=Paper=
{{Paper
|id=Vol-143/paper-5
|storemode=property
|title=Improving Interoperability through better reusability
|pdfUrl=https://ceur-ws.org/Vol-143/paper05.pdf
|volume=Vol-143
|authors=Elisabetta Di Nitto,Roberto Tedesco
}}
==Improving Interoperability through better reusability==
https://ceur-ws.org/Vol-143/paper05.pdf
Improving Interoperability Through Better Re-usability
Elisabetta Di Nitto Roberto Tedesco
Politecnico di Milano - DEI Politecnico di Milano - DEI
Via Ponzio 34/5 Via Ponzio 34/5
Milano, Italy Milano, Italy
dinitto@elet.polimi.it tedesco@elet.polimi.it
ABSTRACT The model-oriented strategy refers to the idea of having
Interoperability among heterogeneous systems can be reached standard, semantically-rich data models shared by various
by adopting and combining at least two strategies: one systems. Sharing the same data model enables the possi-
interface-oriented and the other model-oriented. The first bility of reusing the same data in different systems, and
one refers to the idea of defining well-known interfaces that dramatically increases the interoperability among such sys-
systems should expose. The second one refers to the idea tems.
of having standard, semantically-rich data model shared by Within the context of e-learning, SCORM [1] offers, among
various systems. The SCORM standard supports the model- the other features, a rich data model that can be used to
oriented integration strategy by offering, among the other define and share Learning Objects (LOs). We argue that,
features, a rich data model that can be used to define and despite the fact that it is the most emerging and promising
share Learning Objects. We argue that, despite the fact that standard, SCORM does not address properly some key is-
it is the most emerging and promising standard, SCORM sues, such as specification of metadata describing LOs, and
does not address properly some key issues, such as specifica- composition of LOs. As we discuss in this paper, such is-
tion of metadata and LO composition. As we discuss in this sues affect directly the possibility of re-using instructional
paper, such issues affect directly the possibility of re-using materials both within the same e-learning system and, even
instructional materials both within the same e-learning sys- more, across different systems.
tem and, even more, across different systems. Within the The assumption we start from is that an e-learning system
Virtual Campus approach we have proposed some exten- (or a set of interoperating e-learning systems) addresses the
sions to the SCORM object model that aim to address the needs of three main classes of actors: Authors, Teachers,
above issues. and Learners. Authors design and build courses, possibly
modifying and composing LOs; Teachers enact and manage
courses, exploiting available LOs; finally, Learners attend
Categories and Subject Descriptors courses by consuming LOs, possibly, with the supervision of
K.3.1 [Computers And Education]: Computer Uses in Teachers. Given such a scenario, we distinguish between two
Education kinds of re-use activities: re-use for authoring and re-use for
teaching. The former activity, at authoring time, requires
a data model for LOs that is rich enough to support re-
General Terms use of parts, creation of LOs that reassemble existing ones,
Standards for interoperability, metadata models modification of the workflow that defines the way LOs will
be executed (sequencing in the SCORM terminology). The
latter, at the time a course is given, requires mechanisms
Keywords and metadata that simplify the publication of the LOs and
SCORM, Learning Object, metadata, composition, aggre- their enactment.
gation, sequencing In this paper, based on the experiences we gained within
the Virtual Campus project [6], we propose some extensions
to SCORM aiming at supporting re-use for authoring by em-
1. INTRODUCTION powering the mechanisms for LO composition and at sup-
Interoperability among heterogeneous systems can be reached porting re-use for teaching through the definition of proper
by adopting and combining at least two strategies: one metadata for LOs. The paper is structured as follows. In
interface-oriented and the other model-oriented. The first Section 2 we discuss some limitations of the LOM metadata
one refers to the idea of defining well-known interfaces that specification, and propose our extensions. In Section 3 we
systems should expose. By exploiting such interfaces, sys- discuss about the limitations of the SCORM content aggre-
tems can call each other services thus enabling exchange of gation model, and introduce our approach. In Section 4
data, execution of queries on remote data, etc. The Simple we focus on enhancing the SCORM aggregation/navigation
Query Interface (SQI) [10] is an example of such an interface model, presenting our model for LO composition. In section
for the e-learning domain. 5 we present Virtual Campus as a proof-of-concept platform.
Copyright is held by the author/owner(s).
In Section 6 we discuss some related approaches aiming at
WWW2005, May 10–14, 2005, Chiba, Japan. offering mechanisms for composition of instructional mate-
.
rial. Finally, in Section 7 we draw some conclusions. Our extensions to the LOM mainly aim at providing sup-
port to all the aforementioned activities, explicitly express-
ing some LO properties the LOM does not consider.
2. METADATA SPECIFICATION In particular, as for the software needed for LO fruition,
The Learning Object Model (LOM) offered by SCORM is the LOM seems to concentrate on the specification of the
based on the idea that instructional material is enveloped in client-side expected characteristics (browser and OS), but
metadata describing the instructional material itself. The does not address to issues of describing the server require-
union of the instructional material and of the correspond- ments as well. Thus, in order to support automatic config-
ing metadata is a Learning Object (LO). Examples of LO uration (point A), we modify the LOM metadata 4.4 Re-
metadata are the Language of the instructional material, the quirements, adding a new field expressing whether require-
Description of the LO content, etc. ments regard client- or server-side. Moreover, we propose to
While experimenting with LOM, we have realized that it adopt the CC/PP [13] (Composite Capabilities/Preference
has the following weaknesses: Profiles) standard to express requirements and capabilities
clients and servers have to meet.
1. The exact meaning of some metadata is difficult to be We have also extended the LOM with some additional
specified (e.g., Semantic Density, Difficulty, etc.) As metadata that are summarized in Table 1. We can state
a result, Teachers tend to fill them with values that the level of supervision a given LO requires (e.g., if a tu-
are in the middle of the available scale, making them tor should be available during fruition of the LO) whether
completely useless. the LO requires group (cooperative) study, whether some
artifacts have to be created at the end of fruition, whether
2. Some important aspects about LOs cannot be expressed. the LO requires communication facilities among Learners,
As an example, there is no way to say whether a given and whether the communication or cooperation have to be
LO has been designed to support group study or indi- synchronous or asynchronous.
vidual study. Such metadata can have various uses. In particular, they
can help addressing the issues related to point B, for in-
3. The defined metadata are not fully machine-processable. stance, if the cooperation attribute is set, based on the in-
Some of them are defined as free-text (e.g. Installation formation contained in the Techinical attribute, the runtime
Remarks,) while others rely on vocabularies which are platform could automatically configure a version-control server
not precise enough to allow for a full-automatic pro- which makes it easy for Learners to work on shared docu-
cessing. ments. In addition, if the supervision mode is set to tu-
tored, an up-load facility could be configured in order to
The LOM has been clearly defined in order to improve
allow Learners to send created documents, and Teachers to
search and discovery of instructional material. However, we
manage and evaluate them.
argue that metadata can also be exploited to support many
The aforementioned metadata can also effectively sup-
other activities. In particular, we think at the following
port both automatic tutoring of Learners and LO evaluation
ones:
(points C and D), since they permit to collect information
on Learners’ preferences and attitudes. As a result, profiles
A Automatic configuration of software needed for fruition
can be built and exploited to guide the tutoring process.
of a LO. Following the metadata specification, the
In addition to the introduced extensions, our modified
platform could automatically configure pieces of soft-
LOM supports the concepts of non-electronic LO, precondi-
ware required for the LO to be viewed and exploited
tions, and postconditions. In the following we discuss these
by Learners. As an example, a video streaming server
extensions.
required by a given LO could be automatically config-
In our model, LOs can represent either digital contents
ured in order to work with the e-learning platform.
available in the LO repository or live lectures held in class-
rooms, the metadata Access Modality and Place highlight
B Automatic configuration of software supporting the LO
this difference. In doing so, we give the opportunity to seam-
fruition. As an example, Teachers could configure the
lessly mix electronic and regular learning.
system in such a way that LOs requiring asynchronous
Finally, we can specify properties which must hold before
communication are provided with a forum, the ones
the execution of a LO (Preconditions), as well as properties
requiring synchronous communication can exploit a
which will be true at the end of LO fruition (postconditions
chat, while cooperative LOs can take advantage of a
or, as we call them, Learning Objectives). Both Precondi-
shared, versioned repository. Notice that such a pieces
tions and Learning Objectives can predicate on data stored
of software are not part of the LO requirements as they
in the Learner’ profile, and on Time.
just represent supporting tools.
C Tutoring. Metadata expressing instructional require- 3. CONTENT AGGREGATION
ments can be useful to provide Learners with person- The goal of the SCORM Content Aggregation Model (CAM)
alized automatic tutoring. In fact, they could be used [2] is to provide mechanisms that allow instructional mate-
to support selection of the most appropriate LO for rials to be aggregated. The CAM is based on three com-
a learner, depending on his personal preferences and ponents: Assets, Sharable Content Objects (SCOs), and
attitudes. Content Organizations. Such components are enacted by
means of the SCORM Run-Time Environment (RTE) [3].
D Evaluation. Metadata could be used by Teachers in The RTE “describes a common content object launch mech-
order to analyze and evaluate the effectiveness of LOs. anism, a common communication mechanism between con-
Field name Field description
Supervision Mode The level of supervision on Learners’ activities: “none” (no supervision), “tutored” (a tutor
is available; during fruition learners can explicitly request his/her supervision), “supervised”
(the supervisor is always present during the instructional process), “driven” (Learners act
in a passive way, by strictly following the Teacher’s instructions.)
Cooperation Attribute Whether Learners should take the LO in cooperation.
Communication Attribute Whether Learners will be provided with communication facilities, while exploiting the LO.
Synchronism Attribute In case of a cooperative or communicative LO, it specifies if the LO must be taken syn-
chronously by all learners or asynchronously.
Group Cardinality The cardinality of the group involved into the fruition of the LO. Meaningful group cardi-
nalities are “1” (self-study), “2” (pair study, both learner-learner and learner-instructor),
“m” (group study). Note: 2 is the minimum group cardinality for cooperative LOs and the
maximum one for non-cooperative LOs.
Artifact Attribute Whether the LO requires Lerner(s) to produce an artifact.
Access Modality Situated (in a specific physical location, e.g. a live lecture held in a classroom), or Digital
Place The physical location name. If Modality is Digital, this field is ignored
Precondition on time Constraints on time that have to hold before the LO is taken.
Precondition on user profiles The skills and knowledge a learner must have in order to exploit the LO. It can also predicate
on administrative constraints that have to be fulfilled by the user before exploiting the LO
(e.g., he/she must have payed the enrollment fee.)
Learning objective on time The min/max amount of time required/allowed to complete the LO.
Learning objective on user Educational objectives of the LO in terms of skills a learner can obtain by exploiting it. It
profiles can also express objectives that are not strictly educational (e.g. the fact that the learner
achieves some kind of degree by completing the LO.)
Table 1: Examples of Virtual Campus LOM extensions to the IEEE LOM.
tent objects and [the server], and a common data model for Metadata 1 1 Learning Object (LO)
tracking a learners experience with content objects.” Assets
are defined as “the most basic form of a learning resource
(...). [In other terms, they] are an electronic representation
of media.” Assets can be grouped to produce other Assets.
A SCO is “a collection of one or more Assets that represent
Content 0..* 1..* Atomic LO (ALO) Complex LO (CLO)
a single launchable learning resource.” They represents “the
lowest level of granularity of a learning resource” that com-
municates with the RTE. Notice that, since Assets do not
interact with the RTE, they cannot be launched. Finally,
a Content Organization is a tree composed of so-called Ac- Figure 2: The Virtual Campus LO model.
tivity items which can be mapped on SCOs or Assets (see
Figure 1, extracted from the CAM specification.) All of
these components can be tagged with LOM metadata.
objects, with diverse re-usability properties and limitations.
Such a non-homogeneous model has an impact on the pos-
sibility of re-using LOs in different contexts.
In our approach we overcome such a problem by defining
an unique model for Learning Objects, allowing simple and
powerful recursive composition. In particular, as shown in
Figure 2, we define an Atomic LO (ALO) as a LO whose
instructional material is a file (we call it content) and a
Complex LO (CLO) as a LO whose instructional material is
an aggregation of Learning Objects. Being a LOs, a Com-
plex LO can be threated exactly as any other LO. Indeed,
it has associated a set of metadata, some of which can be
automatically derived from the metadata of the component
LOs (e.g., Size) and some others that need to be manually
inserted by the author of the Complex LO.
Figure 1: SCORM Content Organization.
All of the aforementioned components could be named as 4. SEQUENCING AND NAVIGATION
“Learning Object,” as they provide contents, optionally de- An important aspect of e-learning is to allow the Teacher
scribed by means of metadata. However, SCORM actually to define a path through the LOs that would guide the
do not provide a clear vision of what a Learning Object Learner in the way she/he takes the instructional material.
should be, since the model defines three diverse kinds of Such a path can be specified in term of rules that state, for
instance, the precedence relationships between LOs, the fact
Basic IsRequiredBy
that some LOs may be optional, etc. Concepts
Calculus
The SCORM Sequencing and Navigation (SN) book [4]
is focused on this issue and defines the required behaviors References
and functionality that the system must implement to pro- IsRequiredBy
cess sequencing information at run-time. More specifically, History of Geometry Exam
mathematics
it describes the branching and flow of Activities in terms of T
an Activity Tree, taking into account the Learners interac-
tions with LOs and a sequencing strategy. An Activity Tree IsRequiredBy Limits
Algerbra
represents the data structure that the system implements to
Mathematics
reflect the hierarchical, internal representation of the defined
Activities. Moreover, SN defines a Cluster as a specialized
form of a Activity that has sub-activities. IsRequiredBy
Relying on the aforementioned concepts, SCORM SN de- Mathematics Physics
fines several Sequencing Control Modes (e.g., Sequencing
Control Choice, Sequencing Control Choice Exit, Sequenc- IsAlternative To
Chemistry Chemistry
ing Control Forward Only), Sequencing Rules (a set of con- A B Engineering,
ditions that are evaluated in the context of the Activity
first year
for which the Sequencing Rule is defined), Limit Conditions
(conditions under which an Activity is not allowed to be
delivered), etc. Figure 3: CLO definitions.
In our opinion, SN specification is far too complex to be
effectively implemented. Moreover, the idea to separate con-
tent specification and sequencing specification, on one hand have to complete “Mathematics” before entering “Physics,”
makes the standard more flexible but, on the other hand, while “Chemistry A” (or alternatively “Chemistry B”) can
further complicates the implementation. be taken anytime with respect to the Mathematics and Physics
We propose the integration of both aggregation and se- pair.
quencing in a single specification that we call LO composi- It is interesting to note that “Mathematics”, being reused
tion. in this context, appears as a black-box. Its internal complex-
In our approach, Authors define each CLO in terms of a ity is hidden thus allowing for an easy composition. Even
graph where nodes univocally represent LOs (either Atomic more interesting is the fact that, in such a representation,
or Complex) while edges represent relationships between the less arcs are drawn, the more freedom is left to Learn-
LOs (see Figure 3). Rounded-corner rectangles inside a CLO ers. As an extreme example, a simple collection of LOs,
represent particular CLOs called Inner CLOs. They provide with no arcs at all, permits the design of a course in which
a mechanism to aggregate LOs, but, differently from other all possible paths are allowed.
CLOs, they do not have an identity and cannot be reused LOs can be (re)used either to define other LOs or to pro-
outside the context of the CLO in which they are defined. vide them to the learners. Before making them available
They can indeed participate in any relationship connecting to learners, LOs go through two more steps where all de-
two generic LOs. tails needed for enactment are provided. In the first step,
Relationships indicate the presence of instructional con- the Teacher can further constrain the fruition paths of a
straints between two LOs in the context of a containing CLO learning object. Such additional constraints are defined on
(outside that CLO, the relationship is no longer valid). A a workflow representation of a CLO that is automatically
generic relationship from x to y in the context of z, with x, y obtained from its original definition.
being LOs (either Atomic or Complex) and z a CLO (on In- For instance, Figure 4 shows the workflow representations
ner CLO), is represented by an arrow from x to y inside z, of the two CLOs defined in Figure 3. In this representation,
labeled with the relationship name. The relationships are LOs are mapped into activities that represent the fruition
named IsRequired, IsAlternativeTo, References, and IsRe- of the corresponding LOs. The syntax is similar to a UML
quiredOnFailure. Their meaning is summarized in Table 2, activity diagram. In particular, simple arrows connecting
where, for the sake of brevity, we omit the indication of the activities represent a sequence, vertical bars enclose paral-
CLO where the relationship takes place. lel activities, and diamonds are used to indicate alternative
The example shown in Figure 3 defines two different CLOs. activities. The stereotype <> denotes the fact
The first one, “Mathematics,” is composed of several LOs. that the corresponding path is not mandatory.
“Basic concepts” and “Algebra” are both required by the in- If needed, the Teacher can customize the automatically
ner CLO enclosing “Calculus”, “Geometry”, and “Limits”, derived workflows by performing any of the following ac-
so they should be taken in the first place. The “History of tions:
mathematics” is left as an optional activity and, in case it
1. elimination of alternative paths by selecting a single
is taken, it must follow “Basic concepts” that references to
path or a subset of the available ones;
it. “Exam” is a special kind of LO that we call Test-LO (it
is labeled with T). It is used to model assessments learners 2. elimination/forcing of optional activities;
have to go through. If they are failed the whole CLO have
to be repeated. 3. forcing the order of fruition in case of parallel activi-
The second CLO, “Engineering first year,” is composed re- ties.
using “Mathematics” as well as some other LOs. Learners
All these operations preserve the consistency between the
Relationship Description
IsRequiredBy A IsRequiredBy B indicates that LO A must be completed before starting LO B; i.e., the Learner
has to possess A-related knowledge in order to achieve a correct understanding of B. However, the
IsRequiredBy relationship does not mean that Learners must complete A immediately before B:
Learners are allowed to make use of other LOs after A and before B’s fruition.
IsAlternativeT o A IsAlternativeT o B indicates that A and B are mutually exclusive, although they are both
valid since their instructional function is considered to be identical. Two LOs connected by an
IsAlternativeT o relationship are automatically enclosed within an Inner CLO.
Ref erences A Ref erences B indicates that A cites B as a source of more details on a topic related to A itself.
Taking B at fruition time is not compulsory: Learners can thus decide whether to make use or to
ignore this information. Many Ref erences can enter or depart from the same LO. In this case,
Learners can make use of one or more of the corresponding LOs.
RequiresOnF ailure A RequiresOnF ailure relationship always connects a Test-LO with some other LO. If the Test-LO
is failed, then the LO at the other end of the RequiresOnF ailure relationship has to be taken by
the learner. If no RequiresOnF ailure is specified, learners failing a Test-LO have to re-start the
fruition of the whole CLO.
Table 2: Relationships between Reusable-Level LOs.
Basic concepts Calculus
[Fail] Reusable LO
<> editor Learner Teacher
Reusable
History of math. Geometry Exam LOs
Didactical-level PDF doc. PowerPoint doc. Java applet ...
[Pass] Complex LO
Author User Web Interface
generator &
Limits Didactical-
editor
Algebra Mathematics level Tutoring and Validation Instrumented Fruition
LOs Module (TVM) Engine (IFE)
Fruition-level
LO
tailoring tool Fruition- Workflow engine
Mathematics Physics level
Teacher LOs
Raw
Contents
Profile DB Fruition DB
data
Chemistry A
Authoring Fruition
Chemistry B
Engineering Environment Environment
Fruition
first year Enhanced-SCORM
Environment
package
adapter
Figure 4: Workflow description of CLOs.
Figure 6: Virtual Campus high-level architecture
Calculus
[Fail]
5. THE VIRTUAL CAMPUS PROJECT
Basic concepts Algebra Geometry Exam
Relying on the aforementioned concepts we developed Vir-
[Pass]
Limits
tual Campus, an e-learning platform for the design, deploy-
Mathematics ment, fruition, and evaluation of learning materials. As
for the design phase, its main objectives are to support
re-use and composition of LOs and to enable the defini-
Engineering
Chemistry A Mathematics Physics
first year
tion of the fruition flow for a given Complex LO. As for
the fruition phase, the main objective it to support vari-
ous learning modalities (individual or cooperative, distance
Figure 5: Customization of workflows.
or co-presence, etc.) and to provide some tutoring features
that help the Learner when needed.
The Virtual Campus platform is composed of two main
resulting workflow and the corresponding high-level descrip- subsystems (see Figure 6): The Authoring Environment,
tion since they further constrain the way LOs are used by and the Fruition Environment.
Learners. The Authoring Environment provides Teachers with a graph-
Figure 5 shows a possible customization of workflows de- ical editor (see [6]) to define ALOs and CLOs. Then, an
picted in Figure 4. automatic generator produces a first version of the work-
In the last refinement step for LOs, the Teacher trans- flow associated to a CLO and then supports Teachers in
forms a LO (usually a CLO) in such a way that it can be customizing it by means of a specialized workflow editor.
offered to Learners as a course. This is accomplished by Finally, a LO tailoring tool supports the insertion of all
specifying information needed to enact the LO, such as the fruition-related details. See Figure 7 and Figure 8.
course edition, the enrollment method, start and end dates, CLOs and Courses can be both serialized in a SCORM
the course calendar, announcements, the Teacher’s name, package in order to support export of data toward other e-
the list of already enrolled students, etc. At this point the learning platforms. Our extensions to the SCORM models
Course is ready for fruition and can be published. have been organized within a SCORM package in such a
Figure 9: The Fruition Environment showing a co-
operative LO
way that other SCORM compatible platforms would ignore
Figure 7: The CLO editor. them, but they would still be able to import the atomic LOs
belonging to the package.
The Fruition Environment is based on a RTE-compliant
engine (called IFE) that enables fruition of LOs by Learners.
IFE also supports the sequencing of CLOs (see [7] for de-
tails.) by exploiting a workflow engine that “executes” the
fruition workflow associated to a CLO, thus guiding Learn-
ers and Teachers in the execution of the activities related to
the usage of the LO. See Figure 9.
A tutoring module (called TVM, see [9]), starting from us-
age data, defines models for some aspects of LOs and Learn-
ers and, relying on them, provides Teachers with reports and
graphics about the performance of the Virtual Campus plat-
form and about learning behaviors of her/his students. In
the cases when Learners could choose among multiple paths
through LOs, TVM tries to provide them with suggestions
about the most appropriate instructional path to follow.
6. RELATED WORK
Our approach to improve re-usability is centered on sup-
porting LO composition. The language we propose is based
both on the usage of relationships at the higher level of ab-
straction, and on a workflow-like representation at a more
detailed level. In the following we present the approaches
we are aware of in the two areas.
6.1 Relationship-based systems
These systems allow teachers to define a course structure
by means of logic relationships among the course compo-
nents. MediBook [12] is an example of such systems. Medi-
Book is tailored to the medical domain; the important med-
ical concepts are formalized and related to each other by
Figure 8: The workflow editor.
semantic relationships. In turn, LOs are associated with
concepts and are connected through so-called rhetorical rela-
tionships (e.g. LO-A deepens LO-B, LO-C is-part-of LO-D).
MediBook uses the LOM standard to define LOs metadata
and to store rhetorical relationships. Learners can navigate
through both the rhetorical relationships structure or the
semantic relationships structure. In this last case, they dis-
cover LOs starting from the associated concepts. As a future work we plan to include the SQI specification
An alternative approach, described in [11], uses a sort of into Virtual Campus. We believe, in fact, that the combi-
“direct prerequisite” relationship to order LOs (e.g. LO-A is nation of an improved LO model and a standard interface
a direct prerequisite for LO-B). The matrix associated to the is the most promising answer to the interoperability issue.
resulting graph shows the total number of direct and indirect Another aspect that merits further investigation is the defi-
prerequisites between two LOs. When learners choose a LO nition of proper guidelines to support Authors and Teachers
to exploit, it is possible to calculate the list of required LOs. in the design of LOs. Clearly, the more their LOs correspond
An integer-programming model is then built, taking into to fine granularity learning materials, the more such materi-
account further constraints (e.g. the time effort required by als are reusable and applicable in various contexts. Indeed,
a given LO). By minimizing the model target function, some the mechanisms to compose fine granularity LOs are essen-
LOs are removed from the list. A sequencing procedure tial in this case in order to avoid all difficulties of having a
determining the “best” schedule on the remaining LOs is huge, non-organized collection of LOs.
then executed.
A similar approach, described in [5], uses the same re- 8. REFERENCES
lationship and adds weights in order to represent the diffi- [1] ADL. SCORM 2004 2nd edition - overview.
culty to access a given topic coming from a previous one. http://www.adlnet.org/, July 2004.
To choose a path, learners select it from the whole graph
[2] ADL. SCORM content aggregation model - version
provided by the system. Each route is associated with a nu-
1.3.1. http://www.adlnet.org/, July 2004.
meric index weighting the “effort to learn” the target topic.
[3] ADL. SCORM run-time environment - version 1.3.
6.2 Workflow-based systems http://www.adlnet.org/, January 2004.
These systems allow teachers to define a course struc- [4] ADL. SCORM sequencing and navigation - version
ture as a workflow. Flex-eL [8] is an example of such sys- 1.3.1. http://www.adlnet.org/, July 2004.
tems. Flex-eL provides a process-modeling tool to capture [5] V. Carchiolo, A. Longheu, and M. Malgeri. Learning
the learning process and view it as a stream of activities (a through ad-hoc formative paths. In Proceedings of the
so-called “process template”.) International Conference on Advanced Learning
It is also possible to have more than one process tem- Technologies (ICALT), Madison, Wisconsin, USA,
plate for the same course. Whenever a student enrolls in 2001.
a course, a new instance of the learning process is created [6] M. Cesarini, S. Guinea, L. Sbattella, and R. Tedesco.
by the system. Rather than making all the course material Innovative learning and teaching scenarios in virtual
and activities available to the student at the beginning of the campus. In Proceedings of World Conference on
course, Flex-eL coordinates their availability and completion Educational Multimedia, Hypermedia and
by utilizing its embedded workflow functionality. When the Telecommunications (ED-MEDIA), Lugano,
appropriate learning activity is completed, a new activity is Switzerland, June 2004.
assigned to the work list of the associated person. [7] M. Cesarini, M. Monga, and R. Tedesco. Carrying on
the elearning process with a workflow management
While each of the aforementioned systems has some sim- engine. In ACM Symposium on Applied Computing
ilarity to our approach, none of them exploits LOs, and in (SAC), Nicosia, Cyprus, march 2004.
particular CLOs, as a unit of reuse. Moreover, they are [8] j. Lin, C. Ho, W. Sadiq, and M. E. Orlowska. On
not integrated with SCORM and do not try to exploit both workflow enabled e-learning services. In Proceedings of
relationships and workflows in a unified authoring cycle. the International Conference on Advanced Learning
Technologies (ICALT), Madison, Wisconsin, USA,
7. CONCLUSIONS 2001.
We see SCORM as a good opportunity to support inter- [9] L. Sbattella and R. Tedesco. Profiling and tutoring
operability among e-learning tools since it enables the def- users in virtual campus. In 5th International
inition of a data model that can be shared among them. Conference on Information Technology Based Higher
However, we have noticed some weaknesses in such a data Education and Training (ITHET ’04, Istanbul,
model. These weaknesses mainly concern the way LOs can Turkey, May–June 2004.
be structured and made available for reuse. [10] B. Simon, D. Massart, and E. Duval. Simple query
In our vision all the learning resources have to be thought interface specification. http://nm.wu-wien.ac.at/
as LOs, so that they are described by proper metadata and e-learning/interoperability/query.pdf, July
can be recursively composed. Thanks to the recursive com- 2004.
position mechanisms, reuse both within a single platform [11] A. Steinacker, A. Faatz, C. Seeberg, I. Rimac,
and among platforms can be greatly enhanced: A LO at any S. Hrmann, A. E. Saddik, and R. Steinmetz. Decision
level of composition can be re-used and composed in another support models for composing and navigating through
context. The definition of proper metadata can support not e-learning objects. In Proceedings of the 36th Hawaii
only browsing and re-use of LOs, but also installation and International Conference on System Sciences
execution of them. (HICSS), Big Island, Hawaii, 2002.
The Virtual Campus project aims at providing an im- [12] A. Steinacker, A. Faatz, C. Seeberg, I. Rimac,
plementation of the aforementioned concepts. Moreover, it S. Hrmann1, A. E. Saddik, and R. Steinmetz.
tries to enhance the SCORM run-time environment, exploit- Medibook: Combining semantic networks with
ing a workflow engine to guide Learners through the instruc- metadata for learning resources to build a web based
tional paths. learning system. In Proceedings of the World
Conference on Educationnal Multimedia, Hypermedia
and Telecommunication (ED-MEDIA), Tampere,
Finland, 2001.
[13] W3C. Composite Capability/Preference Profiles
(CC/PP): Structure and vocabularies 1.0.
http://www.w3.org/TR/2004/
REC-CCPP-struct-vocab-20040115/, January 2004.