TECHOLOGY ENHANCED PROFESSIONAL LEARNING

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.

1 INTRODUCTION

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employee skills become outdated in three to five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

268

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

 SciTePress

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.

2 LEARNING & KNOWLEDGE

MANAGEMENT

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

KM.

3 TECHNOLOGY ENHANCED

LEARNING

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

TECHNOLOGY ENHANCED PROFESSIONAL LEARNING - Process, Challenges and Requirements

269

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

knowledge.

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.

4 LEARNING PROCESS

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

modes.

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

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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

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271

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

rectified.

5 TEPL: CHALLENGES &

REQUIREMENTS

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

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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.

6 CONCLUSION & OUTLOOK

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

TECHNOLOGY ENHANCED PROFESSIONAL LEARNING - Process, Challenges and Requirements

273

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.

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