D-Move: Ten Years Experience with a Learning Environment for
Digital Natives
Otto Petrovic
Institute for Information Science and Information Systems, Karl-Franzens-University of Graz,
Universitaetsstrasse 15/G3, AT-8010 Graz, Austria
Keywords: Digital Natives, Learning Environment, Learning Method, Delphi, Collaborative Learning, e-Learning.
Abstract: D-Move is a learning environment for Digital Natives, composed of different software modules and learning
methods, mainly based on constructivist-connectivist learning theories. Digital Natives have the Internet as
their mother tongue and probably require different learning approaches. D-Move started more than ten years
ago with the aim to reach learning objectives in settings that are part of Digital Natives’ everyday life. The
article shows the results of three different phases: Text messaging, multi-channel support and finally,
expansion by a Delphi-based research environment.
Digital Natives’ mother tongue is the Internet and
the digital language of computers (Jones 2011).
They are born after 1980 and are raised with digital
technologies. They are used to obtaining information
quickly, possess a high amount of ad-hoc-
communication, work and communicate in form of
multitasking and use mostly interactive digital
media. As a consequence, new challenges for
learning environments evolve to support them. To
design the learning environment D-Move we
identified five key challenges:
Increasing Amount of Information
Digital Natives are faced with a much higher
amount of information than their parents.
Furthermore, distinguishing between important and
less important information is becoming more and
more difficult. Thus, only providing more
information is viewed by Digital Natives not only as
useless but even as harmful (BITKOM 2014). One
important way to solve this is to move from pure
consumption of broadcasted information such as in
television or traditional lectures to self-directed
acquirement of knowledge and competencies by
acting in the real world (Klippert 1999).
Increasing Need for Authenticity
The strongly increased amount of information
sources lead to greater difficulties in verifying the
source of information. Search engines, used as the
main information source of Digital Natives, deliver a
vast amount of information but verifying them leads
to discomfort and uncertainty. Thus, the need for
authenticity of information sources increases. One
strategy to reach this goal is to obtain the
information and knowledge in real world practice or
at least in learning environments that are perceived
as being close to reality (Linten 2009).
From Lean Back to Lean Forward
More and more Digital Natives prefer lean
forward media with a high amount of personal
engagement and interactivity. They like to create
content by themselves in form of pictures and
videos, posts or comments and share them within
their social media networks. Thus, they take over
core competencies from traditional information
sources like newspapers or television as well as
lectures at a university (Tapscott und Williams
2008). This active role of Digital Natives in learning
processes are based on co-production of learning
content in highly engaged social processes (Lee und
McLoughlin 2010, Alur et al. 2002). One outcome
of this development is a high demand for lean
forward functionalities of learning environments to
co-produce and share learning content by the
learners in real world settings.
Petrovic, O.
D-Move: Ten Years Experience with a Learning Environment for Digital Natives.
DOI: 10.5220/0006258103150322
In Proceedings of the 9th International Conference on Computer Supported Education (CSEDU 2017) - Volume 2, pages 315-322
ISBN: 978-989-758-240-0
Copyright © 2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
High Importance of Peer Group
Peer groups with similar interests, age,
background or social status gain importance in
producing, acquiring and assessing learning content.
Traditional authorities like parents, teachers or
politicians are becoming less important as a main
information source. This tends to result in more self-
responsibility of learners for processes and content
of learning and in an altered role of teachers from
embodying the main source of information to being
the moderator and enabler of learning processes by
offering learning environments and connecting
learners (Kuhlmann und Sauter 2008). Digital
Natives are used to applying their own learning
strategies, speed and locations (Handke und Schäfer
2012). The critical point of this trend is the spill-
over to fact-based learning. To learn when Napoleon
Bonaparte lived by asking peer groups can lead to an
inefficient exchange of ignorance.
Increased Use of Digital Media
The use of digital media is very familiar to
Digital Natives, because they grow up with them.
Digital media are as intimate as face-to-face settings
and much more familiar than the traditional media of
their parents’ generation like newspapers or
television (Meyen, 2009). But recent findings show
that in different groups of Digital Natives different
digital media are more common than in other
groups. For instance, Facebook is a main media for
mature Digital Natives while Snapchat is much more
common in younger age groups. The latter is as
unfamiliar for mature Digital Natives as it is for their
parents’ generation. Thus, we have to distinguish the
media usage between different groups of Digital
Natives (Petrovic 2017). As changes in media usage
always lead to social and cultural changes, they also
affect the behavior of learners and as a consequence
learning methods and processes (Hepp 2011).
Those five challenges for a learning environment
to support learning processes of Digital Natives are
still the main requirements for D-Move after more
than ten years of experimental design, development,
usage and evaluation. The main changes during that
time are based on technological innovations,
changes in media usage by the learners in their
everyday life and learnings from applying D-Move.
D-Move is a learning environment for Digital
Natives, composed of different software modules
and learning methods to support Digital Natives to
learn how to solve novel problems. These types of
problems are characterized by Glaserfeld (1995):
“The solving of problems that are not precisely those
presented in the preceding course of instruction
requires conceptual understanding …” Thus, it
should support the building of ‘strong intelligence’,
the big advantage of humans over computers. This is
characterized by own intentions of the system based
on self-reflection, emotions or social values and
norms, the ability to enhance the problem-solving
algorithm during run-time and finally, to modify
their ‘hardware’ autonomously - in the case of a
human being, the synapses of the brain - to better
solve the problem (Luger 2008). The following
aspects are mainly based on constructivism
(Glaserfeld 1995, Foerster 1992) and connectivism
(Siemens 2005) based learning theories. Those
approaches suggest that humans construct
knowledge and meaning from their experiences and
their interaction with others within networks.
Since back in 2005 the basic aim of D-Move
remains the same: To learn how to solve novel
problems in settings that are part of Digital Natives’
everyday life. This includes not only everyday life
experiences but also the methods and media of
communication and information sourcing which are
familiar to a certain group of Digital Natives. Due to
changes in available technologies and styles of
communication among Digital Natives the
implementation of D-Move ranges from text
messaging in the early implementation to Snapchat
Delphi in current implementations. D-Move is based
on evolved learning methods and digital media used
to meet the challenges of increasing amount of
information, the trend from lean back to lean
forward media, the increasing need for authenticity,
the high importance of peer groups and the increased
use of digital media. The author wants to stress that
D-Move is not an attempt to build the ‚best‘
learning environment for Digital Natives. On the
contrary, Digital Natives call for different learning
methods and environments - one of these could be
D-Move (see also Dodero, J.M., et al. 2015).
2.1 Phase 1: The Early Years
The first instance of D-Move was designed back in
2005 by the author of this paper and implemented
together with partners from six countries in a large-
scale international research project supported by the
European Union (Petrovic & Brand 2009). To fulfill
the requirements shown above the learners were
asked to identify and analyze real world
phenomenon that are connected to certain learning
CSEDU 2017 - 9th International Conference on Computer Supported Education
topics of the particular course. For this purpose,
teams of five to six students were built and informed
by text messages about a certain real world situation
near the university. Their task was to go to this
location, analyze the situation by using the methods
learned in class and find similar situations at other
locations. Text messaging was used to inform the
students via an alert and to support communication
between them. To document and comment the real
world situation using text and pictures a basic blog
system was implemented. Due to a limited time span
between sending out the text alert and closing the
blog, during which also a competition in which
contributions of each team was evaluated, a close
connection to real business settings was able to be
The main learnings from those early years,
which still have high relevance for current learning
environments, are threefold (Petrovic 2008, Petrovic
2008a, Petrovic 2008b). Firstly, there was a huge
difference in innovation openness among students.
Just a small share of the students was pleased from
the beginning with the additional method of learning
introduced in particular courses. They were asked at
the beginning of the term who wanted to participate
in the group with D-Move and who preferred the
traditional classroom setting. The majority voted for
the traditional setting. The factors influencing this
decision were increased effort, unfamiliarity,
unwillingness to spend spare time and additional
costs incurred for traveling to the locations and for
the communication via mobile phones. Secondly,
there was a high group dynamic among the students
in changing their willingness to adopt the new
learning environment. After a dedicated statement of
the lecturer about the differences between successful
and less successful students regarding their openness
for innovations, the first learners switched over to
the group using D-Move. After those early adopters
explained their reasons for changing their mind,
more and more students wanted to be part of the D-
Move group. In the end, very few were left over in
the traditional group. Thus, in our experience
traditional surveys exploring acceptance of future
technologies that neglect those dynamic changes of
mind can lead to wrong conclusions. The third main
finding was the resistance to merge private life with
learning settings at the university. This includes
leisure time versus learning time at the university as
well as using a very personal and intimate device
like the own mobile phone for tasks at the
university. Due to current increased tendencies to
merge work time with leisure time, e.g. by checking
e-mails or posting work related topics in social
media at home, this resistance is becoming less
important in current implementations of D-Move.
2.2 Phase 2: Multi-channel Everyday
Life Learning Environment
2.2.1 System Functionalities based on
Learning Theories and Models
The next step in advancing D-Move was to enable
multi-channel, multi-media and multi-device
interaction between learners and also with lecturers.
The driving force was the mobile Internet and social
media, both of which have become ubiquitous in
everyday life of Digital Natives. Because of the
main aim of D-Move to offer a learning environment
that corresponds to learners’ communication in
everyday life and their information sourcing,
especially the technology platform of D-Move has
D-Move can be characterized by learning
theories that form the basis, the learning methods
used and features of the technical platform. D-Move
is mainly based on contructivist-connectivist
learning theories (Brunner 2009, Siemens, G. 2005).
Constructivism suggests that humans construct
knowledge and meaning from their experiences.
New information is linked to prior knowledge; as a
consequence, mental representations of the reality
are subjective. From a connectivist point of view,
learning occurs distributed in different networks, is
socially and technologically enhanced and is based
on recognizing and interpreting patterns within those
different networks. Both approaches are central
starting points for the functional design of D-Move.
In contrast to learning based on case studies, the
learning content is not only as close as possible to
reality but the real world experiences of learners
form the learning content. Additionally, the media
and the kinds of social interaction used in learning
are the same as used by learners in everyday life. To
reduce the time gap between learning certain facts or
methods in classroom teaching and applying them in
real life, D-Move supports both styles of learning
and bridges them.
Regarding the used learning methods D-Move
mainly supports approaches that help gain
experiences and observations in real life. Different
ethnographic methods like participant observation,
field protocols and picture- and video-based
documentation are implemented to observe social
interaction as well as interaction of humans with
technical artifacts in vivo. Also, cooperative learning
is supported to enable learning by interaction with
peer groups (Anderson et al. 1996).
D-Move: Ten Years Experience with a Learning Environment for Digital Natives
The features of the technical platform can be
classified into front end and back end features. One
central design requirement was the support of multi-
devices to collect, assess, enrich and share
observations in the field and to interact with other
learners as well as with lecturers. To support many
different smartphone types with varying screen
sizes, computing power, local storage and network
speed, D-Move can be fully utilized by standard
web browsers preinstalled on common smartphones,
tablets and desktops. The installation of a dedicated
client application is not necessary, which strongly
enlarges the range of supported smartphones as well
as the willingness of the learners to use D-Move on
their own private devices. Additionally, native apps
which run on the client device can be used for
special functionalities to collect, assess and share
observation data in the form of text, pictures and
video. The implemented interfaces allow the learners
to use familiar standard apps such as Facebook,
WhatsApp, Snapchat or Instagram as well as
dedicated apps for ethnographic field work to
interact with the back end of D-Move. This multi-
channel, multi-device, multi-media approach
supports the central requirement of being as close as
possible to the ‚natural‘ communication behavior of
the learners.
The backend features of D-Move are based on
an information repository to store collected data in
the form of text, pictures and videos in combination
with annotations made by the learners and
interaction content with peer groups and lecturers.
The content of the information repository is
delivered from the front end via interfaces to the
data integration module that supports online and
offline data transfer. The latter is useful for
fieldwork with no, poor or too expensive online
connectivity of the front ends used. The data
manipulation module supports basic content editing,
annotations of collected data in the form of text,
pictures, voice and videos as well as classification of
collected observation content by adding metadata
and tags. The sharing and interaction module
enables collaborative learning by using interfaces
with widespread social media to support common
communication patterns of Digital Natives. It also
offers proprietary sharing and interaction features
for situations where a separation of learning oriented
and private communication is preferred. The storage
module offers a space to distribute learning material
among learners and to share documents, pictures and
videos generated by the learners to document their
observations. The access control module allows the
governing of rights to read and modify data from
other learners.
A main paradigm of the development of D-Move
is to implement, parameterize and use standard
software as far as possible instead of developing the
system from scratch. Despite the advantages of time,
costs and quality a central goal is still to offer tools
that Digital Natives are familiar with. On the other
hand, as mentioned above recent findings show that
different groups of Digital Natives call for different
front ends to collect observation data (Petrovic
2017). This approach also allows for the inclusion of
future front ends that are not yet known today but
may possibly be used by millions within some years.
Figure 1: Collecting and annotating observation data in the
front end to transfer it for storing, editing and sharing
using the back end.
2.2.2 Example of Applying and Evaluating
D-Move in Phase 2
The following case shows the use of D-Move in a
Master Course on the diffusion of innovations
(Rogers 2003) and technology acceptance models
like TAM (Davis et al. 1989) and its successors. In
the first phase, the lecturer gave theoretical input in
a traditional course setting to 25 master students.
The second phase included the fieldwork based on
D-Move. Students started by developing concepts to
perform the fieldwork to identify critical acceptance
issues in innovative approaches for last-mile-
logistics like pickup-boxes, click-and-collect or
same-day-delivery (Petrovic et al. 2013). The
students made videos of customers using innovative
technologies for parcel delivery and conducted on
the spot interviews regarding the customer’s
experience immediately after use of the technology.
Afterwards, they analyzed both results in regards to
the underlying acceptance model. The main findings
were annotated graphically and textually directly
inside the recorded video using the information
repository of D-Move. The students’ final step was
to summarize the findings in a report, which was
also uploaded to D-Move. In a presentation attended
by all students they explained their findings in detail
and discussed differences between the observed
delivery technologies as well as their experiences in
CSEDU 2017 - 9th International Conference on Computer Supported Education
applying the different technology acceptance
After finishing the course, an evaluation was
carried out to analyze the impact of D-Move on
learning process and learning outcome compared to
traditional paper-based case study learning. The
evaluation covered five constructs: Activation,
emotion, satisfaction with the learning process,
perceived learning success and satisfaction with D-
Move. Compared to paper-based case study
learning, the environment shows a high degree of
activation. It also shows that the learning
environment evokes stronger positive emotions for
students. In terms of satisfaction with the learning
process, D-Move was viewed as strongly positive.
The students appreciated the freedom to discover
things themselves (μ = 1.7), the use of independent
judgment (μ = 1.7), the possibility to reflect
observations (μ = 1.6), the demand for individual
initiative (μ = 1.4), and the feeling of being actively
involved (μ = 1.6). Also for the items of perceived
learning success positive results were obtained. This
includes the ability to understand relationships (μ =
2.1) and to apply newly acquired knowledge (μ =
2.1). The overall satisfaction with D-Move was also
perceived positively. For a detailed presentation and
discussion of the results see Petrovic 2016.
Table 1: Evaluation results for D-Move applied in a course
on diffusion of innovations and technology acceptance by
comparison to case study teaching.
Construct Item
α* µ**
Activation Energetic activation 0,84 1,7
with the
Free space
Personal judgment
Personal initiative
Practical application
Know How
New knowledge
Media competence
with the
Sharing options
Location independence
Scale: 1.. much better, 5.. much worse, * α Cronbach’s
Alpha, ** µ Mean value
2.2.3 Learnings in Phase 2
Several applications of D-Move during phase 2
leads, among others, to three main learnings. Firstly,
compared with conventional paper-based case study
learning, learning process and outcomes were
perceived by the learners as superior. Thus, D-
Move is an alternative to traditional case study
teaching. As D-Move was always implemented
together with classroom lectures, findings on the
acquisition of fact-based knowledge cannot be
deduced. Secondly, the main part of students’
workload is performed outside the classroom during
the field observations or in form of document
preparations at home. This leads to conflicts with
traditional regulations for courses like grading on
the basis of individual performance, necessary time
spent in the classroom and additional work for
lecturers for the technical setup of the learning
environment and the support of students outside the
classroom. Thirdly, an important advantage of D-
Move is the support of different front ends with the
same back end. This allows the alignment of the
front end to collect, assess and share data to the
latest communication patterns of different groups of
Digital Natives; also to future, to this day still
unknown tools used by millions in some years. At
the same time, the logic to support different learning
methods, strategies, activities and sequences that are
embedded in the back end can remain stable to a
large extent. This allows the carrying out of long-
time studies in regards to the acceptance of the
system and its impact on learning success.
2.3 Phase 3: Expanding D-Move by a
Research Environment
2.3.1 Enhancements
In this phase a research environment to gain insights
into Digital Natives expands D-Move. Following the
contructivist learning theory mentioned above,
learners develop their own views and opinions on
certain aspects of social interaction and use of
technical artifacts while engaging in the real world.
Additionally, following the connectivist learning
theory, they share their views and opinions in social
networks and learn from changes in their patterns.
The learning support of D-Move can be also used for
insights into Digital Natives’ behavior and their
attitude to certain phenomena in the world of digital.
Due to combining research with everyday life, the
validity of results can be higher than in artificial
settings of controlled experiments or surveys based
on recalling instances of the past or ‘try to imagine‘
D-Move: Ten Years Experience with a Learning Environment for Digital Natives
Figure 2: Method used by D-Move Delphi to gain insights into Digital Natives.
questions. Regarding the identification of trends and
future developments Digital Natives are not only test
persons for a survey but also experts in the world of
digital. They will create future innovations and will
form the majority of the population that will use
them in the future.
To exploit Digital Natives’ role as experts
thoroughly the Delphi method is implemented using
D-Move. The main steps of the Delphi method in
empirical research are: Formulating theses about
current or future issues, obtaining experts’ opinions,
aggregating these opinions and presenting them to
the experts and finally, starting a second round to
obtain consent/dissent to these issues (Loë 2016).
The features of the technical platform and the
learning methods used as described above remain
unchanged in this phase. The module D-Move
Delphi is added to support the interaction between
learners as well as with lecturers following the
Delphi process shown in Figure 2. For a detailed
description of D-Move Delphi and its evaluation
results see Petrovic 2017.
2.3.2 Example of D-Move Delphi’s Results
Figure 3 shows a sample result of the first Delphi
round regarding the future role of self-monitoring
for health and fitness from Digital Natives’
perspective. This chart, using means of info
graphics, shows how familiar the participants are
with this topic of the Delphi together with data on
the demographic build-up of the 134 experts
participating the Delphi. Based on experts’ opinion
on Delphi thesis gained in the first round, in a
subsequent face-to-face meeting topics are identified
with strong dissent/consent and are discussed in
depth. After summarizing the results, a 2
starts as shown in Figure 2. Charts as indicated in
Figure 4 assist the participants in identifying thesis
with high dissent to be discussed in a face-to-face
meeting. They are also used to prove if consent
finding was successful after round 2.
Figure 3: Sample result of D-Move Delphi in round 1.
CSEDU 2017 - 9th International Conference on Computer Supported Education
Figure 4: Topics with high degree of dissent/consent to be
discussed in a face-to-face meeting.
2.3.3 Learnings in Phase 3
Although phase 3 and its Delphi module has just
been implemented and used tentatively, early
learnings can be deduced. Firstly, to understand
Digital Natives’ behavior and views using the
Delphi method together with D-Move’s technical
features is remarkably useful for gaining insights
into current and future phenomena in the world of
digital. Secondly, on the contrary to other university-
based empirical research settings like experimental
research or traditional surveys, recruiting students is
not a disadvantage in regards to representativeness
but because of their role as experts poses even an
advantage. Additionally, as D-Move is run every
year with different Digital Natives, also long-term
studies showing changes over time are possible.
Thirdly, a methodological challenge has to be
solved. As D-Move Delphi is being enhanced
constantly, its functions as a research instrument
also change over time. Thus, special attention has to
be turned to risk of inferences between change in the
instrument and conclusions from its results.
After more than ten years of designing, developing,
using and evaluating D-Move there are a lot of
findings - please see the list of references. In a
nutshell, the main finding is the high usefulness to
obtain learning aims and simultaneously a high
acceptance by learners. This can be attributed to the
alignment of learning theories and learning methods
used together with implemented technical features,
the everyday life communication behavior and
media usage patterns of Digital Natives. This
requires continuous development, especially
regarding implemented front ends due to the very
high innovation dynamics of Digital Natives.
Secondly, D-Move Delphi as the latest module
implemented is very promising. The enrichment of a
learning environment with research features,
especially using the Delphi method with Digital
Natives as experts, allows for new research insights
as well as long-term studies. A third learning is that
D-Move should not be used as a substitute but as a
supplement to classroom teaching. However, the
findings show also that the additional use should not
be based on traditional learning methods with their
associated learning theories - like broadcasting the
lecture via a video stream. We learned also that the
use should not occur on a side-by-side basis
according to learn-and-apply-logic. Rather a tight
integration between classroom and everyday life
learning is necessary, switching from one to the
other mode often. As D-Move is based on
constructivist-connectivist learning theories and not
on stimulus-response-models of learning, there are
limitations regarding representatives and validity as
broadly discussed in the mentioned literature and in
methodological work on qualitative research in
There are two main future development streams
of D-Move. Firstly, the continuous enhancement of
learning methods based on constructivist-
connectivist learning theories together with technical
features to support them. The main aim is to enhance
the seamless integration in Digital Natives’
everyday life using front ends that are most common
in a certain target group. Those future activities are
based on the research paradigm of design science
with its close loops between development of
technical artifacts and their evaluation (Hevner
2004). A strong emphasis will be put on the
enhancement of D-Move Delphi to gain insights into
Digital Natives’ opinions and attitudes. One vision is
to open D-Move like an omnibus survey for market
research regarding current trends and future
innovations among Digital Natives. Also, long-run
studies on changes in Digital Natives’
communication and learning patterns, on an inter-
cultural scale too, will be conducted.
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CSEDU 2017 - 9th International Conference on Computer Supported Education