Process, Challenges and Requirements
Mohamed Amine Chatti
, Ralf Klamma
, Matthias Jarke
, Vana Kamtsiou
, Dimitra Pappa
Milos Kravcik
, Ambjörn Naeve
RWTH Aachen University,Informatik V, Ahornstr. 55, 52056 Aachen, Germany
National Center of Scientific Research "Demokritos", 153 10 - Agia Paraskevi Athens P.O. 60228
Fraunhofer-Institut für Angewandte Informationstechnik FIT, 53754 Sankt Augustin, Germany
Royal Institute of Technology, 10044 Stockholm, Sweden
Keywords: Technology Enhanced Professional Learning, Learning Process, Learning and Knowledge Management.
Abstract: Since we cannot transfer knowledge from one person to another person, learning, also known as knowledge
creation, is the social process of acquiring and applying knowledge. Our claim is that the oscillating process
of knowledge acquisition and application for workplace learning can be best described by the SECI model
introduced by Nonaka in 1994. In this paper, we analysis the learning process in terms of the SECI model,
identify the challenges for technology enhanced professional learning and define the requirements for future
applications such as personalized adaptive learning. We report the results of a roadmap survey done in the
framework of PROLEARN to disclose the desired state of the art in technology enhanced professional
learning in the year 2015 and show ways how to proceed on the way to the desired state.
The knowledge age is demanding higher skilled
jobs, based on critical thinking, creativity,
collaboration, and interpretation abilities.
Additionally, the percentage of “knowledge
workers” is rapidly increasing and 50% of all
employee skills become outdated in three to five
years (Moe, Blodgett, 2000). Therefore, using only
traditional methods of training cannot cover today’s
educational needs. Many authors have recognized
the new demands on one hand and new potential on
the other. In the following we mention some of
them. Drucker sees new horizons. He cites that
education requires focusing on the strengths and
talents of learners (Drucker, 1989). Bork argues that
we need much better learning for all and this
learning has to be affordable for the individual and
the world (Bork, 2001). Hodgins presents the grand
vision of meLearning that will provide personalized
learning experiences to every person on the planet
every day and when the learner is ready the
“teacher” will appear (Hodgins, 2005). In the past
few years, attention has been shifting towards the
importance of knowledge management in corporate
and academic learning environments. Researchers
and companies are starting to recognize relationships
between knowledge management and technology
enhanced learning research fields and to explore the
potential of their combination. Not surprisingly,
there are several commonalities between learning
management (LM) and knowledge management
(KM) (Grace, Butler, 2005). Both share a similar
purpose: how to enhance human knowledge and its
application. In this paper we go a step further and we
argue that LM and KM solutions have to fuse, and
that we should speak about merging and fusion of
the two fields rather than intersection or
complementary relationship between them. In this
work, we address the following important questions:
Why are LM and KM two sides of the same coin?
What does the learning process look like? What are
the requirements to make the learning process work
better? What is the future potential of learning at the
workplace? The rest of the paper is structured as
follows: Section 2 explores the integration of LM
and KM. Section 3 defines learning concepts and
Amine Chatti M., Klamma R., Jarke M., Kamtsiou V., Pappa D., Kravcik M. and Naeve A. (2006).
TECHOLOGY ENHANCED PROFESSIONAL LEARNING - Process, Challenges and Requirements.
In Proceedings of WEBIST 2006 - Second International Conference on Web Information Systems and Technologies - Society, e-Business and
e-Government / e-Learning, pages 268-274
DOI: 10.5220/0001251802680274
points to the relationship between learning and
knowledge. Section 4 focuses on the elements of the
learning process. Section 5 explores the challenges,
and requirements of learning at workplace. Finally,
Section 6 gives a summary of the paper and outlines
perspectives for the future.
Relationships between KM and LM on the one hand
and between KM and computer science on the other
hand has been discussed by many researchers (Jarke,
Klamma, 2002). In this section, we argue that LM
and KM are two sides of the same coin and terms
from the two fields can be used interchangeably.
Naeve defines knowledge as “efficient fantasies”,
with a context, a purpose and a target group, with
respect to all of which their efficiency should be
evaluated (Naeve, 2005). Knowledge can be of
different types, such as know what, know how, know
why, and know who. Know what refers to knowledge
about facts, concepts, categories, descriptors and
information. Know how refers to knowledge of how
something occurs or is performed. Know why refers
to knowledge why something occurs. Know who
refers to knowledge about persons who are in
possession of important and valuable knowledge.
The same might be said of learning. Learning
comprises learn what, learn how, learn why, and
learn who. These learning types will be discussed in
more details in the next section.
KM is not easy to precisely define. Capturing and
managing knowledge, placing people at the center,
creating a culture where knowledge sharing is the
norm, and providing technological capabilities are
the main aspects of KM. These are also the primary
goals of LM which deals with connecting people to
quality learning resources as well as people to
people. Technology enhanced learning platforms
and formal training programs are becoming essential
parts of organizational KM. On the contrary, KM
methods and techniques are being adopted in
learning environments. These methods include
fostering of communities of practice (Wenger, 1998)
and knowledge sharing within learning
environments as well as using repositories to store
learning components. Tools such as live chat rooms,
instant messengers, video conferencing, and
knowledge repositories represent some of the
techniques from the KM field that are being applied
in the learning process. Let us start from the
definition that KM is the collection of the following
processes: create, transform, organize, disseminate,
share, and use knowledge. Take a learning resource.
Decompose it into granular and reusable learning
assets. Support communities where social
interactions can take place. Use technology that on
the one hand helps delivering the right learning
content to the right person and on the other hand
allows posting new useful learning content. What
will be the result? Something that is quite similar to
Learning can be viewed both as knowledge or skill
and as an applied process. Wayne Hodgins defines
learning as a knowledge and social skill that has to
be learned and continuously improved. It is one of
the new basic skills of the future (Hodgins, 2000).
This crucial skill comprises learn what, learn why,
learn how, learn where, and learn who. Learn what
refers to the learning stuff needed and the high-
quality learning object that has to be acquired. Learn
why refers to the definition of effective learning
goals. The main aim of learning is to improve
human performance and increase the ability of any
individual, project team, or organization. Acquiring
new knowledge is itself not the purpose of learning.
We learn in order to better perform, integrate the
gained knowledge in our daily work to solve
problems and achieve the desired end result, create
innovative knowledge and better ideas that lead to
more success, and share our own knowledge with
others. In that sense, a learner becomes a knowledge
worker. That is, someone who doesn’t just consume
knowledge but who is able to create it. Learn how
refers to how learning occurs. It includes how to
acquire new knowledge (e.g. through reading,
professional training, discussions with peers, formal
studies or research), how to apply knowledge
effectively, how to generate, design, plan, structure,
capture, store, evaluate, manage, use, disseminate,
deliver learning assets, how to build a learning
environment that encourages knowledge sharing,
and how to use technology such as collaborative
tools. Since learning nowadays is conceptualized as
a social system within communities of practice
(Wenger, 1998), the best way to learn is with others,
in groups. Learn how also involves the knowledge
how other people learn which is critical to ensure the
creation of engaging learning experiences (Stacey,
2003). Learn where refers to how to locate
appropriate information and where to look for
quality learning objects. As Albert Einstein once
said "don’t memorize anything you can look up”, it
is worthwhile to learn where to find relevant
knowledge or communities working on it, rather
than memorizing the knowledge itself. Finally, learn
who refers to technological and human learning
facilitators that can provide learning support. It also
refers to experts who are in possession of valuable
Learning can also be seen as a process.
According to Hodgins, learning is not a mechanical,
static, linear process, nor one that can be understood
by examining any of its components outside of its
systemic context. It is a very human, dynamic, and
complex flow that resembles an organic structure
more than a mechanical one (Hodgins, 2000).
Learning is an action-oriented as well as a social
process. It is the continuous process of gaining
existing personalized knowledge leading to the
creation of new knowledge. It is thus the cyclic
transition of knowledge acquisition and knowledge
application. The learning process and its components
will be described in details in the next section.
The learning process concepts discussed in this
section are abstracted from Nonaka and Takeuchi´s
SECI cycle, given in their book “The knowledge
creating company” (Nonaka, Takeuchi, 1995).
According to these authors, there are two different
kinds of human knowledge: tacit knowledge and
explicit knowledge. Tacit knowledge - a term
introduced by Michael Polanyi in 1967 - is the
personal and hidden knowledge which resides within
the mind. Examples of tacit knowledge are know
how, expertise, understandings, experiences and
skills resulting from previous activities. Tacit
knowledge is personal and hard to formalize, codify
or communicate. Unlike tacit knowledge, explicit
knowledge is codified, systematic knowledge that
can be transmitted in formal language. It can easily
be captured, accessed and shared. Similar to the
knowledge creation process, the learning process is
knowledge in action, a cyclic conversion of tacit
knowledge and explicit knowledge. This spiraling,
highly dynamic and complex process is modeled in
the figure below. It consists of four modes:
socialization, externalization, combination, and
internalization. These modes occur when tacit and
explicit knowledge interacts with each other. In the
following four sections, we examine each of these
Figure 1: Learning Process.
4.1 Socialization
Socialization is the first mode in the learning process
and the primary source of learning. As Polanyi
(1967, p. 4) mentioned “We know more than we can
tell”. There is a huge mass of high-quality tacit
knowledge embedded in people, which is not easily
expressible. This knowledge can, however, be made
available to others through socialization. In this
mode, learning occurs implicitly, within a social
context through observation, imitation, participation,
interaction and practice, rather than through written
or verbal communication (e.g. on the job training).
The process of acquiring tacit knowledge can be
supported by joint activities, personal connections,
social networking, and community of practice (CoP)
building. CoP “are focused on a domain of
knowledge and over time accumulate expertise in
this domain. They develop their shared practice by
interacting around problems, solutions, and insights,
and building a common store of knowledge”
(Wenger, 1998). Therefore, a learning system should
include an effective collaborative learning
environment that can encourage tacit knowledge
sharing and facilitate socialization.
4.2 Externalization
Through externalization, tacit knowledge is made
explicit, i.e., expressed in language or symbols, in a
form which can be accessed, understood, shared,
adapted, and reused. The conversion of tacit into
explicit knowledge involves techniques that help to
express one’s ideas or images as words, concepts,
figurative language (such as metaphors, analogies or
narratives) and visuals (Nonaka, Konno, 1998).
Externalization is a complex process aiming at
creating high-quality and valuable learning objects.
In the externalization process, software engineering
concepts and principles should be applied. The first
step in this process is knowledge de-
contextualization. That is, extract knowledge from
its context such that it is not bound to the situation
from which it stems, thus enabling the reusability of
this knowledge in different learning situations. The
next step is planning. That is, define a set of goals
and requirements that need to be achieved. Good
planning will leverage the created learning object to
its best use. Modeling and modularity are the
cornerstones of the externalization process. It is
crucial to disaggregate a learning resource into tiny
learning objects and identify how these objects relate
to each other. Those modular learning objects can
then be reused by different user communities for
diverse purposes. Once the objectives of the new
learning resource are defined and modular learning
objects are identified, it is possible to move to the
development step using all software and hardware
means that are able to reduce the time to develop
valuable learning content such as simulations and
experiments. The result of the application of
software engineering concepts in the knowledge
capturing process will be granular, organized and
reusable learning objects.
Successful knowledge capturing also requires the
use of metadata for describing learning objects as
well as adopted, common, open and accredited
standards (Hodgins, 2000). According to Hodgins,
metadata is the full and rich set of information
needed in order to find, filter, select, and combine
the information. It is also crucial to use standards for
metadata and learning objects to assure accessibility,
interoperability, adaptability, reusability, durability,
and affordability of learning (Hodgins, 2000).
Furthermore, since knowledge must be current in
order to be of value, attention should be paid to the
development of up-to-date and dynamic learning
resources. A possible way to achieve this is, instead
of inserting an existing learning object into a
learning resource, just to point directly to the
community which is currently working on the
development of this object. To achieve best results
from the externalization process, a learning system
should include a standard-based, collaborative and
effective knowledge capture system that supports
learning communities in designing, creating,
reviewing, modifying, and posting up-to-date and
valuable learning objects in a short time. This
system should particularly include an intelligent
component for automatic learning object annotation,
which is based on powerful data mining algorithms
and advanced pattern recognition techniques.
4.3 Combination
As discussed in the previous section, the output of
the externalization process is granular, annotated,
classified, context free, standard-based, and up-to-
date learning objects (i.e. explicit knowledge). These
quality learning objects can now be shared,
disseminated, stored, reused, analyzed, re-
categorized, re-contextualized, reconfigured,
reorganized, combined, and delivered. The
manipulation of existing learning objects leads to
new, possibly more complex learning objects. This
process is referred to as combination. The
combination process is supported by learning
repositories to store and manage learning objects and
their associated metadata, as well as learning paths
and activities. In a learning repository, new modular
learning objects can be added and existing ones can
be analyzed, compared, sorted, restructured and
associated. This results in new learning object
configurations and combinations or new learning
paths that can be applied to address different learner
needs and solve new problems.
In addition to learning repositories, the
combination process is most efficiently supported in
collaborative environments utilizing information
technology (Nonaka, Konno, 1998). Stacey
mentioned that active and alive learning
environments are more like learning communities
than learning repositories. They focus on bringing
people to people not just people to content (Stacey,
2003). According to this, learning has to occur
within a social context which supports listening,
viewing, reading, writing, speaking, commenting,
suggesting, asking, discussing, disseminating, and
sharing of learning objects and best practices among
community members. To help building the required
personal connections in an online social network, the
use of synchronous and asynchronous
communication tools is crucial. In addition to
learning repositories and learning communities,
powerful access and search capabilities across
content, metadata and people are required. A learner
should be able to query the learning system to
quickly locate appropriate learning resources, as
well as persons who share his/her interests or experts
who can help achieving better results.
4.4 Internalization
Internalization is the conversion of explicit
knowledge into new tacit knowledge (Nonaka,
Konno, 1998). In the learning process,
internalization refers to the embodying of
knowledge through reflection and application of the
gained explicit knowledge in a given context. It is
closely related to learning by doing, performing, and
working. In the internalization process
personalization is the key. Personalization is the
ability to get just the right stuff to just the right
person at just the right time and place in just the
right way and with just the right context on just the
right device and through just the right medium
(Hodgins, 2000). The learning system should
include an intelligent personalization/adaptation
engine, able to deliver quality learning resources that
are tailored to the learner’s preferences and learning
goals. Learner modeling is the cornerstone of the
personalization process. A learner model reflects
information that is specific to each individual learner
such as current knowledge level, performance,
progress, learning objectives, personal interests and
preferences as well as the topics from the supported
learning domain that the learner has already covered.
A possible way to achieve personalization is to
associate each learning object and each learner
model with metadata, relate each learning object
with one or more suitable learner models, choose the
potentially right learning objects and assemble them
to a learning path by applying similarity rules to the
learning objects and learner models metadata, give
recommendations based on old experiences and
previously solved problems, place the learner at the
center by giving him/her the chance to negotiate the
learning experience and to evaluate this experience
afterwards. To enable this, we would need a
database for learning objects, learner models and
their respective metadata, as well as an experience
database that will constantly be updated and
This section reports the results of a survey done in
the framework of PROLEARN, the EU Network of
Excellence dealing with technology enhanced
professional learning. This survey is part of an effort
to construct a roadmap that aligns business drivers
with enabling technologies to provide a logical
framework for coordinating R&D to meet the grand
challenges of European Technology Enhanced
Professional Learning (TEPL). Some of the key
questions raised in the context of this work are:
What are the envisaged forms of TEPL in 2015
(future states of TEPL) and what factors are they
going to be influenced by in the future? According
to the early findings of the PROLEARN survey, in
the future, TEPL should serve as a means to support
knowledge workers, promoting motivation,
performance, collaboration, innovation, and
commitment to lifelong learning. This vision of
learning entails: (a) TEPL becoming an effective
tool for enterprises to support and enhance work
performance and promote innovation, creativity and
entrepreneurship among their employees; (b)
learning becoming a catalyst in increasing
employability (flexibility and survivability of
employees); (c) democratizing the provision and use
of knowledge in order to provide equal opportunities
for high quality learning for all; (d) enabling and
empowering everyone to learn anything at anytime
at anyplace; and (e) commoditizing the professional
TEPL market, in order to achieve transparency.
The success of learning in a professional setting
is influenced by a number of external factors, (e.g.
technological, social, cultural, political and
economical). In the course of its survey of influential
factors, PROLEARN has developed a classification
scheme that categorizes factors according to their
impact and predictability, taking also into
consideration the level of agreement of the
respondents, as depicted in Figure 2. Dismissing
factors that according to the majority of the survey
participant appear to have no impact on TEPL,
PROLEARN focuses on 6 classes of factors, ranging
from factors with almost unanimously agreed high
impact on TEPL which are mostly predictable (Class
I, important trends) to currently unpredictable trends
that the majority viewed as being of low impact on
TEPL but with strong opposition (Class VI). The
results of phase 1 of the survey are summarized in
the following sections.
Figure 2: Classification of influential factors.
Class I includes factors with almost unanimously
agreed high impact on TEPL which are predictable.
Economical Factors include (a) many new
partnerships (e.g. between vendors, academics,
government agencies and industry consortia) will
emerge; (b) in workplace learning, learning
technology applications will be integrated into wider
enterprise applications suites, creating seamless
learning and working environments; (c) KM and
TEPL applications will be increasingly integrated.
Technological Factors include (a) tomorrow’s
technologies will compress the production cycle of
TEPL content; (b) massive issues (e.g. of spam,
viruses, identity theft, intellectual property, and
legality) will not cause the Internet to collapse; (c)
the ability of ubiquitous Internet access linked with
high bandwidth will have created a potential for
two-way interactive collaboration; (d) the use of
metadata will facilitate the search for, as well as the
management and the aggregation of content objects;
(e) online communities will be providing inspiration
for new ways of learning. Socio-cultural Factors
include (a) education and training will be more
flexible and tailored according to learner needs; (b)
there will be more networks between institutions,
making it possible for a learner to compile his/her
education by choosing learning modules from
different institutions.
Class II includes factors with almost unanimously
agreed high impact on TEPL not predictable.
Technological Factors include (a) the development
of TEPL will be intrinsically linked to the evolution
of new telecommunications technologies that offer
both ubiquitous access and relatively cheap high
bandwidth connectivity (b) the development of
common standards will have progressed
satisfactorily; (c) both complex delivery systems and
software applications will be made to support
various learners with different learning styles; (d)
learning modules will be developed according to
standards and therefore will be easily customized for
learning anytime, anywhere.
Political Factors include (a) education policies
adjust to new learning methods and technologies; (b)
a poor economic climate leads to cutbacks and
reduced funding for e-training by governments.
Socio-cultural Factors include (a) social climate is
driven by forces that encourage sharing, open
exchange and free collaboration, where people can
trust and learn from each other; (b) the social climate
is driven by instincts of control, suspicion and
distrust; (c) with global communications widening
horizons people’s identities are less shaped by their
nationalism and more by their interest and motives.
Class III refers to mostly predictable factors that
the majority viewed as being of high impact on
TEPL but with strong opposition. Technological
Factors include people will refrain from using
technology in their learning because technology will
become too complicated.
Class IV refers to unpredictable factors that the
majority viewed as being of high impact on TEPL
but with strong opposition. No survey statements
were classified under this category.
Class V includes mostly predictable factors that
the majority viewed as being of low impact on TEPL
but with strong opposition. Economical Factors
include (a) globalization will lead to a future where
the content of training will be internationalized; (b)
tough economic climate is a driver for cutting costs
in training within companies; (c) TEPL suppliers
will use low cost labor countries, e.g., India, Asia,
new EU member states, for an increasing part of
their development.
Class VI includes unpredictable factors that the
majority viewed as being of low impact on TEPL
but with strong opposition. Economical Factors
include (a) access to learning content will be
controlled by corporate and institutional
management; (b) TEPL suppliers don’t see the SME
market as an attractive market unless they can
provide their services through an intermediary or
they are supported by public funding; (c) TEPL
products and services are mostly traded, regardless
of the type or country origin of the supplier; (d)
learning will increasingly become a business
activity, following the business models, describing
them as knowledge-intensive services. Political
Factors include (a) too many languages, cultural and
legislative differences in Europe are hindering
advancements in TEPL; (b) more centralized
government and large corporations are making
corporate training very centralized and directive; (c)
public policies and funding instruments greatly
stimulate demand for commercial TEPL products;
(d) legislation and union agreements restrict the use
of personal data of employees; (e) research and
teaching approaches are constrained by government
and corporate needs. Socio-cultural Factors include
(a) a sudden leap towards self-directed learning
styles will leave a mass of people without any
possibilities of achieving skills; (b) the global village
will make local habits disappear.
In this paper, we highlighted the integration of LM
and KM and focused on learning as a skill and
process aiming at enhancing the personal and
professional performance and a means to
improvement and effectiveness. We analyzed the
learning process in terms of the SECI model and
reported the results of a roadmap survey done in the
framework of PROLEARN to identify the
challenges and requirements for technology
enhanced professional learning. In further work, we
plan to continue the survey to take the analysis a
step further and implement a complete, standard
based learning platform called CALP (The
Collaborative Adaptive Learning Platform)
including automatic annotation of learning objects,
learner model based information retrieval techniques
as well as KM methods and tools. The main aim of
CALP is to achieve the highly challenging task of
personalized learning.
Bork, A., 2001. Tutorial Learning for the New Century.
Journal of Science Education and Technology, vol 10,
no.1, 57-71.
Drucker P. F., 1989. The New Realities: In Government
and Politics, in Economics and Business, in Society
and World View, Harper & Row, New York.
Grace, A., Butler, T., 2005. Learning Management
Systems: A new beginning in the management of
learning and knowledge. International Journal of
Knowledge and Learning (IJKL), Vol. 1, Nos. 1-2.
Hodgins, H. W., (2000, February). Into the Future.
Learnativity, Vision Paper. Retrieved October 25,
2005, from
Hodgins, H. W., (2005, February). Grand Challenges for
Learning Objects. Presentation at Learntec,
Karlsruhe, Germany.
Jarke, M., Klamma, R., 2002. Metadata and Cooperative
Knowledge Management. Proc. 14th Intl. Conf.
Advanced Information Systems Engineering, Toronto,
Canada, LNCS 2348, pp. 4-20.
Learning Circuits Glossary, 2005. Retrieved November 7,
2005, from
Moe, M., Blodgett, H., 2000. The knowledge web. Merrill
Naeve, A., 2005. The Human Semantic Web – Shifting
from Knowledge Push to Knowledge Pull,
International Journal of Semantic Web and
Information Systems (IJSWIS) Vol 1, No. 3, pp. 1-30.
Nonaka, I., Konno, N., 1998. The concept of “Ba”:
Building foundation for Knowledge Creation.
California Management Review, Vol. 40, No. 3.
Nonaka, I., Takeuchi, H., 1995. The Knowledge-Creating
Company. Oxford University Press, Oxford.
Polanyi, M., 1967. The Tacit Dimension. New York,
Anchor books (based on the 1962 Terry lectures).
Stacey, P., (2003, February). People to People not just
People to Content. E-Learning for the BC Tech
Industry, Article. Retrieved November 2, 2005, from
Wenger, E., 1998. Communities of practice: Learning,
meaning and identity. Cambridge University Press.
Cambridge, UK.