Pre-Publish Version of: Richter, T. & Pawlowski, J.-M. (2008). Adaptation of e-Learning Environments: Determining National Differences through Context Metadata – In: Arlt, H. (Ed.), TRANS. Internet-Journal for Culture Studies. No. 17/2008, TRANS, University of Vienna, Austria. Accessible at http://www.inst.at/trans/17Nr/8-15/8-15_richter-pawlowski17.htm

1. Adaptation of e-Learning Environments:
Determining National Differences through Context Metadata
Thomas Richter
e-Learning Research Center, Korean German Institute of Technology, KGIT, Seoul – South Korea
richter@kgit.re.kr
Jan M. Pawlowski
e-Learning Research Center, Korean German Institute of Technology, KGIT, Seoul – South Korea /
Information Systems for Production and Operations Management, University of Duisburg-Essen, Germany
jan.pawlowski@icb.uni-due.de
Abstract: The paper shows how existing e-learning modules can be internationalized using structured
information on the context and specifically culture. Reusing e-Learning contents is a promising concept for the
internationalization or cross-cultural purposes. However, most adaptation efforts are often limited to pure
language translation.
As the only alternative is rewriting, reusability allows a massive cost reduction by implementing and adapting
already established courses, for example into developing countries on a low-cost level. Our approach provides a
basis for international and cross-cultural adaptation. In the approach, we identify, collect and store as many
parameters about the source and target context and culture as available. After comparing contexts, we determine
changing needs by analyzing the impacting differences.
To implement this approach on a large-scale, we plan a public database containing the necessary information for
the comparison process. In our research, we have identified a set of around 170 parameters describing national
and, more specifically, cultural attributes related to various situations. Utilizing those in an adequate way will
lead to an easier and efficient adaptation process.
Short Bio:
Dr. Jan M. Pawlowski
Dr. Jan M. Pawlowski is the Director of the working group on E-Learning & Quality Management within the
Institute of Computer Science and Business Information Systems of the University Duisburg-Essen, Germany.
He coordinates a variety of German and international projects. He is the editor of the “International Handbook on
Information Systems for Education and Training” and has published numerous articles in books, journals, and
conference proceedings. He is actively involved in research organizations (AACE, GI, etc.) and coordinates the
quality workgroups within standardization organizations (DIN, CEN, ISO / IEC JTCI SC36).
Thomas Richter
Thomas Richter studied Information Technology and Technical Journalism. After examining in Information
Technology he worked as a free author for e-Learning contents and as a teacher in adult education for Information
Security and Electronic Communication Skills for some years. Besides he mastered in Information Economics
(focus: e-Learning) at the virtual Institute “VAWi” which is provided by the Institute of Computer Science and
Business Information Systems of the University Duisburg-Essen. He now researches on the context-related
Internationalization of e-Learning at the Korean German Institute of Technology in Seoul, South Korea.
Together with Dr. Jan M. Pawlowski, who is his mentor, he wrote a proceeding-paper for the Ed-Media
conference 2007 with the issue on Context Metadata.

2. Introduction
In this paper, we present an approach to describe and compare different learning contexts on the basis of context
metadata. Transferring e-learning contents and scenarios from one context to another, e.g., from a higher
education context in Germany to management training in Korea, requires a high adaptation effort. How can
existing learning scenarios be re-used? For this, we propose structured descriptions of the context, e.g., in a
database for public use, containing as many context descriptions as necessary. This shall lead to a procedure to
compare contexts, provide adaptation guidelines for the transformation process, and to efficiently assist in
developing solutions based on existing materials.
In the following, we first explain the general concepts on the adaptation of e-learning and re-use. We deduce the
need for a common description of contexts using a metadata approach. Afterwards, we show the concrete context
metadata classes and an example of how the comparison process will work. Finally, we show difficulties within
the evaluation on what we can deduce out of and expect from the data, gathered through the comparison process.
The Context of e-Learning and the Adaptation Process
The context of e-Learning denotes influence factors on learning scenarios and, more specifically, e-learning
environments. This means that we consider only factors which have an influence on the learning process but
cannot be influenced by the designer. As an example, the time in which a course is held is part of the defined e-
Learning environment and directly related to the course. But the country’s time zone(s) in which the course takes
place is part of the context. An e-Learning course, which is for example designed for the synchronous
transmission in the whole Russia over the Internet or television, has to deal with 11 time zones at once. The
huge number of time zones can be a reason not to use a synchronous communication approach in this case. The
counterpart to the context of e-Learning can be seen in the e-Learning environment, which for this paper shall be
defined as all components which are designed on purpose within the development process, e.g., the course itself,
defined preconditions concerning the age or knowledge of the human actors, the recommended literature, etc.
The adaptation of learning to different or specific situations and contexts is a widely discussed field. Edmund-son
[Edmu07] discusses cultural adaptation on a general level, defining relevant dimension for cultural
adaptation. Guetl, Garcìa-Barros and Moedritscher [Gue+04] discuss the adaptation on certain (cultural) user
needs. Other approaches focus on specific systems, such as Adaptive Hypermedia Systems [Brus97] or
intelligent software solutions [HoHa98, CrDe02]. All approaches are relevant, however, there is no holistic
approach, taking into account the most relevant influence factors for different classes of e-learning scenarios and
systems. A general approach has to take a lot of further influence factors into consideration so that an adaptation
process for e-Learning can be considered as complete and successful. An adaptation process is considered to be
successful if a course enables students within the new context to experience at least the same knowledge gain
than those within the original one. Therefore, we need to define the main influence factors.
The adaptation process for e-Learning consists of five steps as shown in the Figure 1: Firstly, suitable course
modules are to be found. In the second and most significant step, the reusability as well as the changing needs
are being determined and evaluated. Re-use aims at the efficient development of courses to avoid “re-inventing
the wheel”. Re-use is discussed in various contexts, specifically in the field of software engineering and
development [Jaco+97]. In the educational context, the discussion focuses on learning objects [Wile00] and
learning activities [KoMa04, MaSa05]. The variety of aspects on re-use is discussed in [LiBu03]. The main
outcome is whether it is useful or efficient to re-use existing materials / scenarios. If the course module can not
be reused for the targeted context, other modules have to be found which fit the requirements better or the course
module has to be rewritten. In the third step, the adaptation process starts by considering context information.
Adaptation in this concern means changing the course module Cx (A) and its requirements in a way that it can
be reused in the targeted context Cx (B). After having finished the adaptation process, the solution has to be
validated to find out if the result Cx (B) corresponds to the needs within context B. This validation should
utilize common processes, e.g., using the ISO/IEC validation criteria [ISOI05]. If it does not fit the require-ments,
the adaptation process has to be redone and maybe also the results of the comparison process in step 2
have to be reevaluated. After the validation, the course can be republished.
Figure 1: The comparison within the adaptation process
Context Metadata
Context information can be described in many different ways. However, it is necessary to describe contexts in a
format which can be used to communicate those contexts amongst people or to be used for adaptive information
systems. To achieve such a solution, we use context metadata to represent the variety of information in a
structured, machine-usable common language.
According to the W3C group, Metadata are considered as “data about data” [W3C98]. The less abstract
definition of the IEEE group describes metadata as “information about an object, be it physical or

3. digital” [IEEE02].
Therefore, context metadata are defined specifying influence factors on learning environments in a clearly defined
data structure. These data can for example be stored within a public database and addressed for search, direct
download by people, and applications such as expert systems.
In the following, the classification model of the e-Learning context metadata is discussed. We focus specifically
on cultural issues in the adaptation process. Carrel & Eisterhold show the significance of cultural aspects for the
adaptation of learning environments as they write that “one of the most obvious reasons why a particular
content schema may fail to exist for a reader is that the schema is culturally specific and is not part of a
particular reader’s cultural background” [CaEi83]. Most found influence factors are related to culture in some
way. However, modeling our metadata approach, a class “culture” has to be separated from other fields.
Therefore, a clear definition of the term culture and its differentiation from other fields is necessary. Following
the definition by Hofstede [Hofs91], we define culture as a mental coding which every member of a society,
organisation or group experiences and acts corresponding to this. This definition allows the conclusions that in
our case not everything is to be considered as being called an aspect of culture and further on that cultural
aspects are only to be considered, when they correspond to the behavior and thinking of a group (majority) of
people (and not to individuals as Trompenaar suggests [TrHa06]).
Another found modeling problem is that one culture per nation can not be implied [HoHo05, Gann04, Pog05].
This would not fit the reality. Therefore, we design our metadata and related database so that a single country
needs to be relatable to more than a single cultural dataset.
Different approaches have already defined dimensions of cultures [HoHo05, HaHa90, Hend96]. Since concrete
attributes are needed for the comparison process, the dimension models do not go far enough. Nevertheless,
during the research, they allowed the definition of some (e.g.) cultural context metadata. Based on those models
and approaches, we designed context blocks representing classified aspects of context. Around 170 parameters
were identified. Designing the context blocks in a way that no relations in between remain has not been
possible, but there have been found attributes within every single class, which are not related to other classes
and on an intuitive level, every class contains only such elements which internally are more related to each other
or the class than to others. The data behind the single context metadata vary from a single numerical value to
complex collections of documents in different formats and languages. In the following, a list of the single
context blocks is shown followed by some references, which led to them.
• Demographical development [UN06], [ChWe04]
• Religion [Asch06], [Pau80]
• Technical infrastructure [Edmu07], [Seli04]
• Rights [Lean05], [Marc04]
• History
• Politics [Kear90], [Davi06]
• State of development
• Media richness [Wils02], [Gul+06]
• Financial aspects [Guna05]
• Human actors [Zimm01], [Niel93]
• Rules [Wood04]
• Companies [DaSe04], [DIN04a]
• Geography and educational infrastructure [SaFo02], [LaZh03]
• Learner satisfaction – Known demands [John00], [DaJo02]
• Internet security [Kno+03]
Culture as the still largest class of influence factors has not been included above, because it can further be
divided into the following sections:
• Language [Leon02], [DeMa06]
• Ways of communication [Cak+02], [Ting88]
• Humor [Kend06], [LiLe07]
• Acceptance of media types [Wils02]
• Gender differences [Hasl00], [Hend07]
• Social capital [Mer+02], [Stone01]
• Habits and preferences [Noce02], [DuMa07]
• Acceptance of technology [HeNk06]
• General opinion [Bea+06]
• Taste [Loh+06], [Gans99]
• Indigenous cultures [Dys+06], [HuDa07]

4. • Hints for technology transfer [LaDy06], [LeSm94]
• Pedagogical approach [Mcca07], [DuMa07]
The dependencies between the context blocks are shown in Figure 2, as they are to be modeled within the
database and correspond to our recent state of research. As an example, the classes “Technical Infrastructure”,
“Rights” and “Human Actors” also are strongly related to a lot of other context blocks.
Figure 2: Dependencies between Context Blocks
By our approach, we have outlined a discussion base to describe context in a structured way. It is clear that a lot
of research remains: For specific purposes, extensions are needed, for other purposes only a few categories are
needed. Building those application profiles will be the task in the user communities.
The Comparison Process
The comparison process shall yield the actual differences between two contexts as a result. For the comparison
process and in special to find a solution on the decision which of the found differences are significant for
changing needs, various methods can be used, as similarity comparisons (similar situations have been handled
in a certain way) and recommender systems [MaSa04] (decision support systems). They also can base on former
experiences [PaBi06], e.g., by using knowledge databases. The final decision on changing needs at least at the
moment has to be done manually: Until now, the number of documented experiences is very low and focused on
certain aspects and situations (e.g., user satisfaction after changing the level of activity, a.o.). Another reason is
the fact that a monitored difference between two contexts does not necessarily mean that a course module (or
course) actually is impacted by this attribute. Therefore, we need a collection of experiences, to be made after
and during the adaptation process. Such documentations will be crucial to determine cross-impacting attributes
and the kind and level of their impact.
Before the data can be compared, they have to be gathered. One data set is partly attached to the course module
which is to be adapted. The attached data are those, which describe the individual skills and attributes of the
author. All the rest of the data concerning the course are attached as references to the database. This shall reduce
the overhead. The other dataset related to the targeted context completely has to be picked up at the database.
The gathering of data as far as the data are available (stored within the database) can automatically be collected.
Figure 3: Data-gathering and -comparison procedures
In Figure 3, the necessary steps to realize the comparison process are visualized. The arrows with dashed lines
symbolize data requests while those with full lines represent actions in which data are transported (unless if
tracked or pushed). The process is initialized after the data collection function has been started. The function gets
the name of the targeted context Cx(B) and the storing location of the course module as input information. In
the following the single actions are named by number: Firstly, the data concerning context Cx(A) which are
attached to the course are collected: (1 + 2) the personal data representing the author’s individual attributes are
tracked out of the course module (1 access to the document, 2 reading and storing the data). The general
information which are relevant to describe the author’s context represented by pointers on the database are
identified (3), tracked (4) and the data taken (5) out of the database. (6) The complete dataset concerning the
targeted context Cx(B) are requested at the database by name (pushed or pulled). The data are given to the
comparison function (8), which is divided in a part which realizes automatic comparisons (output 10a) and pre-structures
(9) the information for the manual comparison process (output) as far as they cannot be compared
automatically and the manual comparison process which for the further automated use again has to lead to a
computable input (10.b). The result of those two processes is a list (10a, 10b) which shows all differences
between the two contexts. Data, which only are available within a single context, are to be handled as
differences. This list finally is handled to the evaluation process in which later on the adaptability will be

5. checked and recommendations on changing needs can be given.
The evaluation of the differences deducing changing needs provides difficulties because there are not only
impacts resulting of pair-wise differences but also because of combinations of different attributes, which are not
yet researched. At least it will have to base on documented experiences. Furthermore, the differences - if only
pair-wise taken into consideration - do not lead to a trivial decision, because depending of the targeted context
the acceptance level concerning unused situations sites the users play a significant role. Also here in most cases
at least in the first time, manual decisions are unavailable. What the system can do is providing recommenda-tions
(as a recommender system) but taking the full decision is not possible because of expectable cross-effects.
The comparison process together with the evaluation phase will exemplarily be shown in the following on
sample data sets of South Korea and Germany. First of all, selected data are to be shown as stored within the
database. The attributes have consciously been chosen in a way that different data structures which need different
methods for comparison are shown.
GID Id.-Nr. MD-Name Germany Republic of Korea
1 CM10001 Teacher's Role Assistant on the way to knowledge Knowing Authority
2 CM10002 Value of Errors Chance to learn Disaster
3 CM10003 Context Type of
Society
Low Context High Context
6 CM10006 UAI: Uncertainty
Avoiding Index
65 85
7 CM10007 PDI: Power Dis-tance
Index
26 60
12 CM10012 Language German Korean
15 CM10015 Cultural Vari-able,
Language
Various dialects, High German cul-tural
elements in understanding
Various dialects, Korean cultural
elements in understanding
45 DDM20005 Education
Achievement
Regionally balanced, primary
school (4yrs), middle school
(6yrs), high school (3yrs), free of
charge, balanced between women
and men. University 1st education
free; Duty middle school
Concentrated on towns, primary
school (6yrs) and middle school
(3yrs) free of charge, high
school (3yrs) and university
must fully be paid. Duty middle
school
52 RM30001 Main Religion Main: Christian Multiple religions, Main: Bud-dhist
& Christian
64 TIM40009 Mobile Technol-ogy
Infrastruc-ture
In towns and occupied countryside
available
In towns and occupied country-side
available
81 RM500014 Controlled His-torical
Views
“Auschwitzluege”, violation of law
to say the persecution of Jews did
not happen, (max. 5 years impris-onment)
It is not allowed to publicly
"praise" North Korea (max. 5
years imprisonment)
89 PM70004 Foreign Affairs No direct conflicts Conflict with North Korea
108 HAM10005 Expectable
Group Behavior
Group members are emancipated
and expect cooperation
Group members search group
leader, who defined the group's
opinion
125 HAM10022 User Activity Students expect to have influence
on their learning style and con-tents,
borders must be defined
Students want to have clear de-fined
tasks and methods to use
129 HAM10039 Self Set Educa-tional
Goals
Knowledge and Interest Carrier and Social Position
135 HAM11045 Way how to give
Feedback
Direct feedback incl. Critics Direct critics can cause face-loss
Table 1: Sample comparison between Germany and the Republic of South Korea
The data in table 1 are chosen in a way to demonstrate general differences between collectable data and their
structures on the one hand and the different ways how to compare and at least evaluate them. There are further
fields in the database, such as the related contextual elements, attribute-definition-time, date of the last update,
information source, data-structure-type (type) and a defining description (kind) of the contained information.
In the following, the attributes are pair-wise discussed first on a general level. We try to contrast the differences
intentionally as much as possible, as it is clear that there are nuances in between depending on individuals.
Afterwards, the comparison takes place, followed by an evaluation on how the data can be interpreted. First
representative concrete and interpretable experiences within the described context are expected after finalizing an
evaluation in October 2007.
1 – Teachers Role: The teacher’s role in Germany is the one of an assisting adult [HoHo05] who basically

6. shows and helps the learner to understand the learning contents [Hend96]. He is allowed to present wrong
statements as a method to provoke discussions or irritations within the group of learners. In Korea, the teacher is
seen as person of authority, rarely to be questioned. Spreading wrong information would irritate the students and
in the worst case could result in the loss of respect.
As a consequence, this means that tasks within the learning content, which ask for the critical discussion of
content or methods, have to be presented in a different way. In the opposite way (adapting a course from South
Korea to Germany) the reaction sites the students may be irritated, they could refuse their sympathy if the
author’s writing style or the tutor’s behavior is too dictatorial but finally they would be able to deal with the
situation.
2 – Value of Errors: Depending on the culture, the value of errors can be seen very differently [Hend96]. While
errors in Germany mostly are seen as a chance to learn, in South Korea they show a lack of knowledge and
expertise and can cause a face-loss. In the consequence for e-Learning courses and in special for tutorials and
evaluated practices, the way how to point on errors has to be chosen very differently: While in Germany it is
usual to directly tell a student that (and why) an answer or implication is wrong, in Korea this has to be done
more cautiously, e.g., by stating that the chosen solution has been already good but could be better while taking
an other way or different conclusions.
3 – Context Type of Society: The division of societies into the two types, high context and low context, has
been defined by Hall [HaHa90]. While in high context cultures a lot of contextual elements help people to find
their place within the society and to understand rules, low context cultures do not provide this feeling of
security but define everything which is seen as necessary information explicitly. In this definition Germany is a
low context society and Korea a high context society. Regarding e-Learning, the specific shape of this
dimension is significant to know from both, the originator’s society and the targeted society. Typical attributes
which impact learning for high context societies are for example a strong developed nonverbal communication, a
strong distinction between in-group and out-groups, a high use of metaphors and implicit messages (a lot is
interpreted between the lines), a higher preference to relationships than to reaching goals and a process oriented
working style (instead of product oriented). As a consequence, contents, texts and pictures have to be modified
as well as tasks and group work need to be newly defined.
15 – Cultural Variable, Language: Not only the language itself is significant but also the cultural component
which is integrated in the way how language (and silence [DaJo02]) locally is used [Leon02]. Taking the upper
example with the high and low context societies it is obviously that even if both contexts have to deal with
their first foreign language the interpretation of the contents may be very different. But even when both countries
use the same mother-tongue English irritations can appear [DeMa06].
45 – Education Achievement [Ram+07]: The university achievement is interesting from more than one
perspective; there are cross-effects to other context metadata. We only show some obvious examples and
consequences. First of all, knowing about the education structure allows conclusions on different curricula and
the necessity to pay fees for education in certain levels, combined with a possible duty going to school (context
metadata No. 80, RM50013) until a certain minimum level gives hints on the (generally) expectable knowledge
and skills of the students within a country.
Comparing South Korea with Germany, in Germany the basic education is completely free and education is
defined as a basic human right. The first 10 levels of school (Primary 4, Middle 6) are duty by law. Children
can, if they fulfill the preconditions additionally (free of charge) go to the high school (2-3 years). The
university in Germany is also free of charge as far as it is the first higher education. In South Korea there is a
duty going to school until end of the Middle School (9 years). Primary School (6) and Middle School (3) are
free of charge. High School and university are to be paid. The aim of the learning process is the best possible
education so that there is a chance to reach a higher social position.
These metadata can not be used for explicitly defining actual changing needs but gives hints if other metadata in
special in combination crucially are to be taken into consideration. At least, the experience will show if such
kinds of metadata provide the expected use.
64 – Mobile Technology Infrastructure: This attribute in special should be used when the implementation of
mobile learning technologies is faced. Depending on the used type [ArCl04] of mobile technology (context
metadata No. 65 TIM40010) and the divide within the country (also attachable to regions [Guna05], if defined)
different technologies can be used [TiLa04]. The mobile standard in Germany is the 3G-Standard, including
GSM, GPRS and UMTS. While GSM (low bandwidth) is nearly everywhere reachable, UMTS as a high-speed
mobile standard is reduced on the urban environments. Location based services in a useful way are only possible
within certain regions. In South Korea, CDMS is the commonly used standard and it is accessible in all habited
regions, so that nearly everywhere a high-speed access to the internet via mobile technologies is possible.
UMTS recently is going to be implemented. The difference between both countries in the kind of technology
and its divide for example shows that education via mobile television or multimedia content can easily be

7. implemented in South Korea but right now not within Germany.
129 – Self Set Educational Goals: As already discussed, this attribute represents the motivation which causes
students to learn [MoKe96]. While the German learn because they want to gather knowledge, be able to manage
their live by getting the ability to solve problems and reach social competence, the South Korean students want
to fulfill the society’s (in special their family’s) expectations and find the highest possible position within the
society. The kind of learning motivation alone is no indicator on changing needs and does not need to be
evaluated in this meaning. It is not useful to change a constructivist course into a behaviorist one because the
learning purpose is different. However, this attribute can point out that it could be useful introducing on how to
deal with open tasks when they are to be implemented. The gathering and comparison of the data can be done
automatically but the evaluation is to be done manually.
As we have shown by those examples, the comparison process based on our context metadata can provide useful
hints for the adaptation process. There are further effects which can be expected, such as a better awareness of
problems and changes in intercultural settings or new systems using our adaptation guidelines. However, at the
current state our framework will enable users to take many aspects into account which have been neglected in
many adaptation projects.
Conclusion
The context-based internationalization of e-Learning situations is related to a lot of different aspects to be
considered. Cross-effects between those fields as well as the necessary differentiation of regions and societies
within single countries and the non-static character of the context lead to a complex adaptation process. Not even
the comparison of two contexts can be realized automatically at the current stage. The act of changing the
content and aspects of the situation will stay a manual procedure based on human decisions. The result of this
research until now is the knowledge about significant factors which now can be evaluated and extended.
Based on our research, we will develop a context metadata database providing information and serving as a base
for globally distributed e-learning development and adaptation processes. One step on the way is done: A large
set of possible parameters has been found and defined in form of context metadata. The next step is to find out
which of those have significant impacts on different learning situations, which have to be avoided and how it is
possible to optimize the adaptation process.

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