User Interaction-framework for Adaptive ERP Education
Dirk Peters, Marc Petersen and Jorge Marx Gómez
Faculty II, Department of Computing Science, Chair of Business Information Systems – Very Large Business Applications,
Carl von Ossietzky University of Oldenburg, Ammerländer Heerstr. 114-118, 26129 Oldenburg, Germany
Keywords: Web-based Information Systems, ERP Systems, Intelligent Tutoring Systems, ERP Education, GRAPPLE.
Abstract: Current methods used to learn and understand information systems in a company or in the field of higher
education are not sufficient when it is compared to the capabilities of today’s technologies. The most
common method used to teach and learn the practical issues of an information system is with the help of
case studies. However, the learning material in a case study provided to the learner is neither
technologically-enhanced nor adapted to the individual knowledge background and learning preferences of
a single, individual learner in a group. Adaptive learning environments offer a sufficient way to teach and
learn the usage of an information system while it tracks user interactions and adapts the learning content
towards the learner’s performance. Thus, the lacking integration of an adaptive learning environment with
the target information system is a major issue. In this contribution, we are presenting a developed user
interaction-framework (UIF) for web-based information systems (WBIS) in order to generate individual
tasks for each single learner depending on his specific performance during a hands-on learning process.
1 INTRODUCTION
The complexity of enterprise systems or information
systems (IS) in general, as an important part of
today’s companies, increases more and more. The
pervasion of computer supported business processes
in today’s enterprises affects nearly all business
sections and their employees (Konradin, 2011). This
is especially the case for Enterprise Resource
Planning (ERP) systems, which integrate data,
processes and functions of different business
departments, such as financial accounting,
production or sales and distribution (Gronau, 2010).
At the same time, the resulting, raising need of
appropriate learning material and employee trainings
needs to be met. In companies, as well as in tertiary
education institutes, like universities e.g., it is highly
relevant to bring people in contact with these
systems in order to ensure an effective use of the
different systems (Konstantinidis et al., 2010).
Primarily business economics or business
informatics people need to be educated in this area,
but also engineers or computer scientists should be
addressed. Especially the practical use of these
systems is an important part of getting people
familiarized with the software. Therefore, this
hands-on experience needs to be addressed by future
learning methods. This trend is also discussed in the
literature very well (cf. Hawking et al., 2005; Strong
et al., 2006; Cameron, 2008; Winkelmann et al.,
2012).
Unfortunately, the existing training methods
don’t fulfil this higher and specific demand. The
current and most common way to learn the practical
issues of a system is to conduct case studies. But
these case studies lack due to different reasons. First
of all, the common case studies do often not have a
deeper didactical background. This is because they
are mostly created by the software vendors
themselves and therefore only include technical or
functional issues. Furthermore, existing case studies
are paper-based and not technology-enhanced in
general. Therefore, the learner cannot benefit from
well-known advantages coming from methods like
distance learning or community-based approaches.
The reuse of learning material is much easier within
electronic learning environments (Baumgartner and
Kalz, 2005). This is very important, because the
systems are changing a lot due to software updates
and new software releases. The biggest shortcoming
is the ignorance of specific characteristics of each
individual learner. Most of the time, the learners
have a different background in knowledge and skills,
but existing learning material often addresses a
647
Peters D., Petersen M. and Marx Gómez J..
User Interaction-framework for Adaptive ERP Education.
DOI: 10.5220/0004386006470650
In Proceedings of the 5th International Conference on Computer Supported Education (CSEDU-2013), pages 647-650
ISBN: 978-989-8565-53-2
Copyright
c
2013 SCITEPRESS (Science and Technology Publications, Lda.)
group of people or students, which are very
heterogeneous in their style of learning. Some
learners are working on a case study very active and
reach the learning target very quick. Some others
need more theoretical information about the specific
functions in order to complete a task with
satisfaction.
2 ADAPTIVE INFORMATION
SYSTEM-EDUCATION
According to the literature, intelligent learning
systems exist since the beginning of the 1980s
(Schulmeister, 2007). Surely, the term intelligent has
a very vague definition, but starting with the first
computer assisted instructional programs, the first
reactive or adaptive systems were developed in that
decade. The combination of computer technology
with methods from the artificial intelligence (AI)
aiming at the improvement of educational
instructions can be summarized under the most
important software category, called Intelligent
Tutoring Systems (ITS) (Gharehchopogh and
Khalifelu, 2011). In a general understanding, ITS
consist of a model of the knowledge of a specific
domain (domain model), a model of the learner who
uses the learning system (learner model), a model of
the pedagogical strategy (tutor model) and a
component which is responsible for the
communication of the system with the learner
(interface) (Schulmeister, 2007). Therefore, ITS can
also be classified as adaptive learning systems,
because they react on the basis of the learner
behaviour (Brusilovsky, 2001). Reference models of
adaptive learning environments such as GRAPPLE,
which is used as the basis of the developed UIF, are
implementing the basic concepts in order to create a
broader environment for the learner and her/his
educational life.
The domain of IS is very relevant for a large
group of learners in the field of higher education
institutes (Peters et al., 2012). Furthermore, the IS
education lacks in efficiency due to the described
reasons. At the moment, there are no adaptive
learning environments existing which focus on this
specific IS domain and target group. Therefore it is
highly relevant to make use of the named major
advantages.
3 OBJECTIVE AND APPROACH
In general, the primary objective of our research was
to develop a framework which allows the integration
between an adaptive learning environment and a real
environment of a WBIS.
In order to achieve this objective we developed a
technical solution in a form of a learning system
which firstly supports the tracking of user
interactions and secondly offers the possibility to
display additional information, such as learning
instructions or tasks, within that environment. This
all should be done without modifying the source
code of the target web-based information system. In
the research process, existing related systems and
approaches were reviewed to identify ways of user
interaction tracking and ways to display additional
information. After this literature review, we found
out, that the proposed UIF can be based on an
existing reference model called GRAPPLE which
already comes along with some major functionality
for an adaptive learning environment, like the design
of an adaptation engine or the definition of
adaptation rules. Based on that reference model, the
theoretical concept of the UIF was designed. In a
last step, a UIF prototype was developed in order to
proof the underlying concept. The results are
presented and discussed in the following section.
4 USER
INTERACTION-FRAMEWORK
Nowadays, learning environments have no direct
connection between the learning system and the IS
itself. As a result, students work with two isolated
systems. But both systems contain relevant data
which can be used to improve the learning process.
By integrating both systems, the learning
environment can benefit from the existing data
stored in the IS.
According to Figure 1, the design introduces
existing components of a learning environment,
namely the students, the WBIS, the learning system
(LS) and the teacher. In order to overcome the lack
of integration the UIF adds three more components
to the information flow, namely the configuration
file, a browser plug-in and the tracking and
displaying script, to directly connect the LS with the
WBIS. This allows a way of learning where students
are able to receive their learning tasks, additional
information and guidance as well as support directly
within the targeted WBIS.
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From the user interaction point of view, the student
operates with the WBIS regularly via web browser
which has the UIF-browser-plugin installed. This
plugin receives information and tasks from the LS
over a displaying script and displays the tasks at the
right place directly on top of the WBIS. In addition,
the plugin communicates with the LS over the
tracking script. This tracking script forwards the
student interactions directly through the LS. This
enables the LS to instantly evaluate the learning
results and students’ behaviour in order to adapt the
learning process in terms of giving the student a new
task or additional help information.
Figure 1: Information Flow.
In order to make use of the UIF the creation of a
configuration-file is necessary as an initial step. This
file includes the structure of all relevant html-
elements of the GUI of the WBIS. This creation
needs to be done once for all relevant GUI-elements,
which want to be used in the context of a learning
scenario. Through this configuration-file the LS
knows about the structure of the WBIS and is able to
allocate html-elements to a function of the WBIS.
The underlying (adaptation) logic is delivered by
the GALE (Adaptivity GRAPPLE Adaptive
Learning Environment) framework as part of the
mentioned GRAPPLE framework. There, it is
possible to define adaptation rules, which describe
the exact behaviour of the LS. More precisely, the
adaption rules deliver a reasoning which exercise
will be presented to the student based on the tracked
information of the students' performance. As an
example, the system can provide positive feedback,
if the learner clicks on the correct button within the
WBIS according to her/his current task. However, if
the user clicks on a wrong button, she/he might get
additional information about what was wrong and
how the problem can be solved in a next step.
Besides these very simple examples, the UIF also
allows the definition of more complex adaptation
rules in order to provide a more efficient learning
experience.
5 UIF PROTOTYPE
This section demonstrates the use of the proof-of-
concept prototype within a use case scenario. The
chosen WBIS for the prototypical implementation is
SAP NetWeaver Portal installed in version 7.02
ABAP Trial Version on a virtual appliance of a
Windows Server 2003 R2.
The learning task of the use case scenario is to
show the learner how to create a new appointment in
the personal calendar of the WBIS in the SAP Easy
Access menu. Therefore the UIF will guide the
learner through two different ways of doing this (a
way represents a certain domain model). The first
approach is designed for novice users, showing them
how to perform the tasks by using a common
navigation path. The second approach is designed to
be executed by advanced users and shows how to
perform the tasks using a navigation shortcut.
After the login, the learner is located on the SAP
Easy Access page. The first domain model is
activated and shows the first task. Figure 2
exemplifies how this information is displayed in the
learning environment. The UIF tracks when the
learner has performed this task and the UIF will
display further information on the next pages. After
the user has entered some information into the form
the UIF shows the next task. In order get an
overview about the main idea of the use case
scenario, not every single screen of the described use
case scenario is shown here. Finally, the user is led
back to the SAP Easy Access page.
Figure 2: UIF SAP Example 1.
After completing the first task, the second domain
model is activated, because the first domain model is
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649
marked as known to the learner. As shown in
Figure 3, on the SAP Easy Access page the UIF
instructs the learner in a different way. After this
interaction has been tracked the next advice will be
given by the UIF and so on.
Figure 3: UIF SAP Example 2.
During this example learning session the UIF has
shown seven instructions (four in the first and three
in the second domain model) and tracked ten
interactions (six in the first and four in the second
domain model). Finally, the learner has created two
appointments in her/his personal calendar in two
different ways and mastered two domain models.
6 CONCLUSIONS
AND OUTLOOK
In this contribution we presented a UIF which
allows the allocation of individual learning tasks to
different learners who want to get familiar with the
usage of WBIS. Therefore, we presented an
integrated approach in order to track the learner’s
behaviour and display upcoming learning tasks
within the information system itself. Based on the
learner interaction, the UIF offers the possibility, to
allocate different kinds of tasks with regards to
previous knowledge and/or learning style, to
different types of learners.
The main idea of this contribution is the
integration of an adaptive learning environment with
a real, existing IS. This system and data integration
generates the main benefit of our approach which is
mainly the possibility of tracking, collecting and
analysing data about the learner’s tasks and her/his
performance in the system. Furthermore, through
this integration, tasks can be displayed context-
sensitive on top of the GUI of the WBIS. In
summary, we provide an approach which gives the
opportunity to learners to gather from hands-on
experience in a more efficient way of learning in the
field of IS education.
For future work, the prototypical implementation
can be enhanced with further functions and use case
scenarios in order to conduct a field study together
with students. Based on these experimental results, it
is possible to enhance the presented qualitative
added values of our approach with facts about the
quantitative improvements in IS education.
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