LEARNER-ORIENTED APPROACH FOR ENTERPRISE
SYSTEMS IN HIGHER EDUCATION
USING TEL-BASED CONCEPTS
Research Positioning Paper
Dirk Peters, Liane Haak and Jorge Marx Gómez
Faculty II, Department of Computing Science, Carl von Ossietzky University of Oldenburg
Ammerländer Heerstr. 114-118, 26129 Oldenburg, Germany
Keywords: Learner orientation, Enterprise systems, Application usage mining, Higher education, Technology enhanced
learning, Adaptive learning environment.
Abstract: Higher education institutes like universities or universities of applied science can not ignore the need to
teach well know application and information systems, like Enterprise Systems, e.g. ERP-Systems. Case
studies are the most chosen way to introduce stepwise the handling of these systems to fulfil the needs of the
international employment market and to provide a practical focus within the education. Effective teaching
concepts have to improve this situation by considering the pedagogical and didactical aspects, which
supports the individual learning process of each student. Our research idea in this contribution considers
actual needs of higher education, e.g. present learning in a lab as well as e-learning courses supported by
new methods in Technology Enhanced Learning by recording student’s behaviour to guide him through the
system. Therefore we introduce an adaptive learning model which considers tracking and analyzing results
deduced with methods of Application Usage Mining and built up a new idea to improve the learning
progress. Within this adaptive learning model we propose an adaptive learning environment to bring
learners and supervisors together to achieve positive influences on learner’s behaviour and the learning
progress.
1 NEED FOR CHANGE
Knowledge transfer is a key factor of our
international oriented and globally connected
education system in which universities and higher
education institutions have to survive. Therefore
these institutes have to collaborate with similar
providers in different countries to compete in the
learning market. This leads to a redefinition of their
internal and teaching processes and an adaption of
their curricula to the actual needs of the employment
market. Higher education institutions have to
support the individual learning lifecycle based on
flexible learning services. Universities appear as
“knowledge centers” for lifelong learning and have
to be more flexible then in the past. The beginning
of virtualization demands personalised and flexible
learning services, whenever the learner needs and
wants to access them. These services become an
essential part of his lifelong learning.
Information and communication technologies
(ICT) are often used as an enabler to support these
processes. Technology Enhanced Learning (TEL)
e.g. allows effective and cost-efficient learning
environments based on individual needs and in a
personalised way. TEL is focusing on the
technological support of any pedagogical approach
that utilizes technology and encompasses virtual and
physical technology enhanced learning
environments (incorporating physical learning
spaces, institutional virtual learning environments,
personalized learning environments and mobile and
immersive learning environments). The aim of TEL
is to explore and develop effective practice in the
delivery of flexible, seamless and personalised
services to learners, focussing on the technological
interface between the learner and their learning
environment. Therefore learning activity consists of
learning resources, actions, context, roles and the
learning objective to support the learner to his
learning goals, respecting individual as well as
organizational learning preferences.
521
Peters D., Haak L. and Marx Gómez J. (2010).
LEARNER-ORIENTED APPROACH FOR ENTERPRISE SYSTEMS IN HIGHER EDUCATION USING TEL-BASED CONCEPTS - Research Positioning
Paper.
In Proceedings of the 2nd International Conference on Computer Supported Education, pages 521-526
DOI: 10.5220/0002874105210526
Copyright
c
SciTePress
The technology plays an important role in
supporting all these activities. That is surely one
reason why the European Union (EU) support a
number of project in this area, e.g. within in the 6th
framework the Network of Excellences PROLEARN
(www.prolearn-project.org) and Kaleidoscope
(www.noe-kaleidoscope.org) which have the main
objective to have shape the research area around
TEL. In this research environment we want to
introduce our new concept for teaching Enterprise
Systems (ES) in higher education.
Due to the actual needs of the employment
market, Enterprise Systems are getting more and
more important for the educational environment.
Though, the interferences and complexity of the
different perspectives in computer science, business
economics and the technology aspects make
teaching and learning quite difficult in this field.
Students need to be taught on practically approved
systems to develop their own practical experience
besides the theoretical lectures. Therefore ES offer
the capability for future pedagogic innovation within
higher education, which results from the possibilities
in illustration, visualization and simulation of
business and decision-making processes to students
(Ask et al., 2008).
This contribution introduces an approach, which
presents how technologies like Application Usage
Mining (AUM) could improve the teaching of ES in
higher education. Our research interests consider
ERP-technologies and their usage in adaptive
learning environments in higher education.
Furthermore we focus besides actual standard
software on new concepts resulting from up-to-date
research, e.g. Federated ERP (FERP)-Systems
(Brehm et al., 2009).
2 LEARNER ORIENTATION
Case studies are not the only a method for a
successful learning process design; there are still
problems occurring from missing background
information as well as from differences in the
domain knowledge of the learners. The actual
teaching material does not reflect the different
education and major studies of the students; it is the
same for all, equal whether they are studying e.g.
economics, computer science or pedagogic. Our
research approach follows the idea to utilize new
technologies from the wide range of TEL to reform
these education methods and to guide the learner
through his learning process: we call this Learner
Orientation.
The main idea of our approach is to put the
learner’s behaviour in the focus of interest. This
approach is not new (e.g. (Lenz, 1998)) and used in
many parts of continuing education for adults. The
orientation to the individual replaces the principle of
orientation to the participants which concentrates on
bringing participants into the institution of adult and
continuing education. This development is resulting
from the fact that traditional educational systems are
not considering the learner’s needs. By gearing to
the learner and his behaviour the learning process
deals with all aspects of the individual and leads to a
higher individualization of learning. The result is a
higher self-organized and independent learner. This
is the main reason why it is mainly used on
continuing and adult education. One result from this
is for instance the well-known method of Tandem
Learning, a method where two learners improve
their learning progress in a team and through
exchanging each other.
In the following section we reflect the problems
which occur especially in the area of ES in higher
education and present our technical oriented
approach to improve the learning progress in the
educational area. The aim is provide a technical
solution to consider the personal learning process
and progress to improve the teaching material and
teaching methods. It is a new method to give the
teacher a feedback about the individual problems of
the learner within the material and the tasks itself to
enhance the whole education in this field of ES in
higher education.
3 ENTERPRISE SYSTEMS IN
HIGHER EDUCATION
Higher education utilizes ES (e.g. Enterprise
Resource Planning (ERP)-Systems) or Business
Intelligence Systems to prepare students for the daily
routine in their working life and to provide them a
practical experience in the application of these
technologies. This is often supported by software
developing companies like SAP® or Microsoft®,
which also have an interest in getting the user,
developer and manager of tomorrow in an early
touch with their software products.
A common method for teaching ES like ERP-
systems is the usage of case studies. Leading
software companies like SAP® or Microsoft®
provide customized environments in form of
customized companies (e.g. the International
Demonstration and Education System (IDES) by
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522
SAP® and the Cronus AG by Microsoft®). For
supporting these activities the SAP AG build up an
own higher education competence network in form
of the University Competence Center (UCC) in
Magdeburg and Munich based on the SAP
University Alliance® programme. Besides, they
offer teaching material in form of case studies,
mostly based on material used for the origin
software training courses. These courses give an
overview and more or less detailed impressions of
the system functionality, but often lacks in missing
background information about company structures
and processes.
Though, this concurrent interests have to be
considered and the missing of pedagogic and
didactic aspects need to be improved and
implemented. For an effective usage of case studies
it is necessary to give the learner the chance to find
his own solution, to consider strategic aspects and to
make reasonable decisions. Therefore the case study
design is already a critical success factor (Hans et
al., 2008).
Besides improving this case study material we
want to provide a technical solution based on the
user interaction within the system.
4 IMPROVEMENT OF THE
LEARNING PROGRESS
As mentioned before, today’s business application
systems are mainly focusing on the integration of
different enterprise sectors within one enterprise or
across multiple enterprises. In the best case the
solution is an all-round product which covers all the
functionality a modern enterprise is dealing with. As
a negative side-effect the user faces a rising
complexity and an enormous amount of navigation
and handling opportunities within these systems.
This is one reason why it is becoming more and
more difficult for users to handle such complex
systems in an appropriate way (Haak et al., 2009).
Our superior aim for the identification of the
learner’s behaviour is the deduction of the current
state of knowledge and the system-familiarity of the
user from his executed tasks and steps he performs
during the work within the system. A schema which
illustrates our idea of the improvement of the
learning progress is visualized in Figure 1.
On the basis of logging and tracing information,
which is permanently recorded by the system, we are
able to analyze the behaviour patterns which also
includes specific deficits or general problems a user
Figure 1: Improved Learning Process.
has in performing his ES-supported work. We can
use this information to categorize different user
types depending on their knowledge level. Finally,
the learning material can be modified, according to
the learner’s knowledge category. In a dynamic
monitoring, the results of the tracking and analyzing
phase, which lead to a tailored course material, can
be tracked again to close the circle. Future behaviour
patterns will be tracked and analyzed in the same
way once again, in order to improve the learning
progress step by step.
The schema itself will be described in detail in
the following sections. The tracking and analyzing
part, which is supported by existing approaches, is
explained in Section 4.1. Recognizing these patterns,
we can identify possible problem solutions or
improvements in specific domains in a technology-
enhanced way, in order to improve the knowledge
and the performance of the individual user. The
enhancement can be realized on top of the tracking
and analyzing results. This improvement and its
influence on the learning process is explained in
Section 4.2.
4.1 Tracking and Analyzing with
Application Usage Mining
Regarding the new technological possibilities to
track the learner’s behaviour in ES, such as ERP-
Systems e.g., we can benefit in our work from the
existing AUM approach (Kassem, 2007). The user
leaves traces in the systems which are logged via
trace files and protocols. This tracking information
is a record of the performed steps or fulfilled tasks
of the user in the system. According to Figure 2, the
structure of such a User-Trace-File in the SAP R/3-
system for example contains the name of the
executing user, the code of the performed
LEARNER-ORIENTED APPROACH FOR ENTERPRISE SYSTEMS IN HIGHER EDUCATION USING TEL-BASED
CONCEPTS - Research Positioning Paper
523
Figure 2: User-Trace-File in SAP R/3.
transaction (VA01 for Create Sales Order), the input
values to the specific input fields (0001 for Sales
Organization), the transaction time via time stamps
and different other things.
The AUM idea is based on the well-established Web
Usage Mining (WUM) which is dealing with the
analyses of the user’s behaviour in the field of web
applications (Srivastava et al., 2000; Spiliopoulou,
2000). Defining AUM, Kassem pointed out several
differences between web applications and business
application systems in order to outline his approach.
While browsing the Web, the user has an
anonymous access to a web site, a user in a business
application system has to identify oneself by using a
log-in and his personal password. Another difference
is that a visitor of a web site does typically not need
an authorization in order the access the content,
whereas the user of a business application system
has a certain authorization, according to his function
in the enterprise in order to perform his assigned
tasks. Furthermore another difference is the user’s
behaviour and the objective, which is completely
free or undefined during the visit of a web site, while
a user of a business application system has to
perform predefined tasks according to a business
process, which have to be executed efficient and
with an optimal performance (Kassem et al., 2003).
Data Mining, Process Mining and Workflow
Mining, as other approaches in this field which also
need to be taken into account, are also playing a role
in this context. While Data Mining is the basis of all
these approaches, it deals with the creation of
knowledge out of large data amounts. Therefore
Process Mining uses the knowledge of processes and
extracts data, which describe the execution of
processes in order to model, save and reuse the
process knowledge (Schimm et al., 2003). Workflow
Mining controls, improves, executes and monitors
business processes and aims on the extraction of
information about processes from transaction logs
for the purpose of visualizing the current status of a
workflow model (Zur Mühlen and Hansmann,
2005).
Another approach is the Education Data Mining
(EDM), which is defined as the process of
converting raw data from educational systems (such
as interactive learning environments, computer-
supported collaborative learning, or administrative
data from schools and universities) to useful
information that can be used by educational software
developers, learners, teachers, parents and other
educational researchers. EDM is also an emerging
discipline, concerned with developing methods for
exploring the unique types of data that come from
educational settings, and with using those methods
to better understand the learner (Baker et al., 2008).
This information can also be used by the adaptive
learning environment, which is introduced in the
next section, in order to influence the learner’s
behaviour as well as on the learning progress itself.
4.2 Influences on Learners Behaviour
and the Learning Progress
The results of the tracking and analyzing phase,
supported by AUM, built the basis for our new
approach of a learner-orientation. According to
Figure 3, the adaptive learning model shows how
supervisor and learner are connected to the adaptive
learning environment.
Figure 3: Adaptive Learning Model.
The adaptive learning environment is based on a
Business Process Platform which represents
processes within an enterprise. Exercises will be
generated according to the underlying business
processes. This helps the learner to understand the
functions which are covered by the business
application system. It is also visible that supervisor
CSEDU 2010 - 2nd International Conference on Computer Supported Education
524
and learner are interacting with the adaptive learning
environment. This interaction is described in the
following.
For example, a learner who wants to fulfil a
specific task according to his exercises within the
system leaves a mark which can be expressed in a
graph or a path. Kassem is using Petri nets to
visualize these paths for the analysis of the data. In
our approach the adaptive learning environment
possesses a predefined workflow structure which
contains the “correct” path of fulfilling this task or
exercise. These paths can also be visualized with
other process description languages like Event-
driven Process Chains (EPC) for example.
An imaginable exercise can be the creation of an
order with more than one order item in the system.
In Figure 4 a Petri net illustrates the SAP transaction
Create Order. In the first state A the learner has to
enter the first order item for the order. After entering
an item number and the required amount, the learner
can complete the order with a confirmation in state
B. If he wants to add additional order items, the user
reaches state C. He can enter more order items and
then complete the order, which leads him in state B
finally. In comparison with the transaction workflow
of a learner, it is possible to find out, if the learner
could complete the task in accordance to the task of
the exercise. If the learner had problems in finding
the right button to add additional items to the order,
his path does not have a state C. In this way, the
adaptive learning environment can propose a hint in
order to help the learner in completing the task.
Moreover it could be assumed that the user
obviously needs help if he spends too much time in
state A.
Figure 4: Petri Net of the SAP Transaction Create Order.
The way of assistance how the adaptive learning
environment supports the learner, depends on the
exercise itself and the knowledge level of the user
which was captured by the system. Both parts can be
influenced by the supervisor.
The proposed solution of the adaptive learning
environment will improve the learning progress
because of different reasons. The mentioned benefits
from a didactical point of view have been introduced
in the first chapters. Nevertheless, the technical
realization offers more benefits for learners and
lecturers, e.g. it is possible to deal with each single
problem of an individual learner. The system could
offer different ways to get to the solution or
recommend related literature. In general, the
proposed solution can bring a lot of advantages to
the participating parties like the learners itself, but
also to the associated lecturers. Each group of
learners is associated to a supervising person, which
is normally represented by a teacher or lecturer of an
educational institution. In the traditional way, an
often unequal knowledge level of the learners exists.
The learners have different methods of learning and
the supervisor needs to respond to each one
individual. This is often very time consuming and
can mostly not be realized in practice. As a result we
can assume that the existing ways to identify the
learner’s behaviour are not appropriate to fulfil
today’s requirements in the field of handling ES.
It is also imaginable that different learners who
are working on the same exercise can benefit from
each other. Experienced learners can assist learners
who do not have the same experience. This scenario
is also supported by the adaptive learning
environment. Via the comparison of the solution
paths from different learners the system can identify
similar problem groups or learners which can help
each other in a successful way. If there is a learner
who already completed the task after having the
same issue, he might be the best contact person.
Furthermore the comparison of solution paths
can be used to find out whether there is general
problem concerning the content of the exercise. In
this case, if the majority of the learners have the
same issue which makes a positive completion of the
task impossible, there might be a deficit in the way
an exercise is putted. In this case the supervisor will
be informed about the appearing problem. As a
result it could be necessary to improve or rethink the
teaching tasks and methods. When talking about the
tracking and the further processing of the learner’s
progress, we need to take privacy issues into
account, which are not analyzed at this early step of
research.
5 CONCLUSIONS AND FUTURE
WORK
In summarization, the teaching of ES in higher
education is a young discipline, which has to be
improved to fulfil the needs of today’s students. The
LEARNER-ORIENTED APPROACH FOR ENTERPRISE SYSTEMS IN HIGHER EDUCATION USING TEL-BASED
CONCEPTS - Research Positioning Paper
525
learning process has to be more flexible and
individually customized to the background of the
student and to the needs of global employment
market. Therefore we focus on the concept of
Learner Orientation and design a concept to improve
the teaching of ES in higher education supported by
a technical solution which considers the knowledge
level of each individual learner.
The introduced AUM delivers techniques to
identify the behaviour of a user in an ES. Therefore
it uses the transaction data which is tracked during
the interaction of the user with the system and is
stored in trace- and log-files for further analysis. Our
proposed adaptive learning environment, as a major
part in the adaptive learning model, can analyze the
user’s behaviour and draw conclusions to the
knowledge level and possible problems during the
work. Then, it can deduce problem solutions on the
basis of the analyzed data and assist the learner in
completing his task. Besides the learner, the adaptive
learning environment can also assist the supervisor
or lecturer of a group of learners. Thus, an
improvement of the learning process including all
participating parties can be assured.
In future the architectural design has to be
formulated in much more detail. The different
components of the adaptive learning model have to
be more concrete and well defined. After this, a first
technical implementation of a prototype will be
focused. Therefore it is necessary to identify the
specific requirements and distinguish the parts
which definitely have to be realized. In the end, an
evaluation of the success and the major
improvements in comparison to existing approaches
will be carried out.
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