“Architecture Students Hate Twitter and Love Dropbox” or
Does the Field of Study Correlates with Web 2.0 Behavior?
Martin Ebner
Information Technology Services / Department of Social Learning
Graz University of Technology
Graz, Austria
martin.ebner@tugraz.at
Walther Nagler
Information Technology Services / Department of Social Learning
Graz University of Technology
Graz, Austria
walther.nagler@tugraz.at
Martin Schön
Department of Life Long Learning
Graz University of Technology
Graz, Austria
martin.schoen@tugraz.at
Abstract: Teaching at universities can be seen as service on students. One of the most important
parameter for a successful service is to optimize targeting. To meet the needs of students
according to e-learning services best, an annual questionnaire amongst freshmen is carried out at
Graz University of Technology by its Department of Social Learning since 2007. The poll takes a
detailed look on digital device usage as well as on IT and Web 2.0 competences of the freshmen.
This unique survey in Austria reflects the media behavior of young people aged 18 to 22 and can
be compared to bigger reports like the German JIM-study. It is therefore an important input to the
analysis of our youth culture. Special focus of this year´s survey was laid on the question, whether
the field of study correlates with any of the polled elements? Is there a need for a specialized elearning service subject to the field of study? To answer these questions more precisely also a
project has been started analyzing special e-learning requirements on base of oral interviews with
selected teachers of different faculties. Apart from that, this publication offers all relevant results
of this year´s survey.
Introduction
Modern media change faster than ever before in human society. Today Web 2.0 (O' Reilly, 2004) as well as elearning 2.0 (Downes, 2005) appear to be buzzwords of former e-learning activities. The use of wikis, weblogs,
podcasts as well as e-portfolios is not trendy anymore; it has become accepted in technology enhanced learning
settings. Furthermore, mobile technologies, for example tablet computers or smartphones are extensively used for
different purposes daily; compare studies like the mobileintentindex (see http://www.intentindex.com/mobile/ visited December 2012). Bearing in mind that technology is just the enabler for different learning and teaching
scenarios it seems to be a logical step that the field of mobile learning is a fast growing one too and may have a great
impact and possibility for the next generation learners (Kukulska-Hulme & Traxler, 2005) (Schön et al, 2011). In
2001 Prensky already mentioned "Our students have changed radically. Today's students are no longer the people
our educational system was designed to teach." However, latest since then a worldwide discussion rose about our up
growing youth and their digital life. Does a so called “Net-Generation” exist? Does the behavior of the next
generation arbitrarily differ from their parents´ one? If technology is an essential part of daily life, how does it
influence learning and teaching? At first these questions lead to a number of publication addressing the upcoming
generation named "Net-Generation" (Tapscott, 1997), "Digital Natives (Prensky, 2001), "Generation @"
(Opaschowski, 1999) or "Homo Zapiens (Pelevin & Bromfield, 2002). Afterwards different publications were
written stating that this new generation has different habits like multi-tasking and new ways of communication
(Oblinger & Oblinger, 2005) (Oblinger, 2005). Green & Hannon (Green & Hannon, 2007) mentioned that "the use
of digital technology has been completely normalized by this generation and it is now fully integrated in their daily
life." All these initial research reports have one thing in common: They argue that the new technologies influence
the behavior of the young learners and that they are not able to live easily without their digital devices. Nevertheless,
all those publications lack on detailed evaluation reports or long term studies as well as empirical analyses. To
overcome that scientific gap a number of different studies have tried to address to this research questions since 2005.
For example: Conole (Conole et al, 2006), Bullen (Bullen et al, 2008) and Margaryan (Margaryan, 2008) pointed out
that students' daily life is strongly influenced by new technologies, but learning processes occurs very traditional in
their educational institutes mostly without using such technologies. Even more, two meta-studies of Schulmeister
(Schulmeister, 2009) (Schulmeister, 2012) summarized that the technological environment of today's learners are
changing, but no effects can be carried out concerning their learning behavior. The use of Web 2.0 technologies is
said to be rather low. On the other side, the German JIM-study (JIM Study, 2012) pointed out that in the last year
mobile technologies, especially the number of owned smartphones, increased arbitrarily amongst children between
12 and 18 years. More than 50% of the children own a personal smartphone and have mobile internet access. Similar
the Swiss JAMES-Study reported that already 79% of our youth are using smartphones and more than 66% have
access to mobile internet (James-study, 2012).
In general, the discussion is from high interest, especially for educational institutions like universities.
Consequently, the Department for Social Learning (DSL) as part of the Information Technology Services (ITS) of
Graz University of Technology (TU Graz) is doing a long-term study since 2007 (Nagler & Ebner, 2009) (Ebner &
Nagler, 2010) to take a very close look on freshmen’s IT competencies, their technical surroundings, their e-learning
habits at secondary schools level as well as general Internet and detailed Web 2.0 usage. This year DSL took a
closer look on student groups separated by their field of studies. Due to the fact that e-learning departments should
address exactly the needs of each faculty, one of our research goals is to find out whether there are differences
amongst the beginners due to their field of study and how we can react and prepare for it. Apart from the freshmen
survey DSL has started investigations on that topic this year (2012) too. On base of intensive oral interviews with
selected representative teachers of the differing faculties we try to find specifics and unique characteristics of their
teaching activities such as lecturing and mentoring. The focus is on different methods, media, and didactics but not
on the content itself. These interviews are than transliterated and analyzed. The results will have influence on the elearning services offered by DSL and will be topic for further publications.
The Study
Since winter term 2007 freshmen beginning their studies at TU Graz have been invited to join an annual two-day
information event called “Welcome Days at TU Graz”. It is special arranged for the freshmen and normally well
attended by about 50% of them. TU Graz is a medium-sized university with about 12.000 students and there are
1700 freshmen each year on average. The “Welcome Days” are presented by alumni TU Graz and managed by DSL
in cooperation with other departments of the university. Aim of the event is to help freshmen getting along with TU
Graz services, service institutions, as well as duties and possibilities coming with their studies. In the end of the
event they are requested to go through a paper pencil based form prepared by DSL. For sure, the asking could be
done in an electronic way but the average response is empirically very low by that. One part of the questionnaire
deals with the Web 2.0 behaviour of the freshmen, another one with their technological equipment regarding
laptops, mobile devices, and Internet accessibility. This year´s questionnaire resulted in 715 completed forms. Since
2007 DSL has collected a sample of n=4205 in total (n2007=578, n2008=821, n2009=757, n2010=702, n2011=632, and
n2012=715).
In the following the results and findings of this year´s (2012) survey are discussed. They are presented in
comparison to the results of the former years to analyse and point out changes, progresses, and trends. Because of
the fast shift of technology it has been necessary to adapt the questionnaire year by year a bit. Yet, we tried to keep
the thread through the analysis to keep it comparable over the six years of survey.
Results and Findings of 2012 Survey
Which Trends Can be Seen Towards Technological Equipment?
As in the years before the category “technological equipment” results in the most evident outcomes. Compared to
last year´s survey (2011) all trends can be approved. IPod and MP3 players are on their way down; PC again topped
the high part of more than 80%; no changes according to Apple computers but a lower part for laptops and netbooks
once again. This decrease can be interpreted at the expense of the clear increase of smartphones slowly replacing
functionalities of laptops. The big increase is mostly due to the increase of Android systems (over 55%). IPhones too
did put on a bit (18%) but Symbian lost instead whereas the total number on phones approximately remained the
same. Nearly the same amount of loss on classic mobile phones was added to smartphones (compare figure 1). Be
sure to notice that the students were asked both, first according to their mobile phone in principle – whether they
possess a classic mobile phone without touch screen or a smartphone with touch screen and apps – and then indicate
the operating system additionally. There were only a negligible number of students who did not state the operating
system. It is of further interest whether the small but evident increase of iPad (doubled) and other smart tablets may
compete against smartphones in the future. E-readers still seem to be no attractive devices for freshmen though the
market is constantly on the rise as German statistics quote (Statista, 2012a). Still there is no need for splitting into
different e-readers for detailed analysis.
Figure 1: Comparison of devices used by first year’s students at TU Graz between 2007 and 2012;
The selection “Other mobile” of 2010 includes the selections “M: Symbian” and “M: Windows”
The average freshman of 2012 has a PC and a smartphone – most likely running an Android system – as well as
assumable a laptop or netbook and possibly an iPod. The number of those using more than one workstation is little;
close to 2% for Mac and Windows PC together, less than 1% running all three systems. It is more common to use
Windows and Linux systems (about 7%) than Mac and Windows systems. That means that Linux systems are most
often combined with Windows; there is only less than 2% using Linux alone. There is no one using only a Mac and
a Linux computer together whereas the usage of iMac and/or iPad is not limited only to Mac users. All in all, we can
state a slight increase on mobile devices and an increase of digital devices in total.
Which Trends Can be Seen Towards Communication Behavior?
As in difference to the year before (2011) we can see glut in the communication market, so to say. The boom of
social networks seems to be over; they became accepted like Facebook or simply ignored like Google+ and others.
There is little loss to be recognised in each communicational way (except VoIP). For all of them but Google+ this
loss is not meaningful. Google+ did not make it so far. The detailed analysis (compare figure 5) shows that Google+
is well known but rarely used; it seems not to become a second Facebook or even replace it. Furthermore, we cannot
state that any of the communicational ways has an impact on the others as it could have been recognized for the last
two years caused by the booming of Facebook. On the other hand it means that Facebook has not displaced Skype
according to video communication or chat functionality. All in all, communication in total has gone down for about
7% compared to last year (2011) but still is higher than two years ago (2010).
Figure 2: Comparison of communication behaviour of first year’s students at TU Graz between 2007 and 2012
Values similar to answers given for “often” plus “daily use”
Which Trends Can be Seen Towards Internet Access at Study Home?
Figure 3 left: Comparison of Internet access at study home of first year’s student at TU Graz between 2007 and
2012, the selection “All” is new since 2011
The access to Internet at study home again changed since last year´s survey (2011) according to mobile access.
ADLS still remains the most popular access to Internet unaffected by the other accessibilities. Modem access is on
the rise as it has been since 2010 and already reached “half-way” towards ADSL connection. The trend to multiple
access is therefore given, which can be seen in figure 3 displayed by the category “All”. About 7% of the polled
students own all three types of access at study home. This goes hand in hand with the slight rise of mobile devices
offering Internet access to be seen in figure 1 (compare “iPad” and “other smart tablets”). But the number of those
who did not answer the question and therefore are to be assumed for having no Internet access stays about 2%
(similar to last year).
Which Trends Can be Seen Towards the Usage of E-learning Platforms at Secondary School Level?
According to the usage of e-learning platforms at secondary school level we can postulate a slight constant increase
of activities at all (compare figure 4 and figure 5). The values of figure 4 represent overall summations during the
last four years (2009 to 2012) according to the “usage of Moodle as a school´s e-learning platform” and “usage of an
e-learning platform at school except Moodle”. It is very obvious to see that e-learning activities at secondary school
level are slowly but consequently becoming more relevant. We can state that a quarter to nearly a third of all
students had often or daily e-learning activities during their secondary schooldays. Be sure that figure 4 does not
contain all elements listed in figure 5.
Figure 4 left: Overall comparison of usage of e-learning platforms secondary school level for learning efforts of
first year’s students at TU Graz between 2009 and 2012
Figure 5 right: Comparison of usage of e-learning platforms and PC in general at secondary school level for
learning efforts of first year’s students at TU Graz 2012
If a closer look on that part of the survey is taken, an increase on the usage of Moodle as the most important elearning platform at secondary school system in Austria can be pointed out. Though the increase is mostly caused by
an increase of “rarely usage” (from 25% to 33%) at the expense of “no usage” (from 48% to 35%), there is an
increase in the categories “often usage” (from 16% to 20%) and “daily usage” (from 7% to 9%) too. For the
selection of any other e-learning platform except Moodle we have no noteworthy differences compared to last year
(2011). For all the other selections shown in figure 5 only little differences according to last year´s results (2011) are
shown; therefore only this year´s results (2012) are shown. A sligth increase of up- and download activities can be
recognised, but with no significant evidenve. In other words, computers are fixed parts of secondary school life or
used for learning activities in general; if a school offers an e-learing platform than it is likely Moodle; learning with
the Web in principle still has not become an average way to work with.
Which Trends Can be Seen Towards Web 2.0 Activities According to General Usage and for Learning?
The final part of the survey explores the Web 2.0 competences. All results of this year´s survey (2012) according to
Web 2.0 can be seen in figure 6 and figure 7. We listed a series of most relevant Web 2.0 platforms and offers
structured by similarity of purpose (for instance: applications for communication, applications for online desktop
computing …). We adapted last year´s splitting of each element (Web 2.0 tools) into different relations by adding
the selection “no use” to the relation “ordinary use” and “use for learning”. So for each element students have to
indicate whether they “know” it; in case it is known, how intense the ordinary usage and the use for learning
purposes is (each with selections: “no use”, “rarely”, “often”, or “daily”); furthermore, they has to state whether they
use it “actively” in the meaning of editing or only consuming. Therefore the values exceed the 100% level because
of that multiple answer possibility given for one element. It is important to have both, the information about
“general use” as well as the one for “learning use” to find out, whether there are components that are typically used
only for one of the purposes. For the aspect of informal learning it could be crucial to know the general use.
Furthermore, be aware that the values for not checked elements are not part of the figures 6 and 7. First of all it must
be mentioned that it was important to add the selection “no use” to cover the status of knowing a tool but not using
it. For the surveys before this year (2012) such inputs were to be found either in “not knowing” or “rarely use”.
There was no chance of finding out the actual part of students not using a tool but knowing it, which is a strong
statement about acceptance of a Web 2.0 tool. As a result of the new selection the parts of “unknown” and “rarely
use” often melt down to a little compared to last year´s results (2011 and before). Furthermore we see that nearly all
elements were more often checked “unknown”, “no use” and “rarely used” together than last year´s (2011) checking
“unknown” and “rarely used” combined. But on the other hand we have a distinct increase of usage in general as
well as for learning activities for a couple of elements (such as Videocasts, Audiocasts, and management tools). All
in all we have an increase of often and daily usage for learning of more than 17% since last year (2011), which goes
along with the increase of Moodle usage and (mobile) devices as well as approves the trend to intensified Web 2.0
usage for learning (Ebner et al, 2011). Nevertheless, this increase is due to a massive growth of “often use for
learning” of about 36%. In other words, there is a minus of about 19% according to “daily use for learning”. This
goes along with the general decrease of communicational behavior (compare figure 2). Nevertheless, it has to be
mentioned too that some of the tools (exemplary Facebook, YouTube, wikis, and SMS) are in use by less students
than 2011 but those who use it, use it more intensively. Furthermore, it is interesting that the average use and the use
for learning does not differ that much according to “often” usage. However, future surveys will show, whether a top
of Web 2.0 competences have been reached already for learning efforts or it is just an annual fluctuations. Finally,
the selection “active” induced misunderstandings and will be revised for future surveys. Therefore it will not be
discussed in detail this time.
Figure 6: Usage of Web2.0 and Internet offers of first year’s students at TU Graz in 2012, part 1
As an example we can pick the first element “Google” and see (compare figure 6) that it is used daily by
approximately 83% (nearly everybody uses Google daily or often); even more, we see that Google is used by 48%
daily for learning (54% in 2011) and 92% for daily or often learning (93% in 2011). Figure 6 and figure 7 point out
very impressively which Web 2.0 element is in major use for learning purposes. The difference between using an
element for learning “daily” and using it “often” is about minus 70% in general, which means that only a very few
tools are relevant for daily learning usage. Only Google still is used for learning more often “daily” than “often”.
The highest rise we can state for the usage of Dropbox for learning efforts. It nearly tripled from 8% to 22% for
often and daily learning usage; so every fourth student uses Dropbox. If we take a further look at other collaborative
tools we cannot affirm a significant change; Googledocs keeps its 5% users, Etherpad still is absolutely not in use
neither for general use nor learning purposes. Etherpad stands for a couple of Web 2.0 elements that are even not
known by a remarkable part of the students (40%); such as Googledocs, Second Life, bibliographic systems, Google
Hangouts, Bookmarking, or QR-Codes. Even Adobe Connect and the social network for business purposes from
Germany called Xing are little known. There is another group of elements that are to be mentioned: those who are
known but very little used, such as Twitter (2% unknown but 83% no use). This group is joined by well-known and
“famous” international social networks like Google+ and MySpace as well as the former important social platform
for German speaking countries called StudiVZ.
Figure 7: Usage of Web2.0 and Internet offers of first year’s students at TU Graz in 2012, part 2
The PCA HCA Analysis
As for the last two years (2010 and 2011) we undertook a PCA HCA analysis for this year´s survey (2012) too. The
“Principle Component Analysis” (PCA) and the “Hierarchical Cluster Analysis” (HCA) are standardized methods
for statistical analysis of correlation matrices that recovers deeper relations and interdependency amongst the
multiple elements (variables) of the survey (Ebner et al, 2010). Which one of the elements is significant to the
others? Which one comes along with others? Can we find groups and patterns of students having similar
characteristics according to the survey´s topics and elements? With these methods we find out the impact of
variables on each other and therefore can find clusters of dependent elements.
One result of that factor analysis is that we can slightly differ between four groups of users:
 Group 1: rather using wikis, blogs, forums, Twitter, Google applications, management tools (todo-planer
and online-calendar), RSS, SIP, QR, Linux PC, and Dropbox
 Group 2: rather using Google search engine, word and spreadsheet processing, smartphone, SMS, e-mail,
Facebook, e-commerce (such as Ebay, Amazon …), e-banking, YouTube, widgets in general, as well as
Dropbox and online-calendar
 Group 3: characterized by secondary school level experiences such as MS Office, Moodle, up- and
downloading using school equipment
 Group 4: is characterized by owning an iMac, iPad but no Windows PC, is less gaming
Group 1 is mostly characterized by the usage of distinct software of the Web 2.0 phenomena, we may call them
“Web 2.0 experts”, whereas group 2 represents the so to speak “main-stream behavior” of common people with
Internet technologies. Surprisingly, Dropbox also is part of the common group too. When we take a look at figure 7
we see that Dropbox even is more accepted and used than Videocasts which are established learning resources. It
also bears a comparison with Skype according to its often general usage and often usage for learning. It seems that
Dropbox did make it this year and already gains widely spread awareness. Group 3 stands for a more intensive
experience with basic e-learning habits at secondary school level. Group 4 can be seen as “typical Apple user” that
is only further characterized by less gaming activity than the other groups. This means that typical Apple users
mostly differentiate by their distinct usage of Apple products. So as one result of this year´s (2012) survey we can
state that among all students and regardless to the fields of study we have a tendency of general separation into four
groups of modern technology intelligence. But what about the different fields of study. Is it possibly to relate
divergent behavior to different fields? To answer this question a discriminant analysis has been carried out that will
be discussed in the following.
Special Focus on Fields of Study
As argued above, it is important to have a differentiated understanding for services offered to different target groups.
In our case it is obvious that one possible differentiation is to split e-learning services by fields of study. The special
analysis aimed to find out whether the field of study correlates with Web 2.0 and modern technology behavior or
not. In case it does, what is needed to specialize our e-learning services? With the method of “Discriminant
Analysis” (DA) it was tried to predict a student´s field of study on base of answers given in the questionnaire. If this
can be made possible we can state that fields of study differ according to their technology usage. At TU Graz there
are currently (2012) 19 different bachelor studies to enroll for. To prove the method, first it was needed to simplify
the amount of studies by grouping them into similar ones. Thereby the 19 studies were accumulated to five major
study groups called clusters, which are in detail:
 Cluster 1 “Architecture”: containing studies: Architecture
 Cluster 2 “Civil and Mechanical Engineering”: containing studies: Civil Engineering with Environment and
Construction Management, Mechanical Engineering, Mechanical Engineering and Business Economics
 Cluster 3 “Electrical Engineering and Computer Science”: containing studies: Electrical Engineering,
Electrical Engineering and Audio Engineering, Biomedical Engineering, Telematics, Computer Science,
Software Development and Business Management
 Cluster 4 “Mathematics and Physics”: containing studies: Mathematics, Technical Physics, Geomatics
Engineering, Earth Sciences
 Cluster 5 “Chemistry and Biology”: containing studies: Chemistry, Chemical and Process Engineering,
Molecular Biology, Environmental Systems Sciences / Natural Sciences-Technology
For the next step we used the SPSS Discriminant Analysis. During this procedure every single variable of a defined
set of 107 variables was checked for its discriminating quality. We considered for the analysis all variables
describing the use of internet (connections, devices), known applications from school experiences and the intensity
of usage of different applications. A variable that correlates with another variable but is of lower “discriminating
significance” drops out of the process because it has no further individual value for the prediction. In that way the
result is a list of variables which can be seen in table 1. It must be mentioned that for describing the Web 2.0
behavior we only used variables on frequency of general usage because they are highly correlated with those used
for learning.
Table 1: Listing of variables resulting from discriminant analysis; residual variant causes the rank list
On base of answers given to these 12 variables we can predict the field of study in 41% of all cases. The prediction
is best for cluster 1 (61 correct predictions). Cluster 2 and cluster 3 have around 40% correct predictions. Cluster 5
has 31% correct predictions and cluster 4 only 22%. Figure 8 shows the distribution of the students in a perspective
with two dimensions and two discriminant functions. You can recognize a trend in grouping the students. With
every additional function the discrimination and separation of the groups are getting better. But be sure that this
result does not mean that, for instance, cluster 1, “architecture”, has more or less competence in the domain
described by the variables in the list. Only further computations indicate that the students of “architecture” over all
these variables have less competence and cluster 3, “Electrical Engineering”, has the most. So in case a freshman
tends to answer these variables positively he/she most likely belongs to cluster 3. In other words, we can assume that
the need for basic e-learning support is highest for cluster 1, “architecture”, and can therefore set focused actions
such as awareness campaigns or intensified consulting for this group of students and teachers.
Figure 8: Graphical illustration of the results of the discriminant analysis;
the right sided numbers stand for the clusters 1 to 5
Discussion and Conclusion
Since 2007 the annual survey done by DSL of TU Graz among freshmen has reflected the changing habits of our
youth according to their Web 2.0 competence in general and for learning skills. This year´s survey (2012) results in
a clear increase of smartphones (first of all android systems) at the cost of classic mobile phones. There is an
ongoing trend to more mobile devices as much as to devices in general and to (mobile and modem-based) Internet
access as well. Furthermore, we see a stop in the pervasiveness of the boom of social networks. Google+ is no
alternative to Facebook yet. Although the usage of Web 2.0 offers has decreased a bit, too it is used more intensively
and for more learning purposes by those who use it. There are a couple of established Web 2.0 elements used for
learning and some that are in rise such as Dropbox up to 22% this year (2012). The awareness and reliance to use the
Internet as a cloud for storing, sharing, and saving own data has reached a remarkable level and can become
important for future teaching and learning scenarios. According to this year´s research question of the study (2012) –
Does the Field of Study Correlates with Web 2.0 Behavior? – and with the help of discriminant analysis we can state
that there is a slightly verifiable difference between the technological habits according to the fields of study. Most of
the freshmen have the prerequisites to use the extensive range of electronic educational offerings of our university.
Nevertheless, this analysis is a further step forward to optimize our e-learning services.
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