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Volume 2 - Issue 1 - Winter	  To What Degree are Undergraduate Students Using
their Personal Computers to Support their Daily Study
Practices?
KwongNui Sim, Russell Butson

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  Abstract

This scoping study examines the degree to which twenty two undergraduate students used theirpersonal computers to support their academic study. The students were selected based on their
responses to a questionnaire aimed at gauging their degree of computer skill. Computer activity
data was harvested from the personal computers of eighteen students and video footage of thestudents personal study sessions was gathered from a further four students. Three core themes
emerged: (1) Academic Use vs. Non-academic Use; (2) Computer Orientated vs. Paper
Orientated; and (3) Self-reports of Practice vs. Actual Practice. Overall results suggested threefundamental behaviors relating to technology use: 1] they were more likely to engage in nonacademic work than academic work on their personal computer; 2] they were more inclined touse paper-based approaches compared to digital ones despite the high rate of personal computer
ownership and internet access; 3] there was a disparity between students’ self-reports of thedegree to which they used their personal computers for academic purposes (high) and nonacademic purposes (low) to what we found from computer logs which showed academic use as
low and non-academic use as high. From these results we conclude that for this group of students
computers played an important role in their day to day lives, but the degree to which they wereused in their academic study was lower than we had expected.

Keywords: Student learning; E-learning; Higher education; E-literacy; Student study habits.

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  Introduction
As universities become more digital and computer technologies become increasingly
sophisticated and ubiquitous, understanding the extent to which students integrate thesetechnologies into their daily study practice is essential if we wish to understand their future
potential to advance learning (Butson and Thomson, 2011). Nevertheless, there has been little
research to date that explores students’ first-hand experiences of using new technologies tosupport their academic practice (Sharpe et al., 2005). For example, the degree to which students
today can be regarded as competent computer users in terms of utilising academic software inorder to excel in their studies is hazy. While the literature shows an increasing number ofundergraduate students own a computer and have a broadband connection (Aspden and Thorpe,
2009, Guidry and BrckaLorenz, 2010, Smith and Caruso, 2010, Dahlstrom, 2011), it is unclear towhat extent these personal computers are utilised in the academic study practices of students. The
typical assumption is that these ‘digital natives’ (Prensky, 2001) have a high level of computer
literacy and are therefore likely to utilise their laptops to support their learning in advanced ways.
It is also worth noting that much of the research we found into student use of computer
technologies has been based on surveys and interviews (perception data). By this we mean
students reporting on what they believe they do through post-event recollection. The primaryendeavor in regard to this investigation was to explore the use of datasets other than post-event
recollections. This would require us to locate data collection as close as possible to the students’
daily studying practices. Two approaches were explored and subsequently used: 1) computer
usage data was extracted from the student laptops (Computer Activity Data) and 2) student’s usedvideo cameras to film their study sessions within their home study space (Cohort Behavioral
Data). To ascertain if there was a difference between perception data and naturally occurring dataa traditional self-report questionnaire was included.
Prior to the commencement of the study, three core areas of inquiry were established:
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•

The degree to which students utilised their personal computers for academic purposes ascompared with non-academic use,•

The extent to which computer use had been adopted over traditional paper-based
approaches,•

The degree to which student self-reports (post-event recollections) align with data captureof their actual practice.
MethodGiven the exploratory nature of the study and the researchers social constructivist orientation, thestudy adopted the interpretive, naturalist enquiry approach of Guba and Lincoln (1989), and theanalysis approach of Constructivist Grounded Theory of Kathy Charmaz (2006). This approach
unpins the decision for the selection of a small number within a particular context to understandtheir experiences rather than a focus on generalisable findings. Two separate cohort groups wereused from conveyance and not for any particular purpose, one capturing usage data from their
computers (Computer Activity Data) and the other filming their home-based study practices(Cohort Behavioral Data) with considerably more input being required by this cohort. All
participants were undergraduates and all completed the Self-report survey. The Self-report Data
was gathered through a short questionnaire that ascertained the student perceptions of theirdegree of computer use. The top scoring students, those that self-reported as skilled computer
uses, were invited to participate in the study.
The Computer Activity Data: Eighteen students who self-reported as being skilled computer
users were selected for this part of the study. A software programme (ManicTime) was installedon their personal computers. This software captured usage information on applications used,websites visited, documents accessed and the associated times and durations involved. The data
was gathered over the duration of their first six studying weeks in semester one 2012. We wereconfident that this naturally-occurring activity data was going to yield rich information that was
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  going to be very different to the traditional perception data. It would allow us to review theapplications used, documents accessed, websites visited and periods of use. However, thenumerical nature of this data meant it couldn’t tell us much about the context in which these
digital activities were occurring. It was envisaged that the video capture that the students weregenerating of their home-based study practice would help situate the Computer Activity Data.
Cohort Behavioural Data: A second cohort consisted of four third year undergraduate students
who also rated themselves as being skilled computer users. Each student was asked to capture aseries of short video clips of their home study practices over a three month period. Over twelve
hours of video footage resulted. Our interest was on the context(s): to what extent did theparticipants integrate and interact with computer technology in their daily undergraduate study
habits.
These two datasets represented a mixture of students’ actual computer practices (Computer
Activity Data) and behaviours associated with study practice (Cohort Behavioural Data). While
Computer Activity Data captured what students used their computers for, Cohort Behavioural
Data was used to elicit data on how students used their computers within their study routines, andit gave us an insight into the context. Through these two datasets we hoped to gain some insight
into what students used their computers for.
Findings
Guided by our three core areas of interest, an analysis of the three data sets (Self-reported skill
level, Computer Activity Data, Cohort Behavioral Data) using the coding processes of groundedtheory resulted in three themes:-

Academic Use versus Non-academic Use-

Computer Orientated versus Paper Orientated-Self-Reports versus Actual Practice

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  Academic Use vs. Non-academic Use: From the Computer Activity Data (computer logs) weanalyzed the top three software applications, documents used and web sites accessed. The mostobvious classification was the academic or non-academic use. Academic refers to software,
documents or web services that were related to the participants’ academic study. Non-academic
includes all other uses, such as banking, entertainment sites, Facebook groups, etc. As shown intable 1 the computer logs revealed much higher non-academic use compared with academic use.
Table 1:
Computer log data: breakdown between academic vs. non-academic use.
Percentage (%)
No.

Academic

Non-Academic

10
20
90

90
80
10

of Students
15
2
1

For clarity values have been rounded to the nearest whole number

Paper Orientated vs. Computer Orientated: From the Cohort Behavioral Data, we analyzed thefour participants’ behaviors from their self-created 12 hours of filming. Coding the students
activity while they were studying revealed a clear distinction between behaviors that were paperorientated and behaviors that were computer orientated. By paper orientated we are referring torepetitive behaviors that did not include the use of their computer. By computer orientatedbehavior we are referring to repetitive behaviors that did include the use of their computer. As
shown in table 2 we found these students engaged in more paper-based activities during study
sessions than computer-based.

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  Table 2:
Percentages for comparison of paper orientated vs. computer orientated activities

Participants
12
34Percentage (%)
Paper
Computer
Orientated
Orientated
14
66
56
19
92
0
17
57

Note: The percentages do not distinguish between academic and nonacademic activities and for clarity values have been rounded to the nearest
whole number

Self-Reports of Practice vs. Actual Practice: We found a considerable difference between what
students thought they used their computers for from what they actually used them for. While theself-report questionnaire indicated students believed they used their computers mainly foracademic purposes the results from the computer logs suggested a very different story. As shownin Figure 1 the data shows students generally believed they used their computers more foracademic purposes rather than non-academic (red). However, the computer logs revealed that 17
out of 18 of the students used their computers significantly more for non-academic purposes thanfor academic.

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  This was reinforced from the Cohort Behavioral Data where students spent considerable time
during their home-based study sessions on a variety of applications not associated with academic
practice. As shown in Figure 2 below, this student often played around with music software (asin the illustration on the left) and regularly checked her emails (as in the illustration on the right),
while others had Facebook permanently on screen.

Figure 2: Non-academic work with technology8

The Cohort Behavioral Data also revealed that when these students did use their computers foracademic purposes they exhibited a more limited degree of proficiency than we were expecting.
	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
   	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
8
Consent was granted by participants for the use of the film files for research purpose.
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Volume 2 - Issue 1 - Winter	  For example, we had footage of a student manually adding references for an assignment. The
footage also captured her saying, “I’m just doing my psychology assignment … first of all I’m
going to compile a list of references just using Microsoft Word …” She continued at this task forsome time, repeatedly deleting large section when an error was made. She also commented about
her expectations of the process before she started typing, “This is probably going to take meages”. In the process, she said, “This is really time consuming”; “This is boring”; “God that is soannoying”; and “I’ve done this so many times I’ve just memorized reference lists”. The footage
duration showed that she took more than one hour to add one page of references. Whenquestioned later about her knowledge of software programs that automate much of this process,she replied she had heard of these but hadn’t used them.
Findings
As mentioned earlier, the core findings contributed to the development of three main themes,
namely, Academic Use vs. Non-academic Use, Paper Orientated vs. Computer Orientated, andSelf-Reports vs. Actual Practice.
All participants stated in the self-report questionnaire that computers played an important role intheir undergraduate academic practice. This is not surprising given the rapid increase in theownership of personal computers over the past five years by students enrolled in higher education(Aspden and Thorpe, 2009, Guidry and BrckaLorenz, 2010, Smith and Caruso, 2010, Dahlstrom,2011). It seems reasonable then to assume that these students would be leveraging the benefits
that computer technologies afford. However, our results did not support this. In fact we found thedominant use of their personal computers was for socialising (social networks such as Facebook
and email), personal web services (auction sites and online banking) and entertainment (YouTube,music and movies). This was followed closely by web services such as retail, news, health,television, movies, information on pubs, air tickets, etc. Academic use on the other hand was

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  generally limited. This was a surprise given the growth of study resources such as PowerPoint
slides, academic articles, and assignment guidelines being distributed in digital formats.

An analysis of the Cohort Behavioral Data (video footage) revealed that the participants exhibited
only rudimentary awareness and skills concerning the capabilities of their computers to enhancetheir academic work (e.g., file management, bibliographies, planning, word-processing, databases,
and analytical packages). In fact, the students in this study were completely unaware of the many
academic-related software applications either on their computers or available throughapplications offered by the institution such as bibliography programmes (i.e., Endnote, Zotera,
Mendley), planning programmes (e.g., Outlook, Evernote, OneNote), analysis packages (i.e.,
SPSS, NVivo, and MATLAB), and more generally (e.g., spread sheets, graphics software,
programmes to support PDF annotation).

Given the wide-ranging conscience in the literature on student’s computer savviness, we weretaken aback by the degree to which these students were using paper-based approaches. It was
clearly the preferred means of engagement regarding study. While the common format for course
materials for these students was text based and digital (i.e., Microsoft Word, PowerPoint andportable document formats), all engaged in extensive indiscriminate printing of these materials.
We were puzzled by this behaviour. We had assumed these students would exhibit high levels ofdigital orientated behaviour, instead we found they preferred to print material and apply
traditional study techniques associated with paper-based form. We wondered if this was due to alack of awareness of annotation and mark-up facilities, and the storage and retrieval capabilities
that digital formats offer or did it signal something deeper about student’s preference to use
paper-based approaches.
It is interesting to speculate whether students’ preference for paper-based approaches is the lackof skill/knowledge with the technology or whether it could be due to a dependence on paper167	
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  based approaches inherent in higher education. It seems reasonable to assume that the way inwhich an institution embraces and implements technology is going to have a bearing on the way
in which students will engage with technology in their higher education learning. Althoughstudents are comfortable with technology and see it as integral to higher education (Dahlstrom,2011), they may expect that teaching staff will model the academic use of technology (Smith andCaruso, 2010).
Given the dominance of paper-based approaches it is not surprising that the Cohort Behavioural
Data (video footage) showed that students were very reliant on their printers. In fact you could
say that for academic use the computer was of little use without an attached printer. From thevideo footage it appeared that the computer was simply a device that was used to accessdocuments that the student then printed and stored in ring binders. Incidentally, all used ringbinders to store and categorise their printed resources.
Finally, it is worth noting that much of the literature we found on the role of computer technology
in supporting learning in higher education was based on perception data. Part of this study was toexplore the validity of this approach by examining whether there is a difference between students’
perceptions and actual practice in relation to their computer use and their computer literacy. The
results from Computer Activity Data suggest that there is considerable difference.

Studies employing perception data are typically optimistic regarding student dependency on
personal computers for supporting study. A vast array of studies claim that computer technology
now plays a significant role in supporting undergraduate education (Aspden and Thorpe, 2009,
Dahlstrom, 2011, Guidry and BrckaLorenz, 2010, Smith and Caruso, 2010) and that thisincreased use is resulting in learning efficiencies for students (Smith et al., 2009). There is adegree of acceptance from much of this material that presupposes students “use technology inacademia to give them access to resources and progress reports, make them more efficient as
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  students, facilitate connecting with others, and make learning more relevant and engaging”(Dahlstrom, 2011).
The naturally-occurring practice data captured in this study did not support these claims. Why
were our findings different? When we looked only at the perception data (self-reportquestionnaire) gathered from our participants we found the conventional pattern appearing. The
difference only occurs when we accessed the naturally occurring student activity. It gave us away of ‘seeing’ what students did (and did not do) rather than what they said they did or did not
do. As Starr and Fernandez (2007) noted, self-reported behaviours can be quite inaccurate fordescribing practice and thus we believe there are grounds to question the veracity of studies thatuse post-event capture to represent actual practice. Furthermore, participants’ “perspectives inaction” (records of behaviour) and/or “perspectives of action” (accounts of behaviour) (Belk andKozinets, 2005, p. 132) should be taken into consideration when studying practice.
In summary
The aim of this study was to explore third year undergraduate students’ practice of using theirpersonal computers in their independent study sessions. While the findings from this study are
specific to the cohort groups involved and are therefore not generalisable, the results do offer new
understandings and insights into the use of computers to support undergraduate study. From thisstudy, students were found to be active computer users and highly computer literate in nonacademic use. Their academic use, in terms of practice and literacy, however, was low andlimited. Students were also found to favour working in a paper-based manner rather than a digitalone. Finally we found a clear difference in outcomes from perception data to naturally occurringdata. We believe this finding brings into question the current dependence on perception data toreveal authentic, situated practice.

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Volume 2 - Issue 1 - Winter	  We are confident that this study will promote a deeper conversation about the role of technology
in higher education and the use students currently make of personal computers/devices to support
their study. Perhaps more research on larger and more diverse groups of students could beconsidered. Additionally, authentic and situated behavioural data should be employed inresearching technology use. The difference found between perception and practice data signals
the need for a substantial shift in the way we understand and gather data in this emerging field.
Acknowledgements
To Carla Thomson, University of Otago, for her contribution to the Cohort Behavioral Data.
The support and generosity of Dr Sarah Stein (Senior Lecturer at HEDC), without which thepresentation at The European Conference on Technology in the Classroom (ECTC) in Brighton,
UK on 11th-14th July 2013 could not have been undertaken.

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Belk, R. W., & Kozinets, R. V. (2005), Videography in marketing and consumer research.
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Dahlstrom, E. (2011), Connecting student data from ECAR and CDS. Educause, 1-7.
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  Guba, E. G., & Lincoln, Y. S. (1989), Fourth generation evaluation, Newbury Park, Ca, Sage.
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