IÕve got time to read it nowÕ (Target
child 1)
sample work taken from first draft through to final version was often still fairly tight to the ori-
ginal. Furthermore, in spite of the childrenÕs lauding the computersÕ spell-checking facilities, spell-
ing mistakes often remained.
The teacherÕs aim was for the students to have a choice about how they approached the
subjects. Most of the tasks he set were open-ended for completion over a number of days
or weeks. The children could then work on them as and when they wished. New programs
would be demonstrated but the extent to which they were utilised was largely in the hands
of the children. The differences observed in the use of programs therefore reflected their
preferences.
However, there may be significant effects for individualsÕ learning if they did not find the tasks
set easy to understand or structure as this introduced an additional layer to their work. If they
worked more slowly than others did too, this would then mean they were unlikely to achieve
all the task within the time frame allowed. Successful working within Revelatory and Conjectural
curricular paradigms needs to be framed by effective explanations and support. Repeated experi-
ence of non-completion of tasks could lead to lower levels of self esteem and incomplete learning.
In a follow up interview, two pupils reflected:

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We actually writ everything but never got it finished. No. We would just move on to a different
subject. (pupils, Year 7, secondary school)
The higher attaining pupils seemed to be able to adapt their preferences and turn their hand to
most tasks. They also saw tasks to completion more than the low attainers did. The individual
effect of ICT may therefore have been masked by this overall attainment effect. It is likely that
where students are unable to adapt their learning style, they will be disadvantaged, unless the task
is adapted for them. Where students are unable to complete tasks, they can however find alterna-
tive activities on the computer that do not interfere with othersÕ learning. A danger exists in asso-
ciation with this; because the ICT makes off task behaviour less visible and disruptive, it is less
likely to be detected and redirected by the teacher.
The home use of computers would appear to be a significant factor. Home computers are
rapidly becoming a mainstream household item. 81% of households had access to a computer
in the home (BECTA, 2002), up from 78% in the 2001 survey, and 98% of 5–18 year olds
used computers at home, school or elsewhere, with 92% using them at school and 75% using
them at home. The gap appears to be more in how these computers are used (Facer, 2002). In
our small study we found those children who were using the computer for homework alone
made less learning gains than those who used it for multiple purposes. This may be that
the children are less confident with the computer, feel less attracted to its use, or merely
use it as they might use a fountain pen to Ôcopy up in neatÕ or a book Ôto find out factsÕ.
For these children, the computer does not appear to be transforming the way they think about
things. Cox et al. (2003, p. 13) suggest there is a lack of congruence between childrenÕs home
and school experience of computing which fails to blend out-of-school and within-school use
constructively. However, just bringing the opportunity to ÔplayÕ into school would not appear
to be adequate since the class teacher in our study allowed this at times within school in con-
trast to the very focused skills based teaching of ICT in many primary schools. There would
appear to be some internal factor which predisposes children to be exploratory or not and
further research is needed to try to identify what this might be. Pragmatically, a more explor-
atory approach might be fostered by scaffolded use of ICT, where pathways of exploration are
signposted for children.
12. Implications for teaching and learning
While the use of computers may mediate a Ôone size fits allÕ teaching approach to some ex-
tent (McDonald Ingvarson, 1997), we suggest that further progress might be made by a
more differentiated use of them. Pittard, Bannister, Dunn, and Riding (2003) in their summary
of large scale studies of the impact of ICT on attainment, motivation and learning point to
the type of ICT use as being important in affecting attainment, where spontaneity and flexi-
bility are favoured. They also suggest that Ôusing ICT in the right ways can help personalise
pupil learning, develop pupil-centred and collaborative approaches to learning and offer new
ways of supporting and enhancing pupilÕs conceptual learningÕ (Pittard et al., 2003, p. 14).
Attention to the differences in pupilsÕ response to ICT may help to direct how these Ôright
waysÕ can be addressed practically.

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If the availability of ICT does not directly liberate some pupilÕs learning, then some other
means of making it more appropriate to individualÕs learning needs to take place. This might
involve creating more structure in open-ended tasks (Hoyles and Noss, 1992, cited in Cox et
al., 2003, p. 17), The teacher in our study orally outlined ways to approach tasks such as
ÔWho, What, Where, Why, HowÕ and, on some occasions, this was sent through the interactive
white screen to each pupilÕs computer, but wholists and some low attainers may have benefited
from this guidance being available as a permanent writing framework. This would then have
freed them from an initial stage in the task of trying to create a structure for their exploration.
They might also have benefited from more paired work where a different approach to ICT use
would allow them to experience beyond their capabilities or inclination. In addition, proficiency
in ICT offered a source of self-esteem for some low academic attaining students and anxious
personality types. Choosing partners with either strength in subject knowledge or ICT skills
would enable teachers to combine students to give both partners a zone of proximal develop-
ment to encourage their learning and opportunities to build their self-esteem. Collaboration
which builds on different more responsive and flexible learning strategies rather than styles
might also help to scaffold learning (Cassidy, 2004).
Analytics and anxious personality types could have found difficulty in finding their way around
programs, being unable to retain the overall picture of how it works. On the other hand, they were
probably helped by activities such as writing a leaflet about how to use programs, as this allowed
them to sequentially organise the material. Verbalisers and extraverts were also enabled to learn
socially through the extended means of communication and through joint work projects which
often culminated in a Power Point presentation. A variety of tasks would support this broader
appeal of ICT for different cognitive styles.
A potential disadvantage of ÔlabellingÕ, as we have done for the purpose of discussion in this
paper, is that in real life situation, it may diminish attention to the complexity of interrelationships
through oversimplification. It is likely that each individual will have several, possibly conflicting,
individual differences and that these will tend to dominate and interact in different contexts. Our
study suggests that use of a single learning style inventory might yield little of practical value to
personalise teaching. Since it is the interaction of individual characteristics, tasks and programs
which is fundamental to the successful use of the ICT for learning, it is important to become
aware of individual preferences and differences at an early point so that contexts and tasks can
be adjusted or learning strategies supported. This might mean staff working alongside individuals
to observe their approach.
Staff sensitivity to individual learning differences may be usefully enhanced by having a lan-
guage with which to conceptualise them (Rosenfeld Rosenfeld, 2004). Chen and Ford (1997)
look forward to adaptive information systems which accommodate to individual differences, but
until such responsiveness exists, provision of different structure and support by the teacher
would enable the individual use of ICT to be tailored to maximise each childÕs engagement
and success. Coffield et al. (2004) refer to a Ôlexicon of learningÕ (p. 39) which allows teachers
to discuss their own and other learning preferences without labelling and praise the practical
value of JacksonÕs Learning Styles Profiler (2002 cited by Coffield et al., 2004) as a self-devel-
opment tool for teachers.

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13. Conclusion
ICT can be a powerful motivator (Bromfield et al., 2003) and an impetus for creative thinking
(Wheeler et al., 2002). Movement away from drill and practice software or Ôcopying upÕ handwrit-
ten drafts (Waite, 2004) and the increased use of open-ended tasks, which allow some students to
match the execution of tasks and selection of programs more to their preferred style introduces
more flexibility. However, some students appear to be unable to direct their use of ICT to the goal
of learning without further structure. Moseley et al. (1999, cited in Cox et al., 2003, p. 11) found
that effective explanations were key to good practice in pedagogy using ICT, where students were
involved and examples and counter-examples demonstrated.
There may be a correlation with attainment level. If pupils are very able, they appear to adapt
the ICT to their own purpose more than if they have lower attainment, so that benefits of ICT in
terms of improved attainment are further reinforced by high attaining students more flexible use
of ICT. However, if ICT and tasks are compatible to their learning style, lower achievers may be
enabled to improve their performance. Further studies are needed to investigate the inter-relation-
ship of types of programs, types of tasks and types of learning style and other mediating factors
such as pedagogy and motivation.
The development of an exploratory ÔplayfulÕ approach also appears to be an area worthy of fur-
ther investigation.
There remains a tension between the ecological study of ICT providing a richness of par-
ticular classroom contexts and the rigour of a more experimental design to enable a genera-
lisability of results. While naturalistic observation enables real cases to be considered, the
complexity of learning situations makes it difficult to be sure that effects are due to the appli-
cation of ICT. A more experimental approach might be needed to test out hypotheses derived
from close ethnographic observation. The learning outcomes for this study, for example, were
judged on the basis of samples of work, teacher assessments and SATs results, integral to
school life, and this presents some difficulties in comparison with more narrowly defined learn-
ing outcomes such as Riding and Grimley (1999) identified. Furthermore, we are unable to
separate the distinctive contribution of the ICT from the particular context of this case study.
Establishing correlations, let alone causality, is a fundamental problem with educational re-
search, given the many variables beyond the control of researchers (Rudd, 2001). Although
large scale studies can provide information about trends, they cannot explain what is happen-
ing at a micro level between the child, computer and their context. We hope that the richness
of our data will offer insights into factors influencing childrenÕs learning worthy of further
investigation.
This study only addresses implicitly the usefulness of ICT as a mode of presentation of learn-
ing material, and this is the primary focus for Kemmis et al. (1977) and Riding and Grimley
(1999). A distinction also needs to be drawn between this and its usefulness as a flexible tool,
among others, for pupil learning and the presentation of those learning outcomes. Somekh
and Davies (1991) in their discussion of pedagogical change arising from ICT argue that we
need to move
Ôfrom a view of teaching and learning as discrete, complementary activities to an understand-
ing that teaching and learning are independent aspects of a single activityÕ (p. 156).

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We would go further and suggest that these aspects are not independent within the single activity
but merge in the rich context of an ICT-rich social constructivist learning environment, where stu-
dents also learn through teaching others.
Further research is needed to explore whether a link with learning style exists and whether the
concept of learning style offers the best fit as a theoretical framework for the study of individual
differences and ICT. Despite one pupilÕs comment that Ôwe will still need teachers to turn the com-
puters onÕ in the future, implying ICT may absorb more active teaching roles, it appears from our
study that increasing use of computers in classrooms needs to take account of individual needs to
maximise pupilsÕ potential and that the teacherÕs role is central to facilitate this.
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