Layered Knowledge Networking in Professional Learning
Environments
Mohamed Amine Chatti, Hendrik Thüs, Christoph Greven and Ulrik Schroeder
Informatik 9 (Learning Technologies) RWTH Aachen University, Aachen, Germany
Keywords: Knowledge Management, Technology Enhanced Learning, Lifelong Learning, Professional Learning,
Work-Integrated Learning, Personalized Learning, Network Learning.
Abstract: Knowledge Management (KM) and Technology Enhanced Learning (TEL) became a very important issue
in modern organizational professional learning and work process integration. Former learning and KM
theories which characterize knowledge as a thing or process no longer fit today's digital world where the
amount of required information is no more manageable and the half-time of knowledge in general is rapidly
decreasing. Younger approaches such as the Learning as a Network (LaaN) theory describe knowledge as
complex and emergent and put a heavier focus on knowledge networking. The LaaN theory further stresses
the convergence of the learning and work processes in professional learning settings and views KM and
TEL as two sides of the same coin. Driven by the LaaN theory, the Professional Reflective Mobile Personal
Learning Environments (PRiME) project describes an integrated KM and TEL framework which connects
learning and work processes. It enables the professional learner to harness implicit knowledge and offers
knowledge networking at three different layers: the Personal Learning Environment (PLE), the Personal
Knowledge Network (PKN) and the Network of Practice (NoP). Continuous knowledge networking results
in constant evolution of knowledge leading to personal as well as organizational learning.
1 INTRODUCTION
Since its introduction in the early 1990s, Knowledge
Management (KM) has always played an important
role to increase the productivity of knowledge
workers and achieve organizational benefits. Mainly
following two major approaches regarding
knowledge-as-a-thing on the one hand or
knowledge-as-a-process on the other hand, KM
could not fulfill the high hopes laid in it. Also, the
finding that knowledge is something personal in
nature could not help out when Personal Knowledge
Management (PKM) came up in the past couple of
years (Chatti, 2012).
Similarly, over the last decade, Technology-
Enhanced Learning (TEL) has been addressed as a
possibility to go new ways in education, but as with
KM the view of learning as a passive, teacher-driven
process where knowledge is viewed as an object that
can be transferred from the mind of the teacher to
the mind of the students precluded a real innovative
success. That did not change with the emergence of
the Web 2.0 movement which brought up various
tools to connect learners and put them in an active
role. The traditional pedagogical principles were,
however, kept untouched.
In a professional context, despite the recognition
of the strong links between KM and TEL, the two
fields are still evolving down separate paths. In this
paper, we recapitulate the shortfalls of KM and TEL
and present the Learning as a Network (LaaN)
theory as a new vision of learning defined by the
convergence of KM and TEL concepts into one
solution. Furthermore, we present a possible
application of the LaaN theory in the frame of the
Professional Reflective Personal Mobile Learning
Environments (PRiME) Project. PRiME focuses on
the convergence of the learning and working
processes and proposes an integrated KM and TEL
framework that offers layered knowledge
networking to foster continuous individual and
organizational learning
The remainder of this paper is structured as
follows. Section 2 addresses the relationship
between professional learning and knowledge
management. In Section 3, we briefly discuss the
LaaN theory as a theoretical basis for our work.
Section 4 presents the conceptual and
implementation details of the PRiME project.
363
Chatti M., Thüs H., Greven C. and Schroeder U..
Layered Knowledge Networking in Professional Learning Environments.
DOI: 10.5220/0005491803630371
In Proceedings of the 7th International Conference on Computer Supported Education (CSEDU-2015), pages 363-371
ISBN: 978-989-758-108-3
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
Finally, Section 5 gives a summary of the main
results of the paper and outlines perspectives for
future work.
2 KM AND TEL
In a company context, Knowledge Management
(KM) and Technology-Enhanced Learning (TEL)
have so far been regarded as two impartial areas.
While KM concentrates on knowledge creation and
distribution, TEL focuses on formal learning and
training of the employees. This tightened perspective
can still be read from today’s companies’ structures.
KM and TEL are commonly related to two different
departments, namely IT and human resources.
2.1 KM
With the emergence of KM in the 1990s,
organizations had highest hopes in it to improve the
knowledge worker performance and at the same time
increase the efficiency of the organization to achieve
strategic advantages. In the KM literature, there have
been two major views on knowledge, namely
knowledge-as-a-thing and knowledge-as-a-process
(Chatti, 2012; Chatti et al., 2012).
The idea of knowledge-as-a-thing assumes KM
to be most likely simple information management
(Hildreth and Kimble, 2002; Kimble et al., 2001;
Malhotra, 2005; Wilson, 2002). In general, it covers
information capturing, storing, and reusing.
Capturing knowledge, however, is not an easy task
and moreover very time and effort consuming. The
management of knowledge also conflicts with the
work process and describes an additional overload.
Furthermore, the knowledge-as-a-thing KM models
cannot deal with the complex nature of knowledge
including e.g. knowledge evolution or its context-
sensitivity.
The more recent KM initiatives stress the
importance of the people’s side of KM and view
knowledge as a process. These initiatives often
address the duality of knowledge and move the
focus to the distinction and conversion between tacit
and explicit knowledge. A popular representative of
the class of knowledge-as-a-process KM models is
Nonaka and Takeuchi’s SECI model, which
describes knowledge as a spiraling process of
socialization, externalization, combination, and
internalization which are transforming knowledge
between tacit and explicit forms (Nonaka and
Takeuchi, 1995). Due to the variable iterations of the
four steps, the model creates the impression to be
flexible. However, it is as predetermined as all the
knowledge-as-a-process KM models trying to
describe an automated process for knowledge
creation not able to deal with complexity of
knowledge and the unpredictable nature of the KM
process.
In response, in recent years, the importance of
personal knowledge has been highlighted in various
works and the interest in the topic of personal
knowledge management (PKM) has steadily
increased (Gorman and Pauleen, 2011; Jarche, 2010;
Prusak and Cranefield, 2011; Snowden et al., 2011).
PKM recognizes that knowledge as well as learning
is personal in nature. It puts the knowledge worker
and her tacit knowledge at the center. In contrast to
the early KM approaches, the PKM approach shows
a bottom-up instead of top-down flow of knowledge.
However, the current PKM approaches are still very
process-oriented and do not really deal with the
relation between personal and organizational KM.
So far, there are no underlying, supporting
theoretical frameworks for PKM and problems like
rapidly changing knowledge with a very short half-
life, the complexity of work and its environments,
etc. are not considered (Chatti, 2012).
2.2 TEL
TEL actually shares the same fate with KM.
Summarizing different available approaches, TEL
commonly means offering Virtual Learning
Environments (VLE). These include Learning
Management Systems, Learning Content
Management Systems, Content Management
Systems, and Course Management Systems. All of
them concentrate on the provision of information.
Although efforts have been made in regard to
interoperability of such information repositories,
they are still centralized and commonly under the
control of a formal educational institution (Downes,
2005).
In the last years, TEL has been influenced by the
emergence of the Web 2.0 movement. The term TEL
2.0 emerged to refer to TEL approaches that adapt
new techniques for collaboration, networking, and
learners’ active participation in the learning process.
While that offers great possibilities, TEL did not
really change or influence the traditional
pedagogical principles behind it. Content is still
organized in standard ways, following the top-down
approach pushing information to the learners.
Gained knowledge is time-limited e.g. semester-
bound and not seen as continuous or fluid. By this
linear and predefined process, newly gained
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knowledge cannot be reused and gets lost (Brown
and Adler, 2008; Mott and Wiley, 2009).
2.3 Convergence of KM and TEL
Over the past years, companies and researchers are
starting to recognize relationships and intersections
between the KM and TEL fields and to explore the
potential and benefits of their integration (Grace and
Butler, 2005; Lytras et al., 2005; Malhotra, 2005).
Chatti et al. (2012) go a step further and point out
that professional learning and knowledge
management can be viewed as two sides of the same
coin and stress the need for the seamless integration
of the two concepts into one solution for the purpose
of increasing individual and organizational
performance. The authors introduce the Learning as
a Network (LaaN) theory as a bridge between TEL
and KM. In the next section, we briefly discuss the
LaaN theory as a theoretical basis for our work.
3 THE LAAN THEORY
The Learning as a Network (LaaN) theory has been
proposed by Chatti (2010a, 2010b) as a new vision
of learning towards a new model of personalized and
networked learning. LaaN provides the theoretical
foundation to address the diverse learning needs of
individual learners in today’s learning environments
characterized by increasing complexity and fast-
paced change. LaaN draws together some of the
concepts behind connectivism (Siemens, 2005),
complexity theory (Holland, 1992, 1998; Snowden,
2002), and double-loop learning (Argyris & Schön,
1978, 1996). An abstract view of LaaN is depicted in
Figure 1.
Figure 1: The LaaN Theory (Chatti, 2010a).
Within LaaN, connectivism, complexity theory, and
double-loop learning converge around a learner-
centric environment. LaaN starts from the learner
and views learning as the continuous creation of a
Personal Knowledge Network (PKN). A PKN
shapes the knowledge home and the identity of the
individual learner. For each learner, a PKN is a
unique adaptive repertoire of:
• Tacit and explicit knowledge nodes (i.e.,
people and information) (external level).
• One’s theories-in-use. This includes norms for
individual performance, strategies for
achieving values, and assumptions that bind
strategies and values together
(conceptual/internal level).
In LaaN, the result of learning is a restructuring
of one’s PKN, that is, an extension of one’s external
network with new knowledge nodes (external level)
and a reframing of one’s theories-in-use
(conceptual/internal level).
LaaN-based learning implies that a learner needs
to be a good knowledge networker as well as a good
double-loop learner. The ability to create an own
representation of knowledge, reflect, (self-) criticize
and finally change and correct it is as important as
the capability to recognize patterns or find,
aggregate, and remix available knowledge nodes.
At the heart of LaaN lie knowledge ecologies. A
knowledge ecology is based on the concept of
PKNs, loosely joined, and can be defined as a
complex, knowledge intensive landscape that
emerges from the bottom-up connection of PKNs.
Knowledge ecologies house self-directed learning
that occurs in a bottom-up and emergent manner,
rather than learning that functions within a
structured context of an overarching framework,
shaped by command and control. As compared to
popular social forms that have been introduced in
the CSCL and CSCW literature such as communities
of practice, knots, coalitions, and intensional
networks, knowledge ecologies are more open, more
flexible, less predictable, and less controlled (Chatti
et al., 2012).
LaaN further represents a vision of professional
learning, where the line between KM and TEL
disappears. Unlike traditional KM and TEL
perspectives, LaaN views knowledge as a personal
network rather than as a thing or process. In LaaN,
work/learning is viewed from a professional learner
perspective, and KM and TEL are seen as being
primarily concerned with a continuous creation of a
PKN. This ensures that the differences between KM
and TEL are converging around a learner-centric
work/learning environment and manage that the
roles of KM and TEL are blurring into one, namely
supporting professional learners in continuously
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creating and optimizing their PKNs. In this sense,
KM and TEL are not the two ends of a continuum
but the two sides of the same coin. Moreover, LaaN
enables the seamless integration of learning and
work. The view of learning as the continuous
creation of a PKN makes learning and work so
intertwined that learning becomes work and work
becomes learning. As illustrated in Figure 2,
professional learning in LaaN is no longer regarded
as an external online training activity separate from
the work flow, but rather as a learner-controlled
evolving activity embedded directly into work
processes (Chatti et al., 2012).
Figure 2: LaaN: Convergence of KM and TEL (Chatti et
al., 2012).
In the next section, we present the details of the
PRiME project as a possible application of the LaaN
theory.
4 PRIME
The joint research project Professional Reflective
Mobile Personal Learning Environments (PRiME) is
conducted by the Learning Technologies Research
Group of the RWTH Aachen University and DB
Training, Learning & Consulting of the Deutsche
Bahn AG. It is funded by the German Federal
Ministry of Education and Research with a runtime
of three years, finishing in June 2016 (Greven et al.,
2014).
PRiME illustrates the LaaN theory in action. It
offers an integrated professional learning and
knowledge management framework for personal as
well as organizational learning, addressing the
following objectives:
• Provide an innovative professional learning
approach, where informal and network learning
converge around a self-directed learning
environment.
• Design a work-integrated framework that links
mobile job activities and self-directed learning
in context.
• Develop and evaluate mobile learning
applications to support mobile learning in
context.
• Support continuous knowledge networking and
reflection at three levels: (a) the personal
learning environment (PLE) level where
professional learners can annotate learning
materials on their mobile tablet devices; (b)
these materials can be shared, commented, and
rated by peers at the personal knowledge
network (PKN) level; (c) the newly generated
learning materials can then be shared and used
within the company at the network of practice
(NoP) level.
• Develop and evaluate learning analytics tools
and methods (e.g. dashboards,
recommendation, intelligent feedback, context-
based search) to support reflective learning at
the workplace.
In the next sections, we discuss the underlying
concepts and the current implementation results of
the PRiME project.
4.1 Conceptual Approach
The main goal of PRiME is to offer seamless
learning across time, location, and social contexts
combining the work and learning processes into one.
Context has been identified as a key factor in
workplace learning to achieve effective learning
activities. Nowadays, professional learners perform
in highly complex knowledge environments. They
have to deal with a wide range of activities they
have to manage every day. Moreover, they have to
combine learning activities and their private and
professional daily life. The challenge here is thus
how to support learning activities across different
contexts (Greven et al., 2014, Thüs et al., 2012).
PRiME translates the principles of LaaN into
actual practice. In PRiME, a professional learner is a
lifelong learner who is continuously creating and
optimizing her network. Driven by LaaN principles,
PRiME aims at helping professional learners to
continuously build their personal networks in an
effective and efficient way, by providing a freeform
and emergent environment conducive to networking,
inquiry, and trial-and-error; that is an open
environment in which learners can make
connections, see patterns, reflect, (self)-criticize,
detect and correct errors, inquire, test, challenge and
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eventually change their knowledge; thus changing
the organizational knowledge.
The learning process in PRiME is a spiral and
cyclic conversion of individual and organizational
knowledge at three different layers of knowledge
networking and maturity: the personal learning
environment (PLE), the personal knowledge
network (PKN), and the network of practice (NoP),
as depicted in Figure 3.
Figure 3: Continuous Knowledge Networking in PRiME.
4.2 Implementation
PRiME provides an integrated learning and work
platform through a set of Web and mobile
applications to support continuous knowledge
networking at the three different layers. The
platform can be used in any organizational setting.
Mobile professional learners represent the primary
target group of PRiME. As a proof of concept, we
addressed in this work service technicians at
Deutsche Bahn as a possible target group. These
include car inspectors, specialist authors, training
developers, and trainers working in the field of car
inspection service. The car inspector is a mechanic
that performs rail-worthiness checks on trains. He
repairs small-scale damages on trains and decides
about trains’ dispositions on extensive problems.
The specialist author is responsible for the creation
of new learning resources. The training developers
are responsible for the selection, aggregation, and
creation of trainings from existing learning materials
created by specialist authors. The trainers are
responsible for the organization and execution of the
professional technical trainings and workshops with
car inspectors. They use the learning materials
prepared by training developers.
The document base in the PRiME platform
consists of existing company documents, such as
guidelines and instruction rules. The majority of
such documents are already available in a digital
form, e.g. as Word, PowerPoint or PDF. Through a
Web-based application, called the Bundler (see
Figure 1), these documents are imported into the
PRiME system, processed according to their
hierarchical logical structure, and stored in a tree-
like structure. Specialist authors can use the Bundler
to upload their documents, which can be
automatically processed to fit the PRiME data
schema which consists of so-called Snippets and
Bundles. Snippets represent atomic learning units
that can take the form of text, table, image, audio, or
video. Bundles are used to structure such snippets.
Each bundle can hold several snippets and also
several sub-bundles. The bundles can be seen as the
structure of sections in a book, whereas the snippets
are the content of a book. Whenever possible,
additional information, such as the name of the
author, keywords, or other metadata, is also
extracted from the uploaded document. After this
initial step, the document is presented in its tree-like
structure of bundles and snippets to the specialist
author who can manually alter the automatically
generated result by splitting and merging single
elements. When the author is satisfied with the
result, the initial version of this document is released
to the PRiME system as a set of bundles and
snippets which can be used by car inspectors as
learning resources and reused by training developers
and trainers as building blocks for training and
workshop materials.
The Bundler can further be used by training
developers to mash up and create own bundles from
existing bundles and snippets in the PRiME system
according to their needs (see Figure 4). Thereby, the
training developers can search for already existing
snippets. Different filters and search criteria help to
limit the search results to only show context-relevant
snippets. In the left column of the screenshot, a tree-
like view helps to easily structure and arrange
snippets at various levels of a new bundle with
simple drag and drop actions. The right column
shows a document-like view of the aggregated
bundle. The new bundle is then published and can be
used by trainers in their workshops and subscribed
to by the car inspectors who are interested in it. The
Bundler further offers different export modules that
allow the trainer to convert bundles to traditional
formats, such as pdf, word, PowerPoint that can be
used as handouts in the workshop.
In addition to converting an existing document to
snippets, and mixing up existing snippets to new
bundles, the specialist author may also use the
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367
Bundler application to create snippets and bundles
from scratch.
Figure 4: Bundler: Existing documents are imported and
processed in the PRiME system.
In PRiME, learning is a continuous process
which involves the learners, their personal networks,
and the organization itself. PRiME divides the
learning and working process into three layers,
namely the Personal Learning Environment (PLE),
the Personal Knowledge Network (PKN), and the
Network of Practice (NoP). In the following
sections, we discuss in detail the work and learning
activities in relation to each layer and how these
activities are supported by the PRiME tool set.
4.2.1 Personal Learning Environment (PLE)
A Personal Learning Environment (PLE) enables
professional learners to compile their own individual
knowledge assets which are relevant for their
everyday working context. Each learner decides on
her own, which information is important for solving
daily tasks and for improving one’s knowledge. The
task of a mobile PLE is to support the learners in
their everyday life, either for solving current tasks
and problems or for learning in context. Knowledge
assets in the PRiME system include bundles,
snippets, and annotations. An annotation is
multimedia information created by the learner,
which can either be attached to bundles and snippets
or detached from those structures.
In a PLE, a learner should not only be able to
define which information is available but also how
the learning environment should look like. One
possibility to achieve this is to implement one
monolithic application with all the required
functionalities and various options for the learner to
adjust everything. In the PRiME system, we opted
for a set of applications where each application is
responsible for a single task. All PRiME
applications are able to communicate with each
other and to share functionalities. By selecting the
own set of applications, the learner also decides how
the own learning environment looks like. The
starting point for all the PRiME applications on the
learner’s mobile device is the Dashboard (see
Figure 5). It encapsulates all the functionalities by
displaying each PRiME application and by
providing a centralized communication system for
all the installed applications in the PRiME
ecosystem.
Figure 5: The Dashboard is the main entry point to the
PRiME ecosystem.
With the help of the application BundleReader
(see Figure 6), a car inspector is able to subscribe to,
create, and display her own set of bundles and
snippets that are required for her work. Alongside,
she can also create her own multimedia annotation
for each bundle or snippet available in the
application. For displaying additional information
for one bundle or snippet, the user has to swipe a bar
from the right border to the center. This newly
opened area contains all the metadata about the
currently highlighted bundle or snippet, namely the
author, the version, the date of last change, as well
as public annotations and comments. Annotations
are questions and corrections to a bundle or snippet
that offer the possibility to capture knowledge
during the work process. Instead of taking a note on
a loose sheet of paper which normally gets lost, car
inspectors can take a photo of a machine or record a
short video of a procedure. In general, annotations
cover various types of multimedia (e.g. text, image,
audio, video, or drawing) which can be created very
easy and they provide a great expressiveness at the
same time. To create a new personal annotation, a
user can just tap the toolbox in the upper right corner
and select the type of annotation to be added. The
newly created annotation will appear on the right
side of the application, associated with the bundle or
snippet it has been created for. Annotations are
context-sensitive and can be extended automatically
with meta-information, such as recording time or
location. At first, they are strictly personal and not
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visible to any other user. Thus, they can hold some
personal work instructions that are helpful for future
tasks. Furthermore, the car inspector can use the
intelligent search functionality provided by the
BundleReader to discover context-relevant bundles.
For example, some knowledge is location-based due
to machinery, or physical conditions such as noise
might result in exclusion of media types containing
audio. The search result can further be filtered
according to author, topic, keywords, time, location,
etc.
Figure 6: The BundleReader enables learners to subscribe
to, display and annotate bundles and snippets.
Car inspectors at work often do not have the time
to write extensive annotations and link them to a
specific bundle or snippet. The application Notepad
was developed to support car inspectors in taking
quick notes in form of text, picture, video, audio, or
drawing which can be used for self-reflection after
work or as basis for annotations on bundles and
snippets in the BundleReader. For example, while
reading a snippet, the car inspector might remember
that she took a picture which can clarify the
instructions given in the snippet and use the picture
as annotation for that snippet.
Figure 7 shows the Notepad application. A new
note can easily be created by tapping the red button
in the lower right side of the application. A new
menu appears where the car inspector can select
which kind of note she wants to create. In addition to
the note itself, keywords, short comments and other
meta information, such as time and location can be
added optionally. This simplifies the process of
finding the correct note when needed. Selection
from storage like SD card is possible as well as
using the device-internal tools to record multimedia
like the camera application. A personal media
gallery collects all the created notes. The car
inspector can define when to synchronize them with
a server-side personal repository.
Figure 7: The Notepad enables learners to quickly take
notes in their PLE.
4.2.2 Personal Knowledge Network (PKN)
The Personal Knowledge Network (PKN) layer
fosters continuous networking and collaborative
knowledge creation. It enables professional learners
to share tips and tricks and collaboratively work on
the constant improvement of the available
knowledge assets.
As mentioned in the previous section, at the PLE
layer, a car inspector can use the BundleReader to
make annotations on the bundles and snippets she
has subscribed to and keep them private per default.
If she decides that her personal annotations are
worth sharing, she can publish them to all other
subscribers or share them with selected peers or
groups that can be personally defined. Annotations
can then be seen by all recipients who can give
ratings and might reply to these annotations with
their own ones. This way, expert discussions can
emerge resulting in collaborative creation and
maturing of knowledge. Car inspectors who apply
the knowledge in their daily tasks have thus the
possibility to give valuable feedback to aid the
specialist authors in improving the produced bundles
and snippets.
As the available knowledge is rapidly growing
and updates in the system are hard to track, PRiME
users should have an easy way to stay up to date
without being overwhelmed with the constant flow
of information. This is achieved through the native
mobile application Newsstream which provides an
aggregated view of recent activities in the PRiME
system, as shown in Figure 8. Car inspectors,
specialist authors, training developers, and trainers
who subscribed to a specific bundle continuously
receive notifications on the annotations, ratings, and
changes made to the bundle. By clicking on a
notification, they are directly forwarded to the
respective bundle, snippet, or annotation in the
BundleReader. Car inspectors can follow the
discussion and rating activities on the bundle or
LayeredKnowledgeNetworkinginProfessionalLearningEnvironments
369
snippet they are interested in and discover quality
annotations contributed by peers. They can also set
filters so that e.g. only notifications related to a
specific snippet or given by a specific peer are
displayed. Furthermore, they can use the
Newsstream to receive recommendations according
to their preferences and activities in the system. On
the other hand, specialist authors, training
developers, and trainers can get continuous feedback
that can be used in the enhancement of their snippets
and bundles.
Figure 8: The Newsreader provides an aggregated view of
recent activities.
4.2.3 Network of Practice (NoP)
The Network of Practice (NoP) represents the
organization layer in PRiME. It supports the
propagation of the knowledge created at the PLE
and PKN layers to the entire organization. The NoP
layer harnesses the collective intelligence to ensure
that the organizational knowledge is accurate and up
to date. An organization represents a knowledge
ecology. Organizational learning occurs when
individuals within an organization experience a
problem and work on solving this problem. This
happens through a continuous process of
organizational inquiry, where everyone in the
organizational environment can inquire, test,
compare and adjust her knowledge, which is a
private image of the organizational knowledge.
Effective organizational inquiry then leads to an
update of one’s knowledge, thereby updating the
organizational knowledge.
The knowledge which is available in PRiME can
be continuously improved with every action in the
system. At the PKN layer, the collective intelligence
decides which knowledge is of high quality through
commenting and rating. Quality knowledge that
emerges as a result of the continuous interaction
between PRiME users at the PKN layer builds the
cornerstone for the enhancement of the organization-
wide knowledge assets. When a new annotation to a
snippet at the PKN layer is highly rated, the
specialist author is notified and can use this
annotation for the enhancement of the snippet.
Training developers and trainers can use the
improved snippet in their trainings and workshops.
Car inspectors who subscribed to this snippet will
automatically get notified about this update. The
whole process starts then anew at the PLE layer.
This continuous knowledge networking process
ensures an effective individual and organizational
learning.
5 CONCLUSIONS AND FUTURE
WORK
Technology Enhanced Learning (TEL) in
professional and organizational settings is
increasingly gaining importance. In this paper, we
addressed the challenge of convergence of
professional learning and knowledge management
(KM). Driven by the learning as a network (LaaN)
theory, which presents a new perspective
characterized by the convergence of learning and
work within a learner-centric knowledge
environment, we discussed the conceptual and
implementation details of the Professional Reflective
Mobile Personal Learning Environments (PRiME)
project. PRiME fosters knowledge in action and
provides a new vision of learning at the workplace
defined by the seamless integration of TEL and KM
concepts into one solution toward a new model of
professional learning in context. Learning in PRiME
is the result of continuous knowledge networking at
three layers, namely personal learning environment
(PLE), personal knowledge network (PKN), and
network of practice (NoP). Different mobile
applications have been introduced to support the
various activities related to each of these layers.
We had a series of workshops with potential
PRiME users at Deutsche Bahn in which we
collected requirements and discussed early
prototypes of the different applications. We plan to
perform an empirical study of our approach, which
will allow us to thoroughly evaluate the usability of
the developed applications as well as the
effectiveness of our method to support work-
integrated networked learning. Besides extending
the PRiME application ecosystem, future work will
also include the implementation of personal
dashboards to support self-reflection and awareness,
as well as different learning analytics methods that
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leverages the context information to provide
effective recommendation and intelligent feedback
to PRiME users.
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