E-learning systems are often based on the notion of "course": an interconnected set of resources aiming at presenting material related to a particular topic. Course authors do provide external links to related material. Such external links are however "frozen" at the time of publication of the course. Metadata are useful for classifying and finding e-learning artifacts. In many cases, metadata are used by Learning Management Systems to import, export, sequence and present learning objects. The use of metadata by humans is in general limited to a search functionality, e.g. by authors who search for material that can be reused. We argue that metadata can be used to enrich the interconnection among courses, and to present to the student a richer variety of interconnected resources. We implemented a system that presents an instance of this idea

Giuliani, Alberto

Ronchetti, Marco

Saini, Paramjeet

English

University of Trento : UNITN-eprints

  
 
 
 
 
 
UNIVERSITY 
OF TRENTO 
 DEPARTMENT OF INFORMATION AND COMMUNICATION TECHNOLOGY 
  
38050 Povo – Trento (Italy), Via Sommarive 14 
http://www.dit.unitn.it 
 
 
 
 
 
 
 
 
 
 
 
 
 
DE-FRAGMENTING KNOWLEDGE: 
USING METADATA FOR INTERCONNECTING COURSES  
 
 
Marco Ronchetti, Alberto Giuliani  
and Paramjeet Saini 
  
 
 
 
 
May 2003 
 
Technical Report # DIT-03-019 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
. 
DE-FRAGMENTING KNOWLEDGE: USING METADATA FOR 
INTERCONNECTING COURSES  
 
Marco Ronchetti, Alberto Giuliani and Paramjeet Saini 
Dipartimento di Informatica e Telecomunicazioni 
Università degli Studi di Trento,  38050 Povo (Trento) 
Italy  
 
 
ABSTRACT 
 
E-learning systems are often based on the notion of 
“course”: an interconnected set of resources aiming at 
presenting material related to a particular topic. Course 
authors do provide external links to related material. Such 
external links are however “frozen” at the time of 
publication of the course. 
Metadata are useful for classifying and finding e-learning 
artifacts. In many cases, metadata are used by Learning 
Management Systems to import, export, sequence and 
present learning objects. The use of metadata by humans 
is in general limited to a search functionality, e.g. by 
authors who search for material that can be reused.  
We argue that metadata can be used to enrich the 
interconnection among courses, and to present to the 
student a richer variety of interconnected resources. We 
implemented a system that presents an instance of this 
idea. 
 
KEY WORDS 
Metadata, Knowledge Management 
 
 
1. INTRODUCTION 
 
E-learning systems are often based on the notion of 
“course”: an interconnected set of resources aiming at 
presenting material related to a particular topic. 
Frequently courses become “islands”, and the e-learning 
environment becomes an archipelago of disconnected 
entities. Course authors do provide some form of  
“external interconnections”, e.g. links to related material. 
Both internal and external links are “frozen” at the time of 
publication of the course: however the external world 
changes continuously and there might be new related 
resources that remain unknown to the static world of the 
course. Of course they can be updated every now and 
then, but this requires that the author is aware of what 
related material is available. Therefore an e-learning 
platform ends up being a container of these knowledge 
fragments, which are weakly interlinked. The main 
structural link often remains the fact that courses belong 
to a predefined path that is the student’s curriculum. Links 
between similar courses belonging to different curricula 
in the same university are however often completely 
missing, even when the covered topics are similar. This is 
probably more likely to happen in university systems 
strongly based on Schools (like in Italy) rather than 
primarily the on Departments (like in USA). 
 
A tool for allowing authors to find related material exists: 
metadata. In recent years several standards have come out 
to define a common convention for metadata definition 
and representation (IEEE LTSC P1484.12, AICC AGR, 
ADL SCORM, Dublin Core Metadata Initiative etc.). 
Some metadata are “container oriented”, i.e. they are 
meant to inform the learning management system about 
format, sequencing etc. of the material. These metadata 
are objective, and therefore rather easy to define and use, 
but do not solve our problem. Other metadata are meant 
to facilitate the use of the material by teachers, by 
curriculum designers and by producers. Among these, 
there are data description about the type of granularity of 
objects, pedagogical purpose, assessments and learning 
objectives, etc.: all of these metadata represent subjective 
interpretations of resources, leading to serious problem in 
the definition of an agreed upon value. An interesting 
point of view is expressed by Nilsson et al. [1,2]: they 
argue that the image of meta-data as being objective 
information about data is wrong, or at least incomplete. 
They support the existence of multiple, even conflicting 
descriptions, and maintain that the RDF technology allow 
to implement and use such subjective metadata. 
 
We are interested here in metadata that describe the 
content i.e. that are able to convey the notion of “what the 
learning object is about”. Such metadata present both the 
problem of objectivity, and the problem of describing 
them using a shared language (a common ontology). We 
discuss this last point in another paper [3], and assume 
here that this problem is solved: i.e. that there is a 
common language for describing the argument that a 
learning object is about. We will also assume in the 
following a given granularity for a learning object: for us 
it will be a self-contained portion that explains one or 
more concepts. With such choice of granularity, an image 
by itself will not be considered a significant learning 
object. 
 
In such scenario, it would be possible to solve the 
knowledge fragmentation problem: authors would be able 
to search for material that is related to the topic they 
discuss, and they could put references that interlink their 
courses with related ones. 
The problem is that this author-driven process is costly, 
and that the result is static. In the present paper we present 
an approach that allows automating the process, and that 
produces a result that remains up to date in time.     
 
 
 
2. USING  METADATA FOR AUTOMATIC 
GENERATION OF KNOWLEDGE 
INTERLINKING 
 
We outline here our idea. The main assumption is that 
we have an ontology (that at minimum should be a 
taxonomy), and that it is possible to use it to index 
lectures or lecture fragments. Whenever a student requests 
a page containing a lecture fragment, the page is 
automatically decorated by adding (e.g. at the end) an 
active point (e button, or a link) that connects to another 
page that is generated on the fly. The process that 
generates the new page goes through the following steps: 
1. look up the indexes, and retrieve the content 
description metadata M, i.e. the particular metadata 
that describe the content of the original page in terms 
of the ontology; 
2. look for all the other Learning Objects that have the 
same value of the metadata M, and build a list of 
them 
3. explore ontological space by traversing the relations 
that link the content description metadata M  with a 
set of other content description metadata {M1…Mk} 
4. for each of the values{M1…Mk} look for all the other 
Learning Objects that have the same value for the 
content description metadata, and build a list of them. 
5. if requested, iterate though points 3 and 4. 
6. summarize the information that has been found and 
present it to the requestor. 
 
In other terms, what happens is the following: 
Step 1 finds out what is the topic T of the page.  
Step 2 finds all the pages that are known to the system, 
that are about the same topic T. 
Step 3 finds the set of topics {T1…Tk} that are related to 
the original topic. 
Step 4 finds all the pages that are about each of the related 
topic {T1…Tk} 
Step 6 presents the result. The resulting page contain a lis t 
of URLs (links) ordered by relation. 
 
Since the resulting page is generated on the fly, it is 
always u to date: as soon as a new page is added to the 
system and it is labeled with suitable metadata, it becomes 
visible as “related topic”. The process in not obtrusive 
from the user point of view, because it is only triggered 
on demand (i.e. when the user presses the button that 
might say something like “show me related material”. 
 
The nature of the relations that are traversed depends on 
the ontology. At the minimum the ontology should 
classify material as belonging to a certain topic, and 
present relations among topics (e.g. taxonomical 
relations), but certainly it would be preferable to have a 
richer ontology that contains other relations (like for 
instance “is used by”, “requires knowledge of” etc.). In 
the case of such a richer ontology, the system could offer 
to the user the option of selecting one particular kind of 
relation. 
 
Moreover, the production of the final page could be 
filtered according to some user’s choices. For instance, 
the user could ask to see only the pages that belong to the 
same university, or that are produced by a certain author, 
or that were added to the system after a certain date. 
 
Finally, the system could include the possibility of 
explicitly navigating the ontology by using a “concept 
map navigator”-like tool. We would expect that such kind 
of navigation would not be useful for students, being 
probably too abstract, but that it might be useful for 
teachers and authors for exploring the whole learning 
management system space.  
 
 
3. THE ARCHITECTURE 
 
We implemented a prototype [4] that allows exploring 
most of the ideas outlined in the previous section.  
A keystone of the system is the chosen ontology. We 
focused on a particular domain: the subset of Computer 
Science that is thought during undergraduate studies. We 
used the work that some of us had previously done, i.e. 
the definition of ontology for such domain [3]. Our 
ontology was extracted from the huge work performed by 
a task force created by the Association for Computing 
Machinery (ACM) and described in the “ACM 
Computing Curricula 2001” [5]. Details about the reason 
why we chose the definition of suggested Computer 
Science curricula as our starting point are given elsewhere 
[3]. Here we only say that the resulting ontology is at 
present a taxonomy based on Areas, Units and Topics. 
The only relation is therefore at present a “part-of”, but 
we plan to work to enrich the taxonomy by adding other 
types of relations among topics and among units. For our 
goal, that is for showing a prototype that implements our 
ideas, such ontology is sufficient. Also, it turns out to be 
very useful in practice (we are currently using it for 
indexing all the on-line material in the area of Computer 
Science at our university). 
The key architectural ingredients for our prototype 
are: 
?? The LMS: a standard learning management system 
that provides access to learning resources (in the 
simplest case a simple WebServer). It acts as 
Learning Object Storage 
?? The MD-Storage: a database for collecting metadata; 
?? The Engine: a collection of JSP pages that interrogate 
the MD-Storage to dynamically creates the pages for 
presenting the related topics 
?? The Broker: a dynamic web server that interacts with 
the MD-Storage 
?? The Navigator: an applet that allows to directly 
navigate the ontology. 
 
The user has two possible starting points. The simplest is 
the Navigator. The user can navigate the applet passing 
through areas, units and topics. For each topic (the lowest 
hierarchical level in the taxonomy) the Navigator can ask 
the Engine to retrieve from the MD-Storage a list of the 
Learning Objects that are related to it. This list is then 
formatted by the Engine, that produces the final view: a 
(possibly filtered) collection of Learning objects related to 
the chosen topic.  
We implemented two versions of the Navigator: one 
presents a taxonomical view (well suited to the present 
stat of the ontology) and the second allows navigating in 
the style of concept maps. This second option is better 
suited to a richer ontology, which exhibits the structure of 
a graph structure rather than that of a tree. Figure 1 shows 
a snapshot of this second option. 
  
 
 
Figure 1: Navigating metadata in the ontological space with our tool. 
 
The other possible starting point for the user is a common 
page served by the LMS. In our architecture, the page is 
never directly requested to the LMS, but is requested to 
the Broker. The Broker retrieves the page from the LMS, 
and decorates the page with an active element: a button or 
a link that the user can exploit to talk back to the Broker 
for requesting additional information. When the user 
decides to exploit this possibility, the Broker activates the 
Engine, which in turn starts a process that interrogates the 
MD-Storage following the sequence of steps 1 to 6 
described in section 2. At the end of the process the 
Engine is able to present (in an additional Browser 
window) the list of URLs representing the related learning 
objects (e.g. lectures or lecture fragments) that the user 
expects. Each of these URLs interacts with the Broker, 
starting a new iteration of the process that we described 
here.   
 
Such system allows using the LMS in a direct and 
traditional way, without the benefit of the knowledge 
extracted from the metadata, or through the Broker that 
enables the whole process we described. 
 
We should also say that the page with the referenced 
material also presents a link to the Applet, so that the user 
can at any time switch between the explicit metadata 
navigation allowed by the Applet and the implicit one 
provided by the Broker. 
 
We must also add that having a single LMS is not a 
requirement: our architecture can rely on several 
heterogeneous LMS’s, and it is able to interconnect 
learning material regardless its location. All that is 
requested is that the resources metadata are defined, i.e.  
that the MD-Storage has a notion of the available learning 
objects and knows their location.  
 
A possible extension that we are presently considering 
is to have a distributed MD-Storage, possibly based on 
peer-to-peer technology. The only requirement is that the 
ontology be shared. Even such requirement could be 
released by introducing the notion of a Mapper, which 
interrelates different ontologies. 
 
At present we implemented the architecture we 
described and used the system for indexing approximately 
1000 learning objects. Considering that our ontology 
consists of approximately 1000 topics, this means that at 
present we have some sufficiently well covered areas, 
while other areas are not covered by learning objects (or 
they are not yet described by the content description 
metadata. Our plan is to progress in parallel to the project 
of putting on line the entire curriculum of CS at our 
university. In the meantime, we intend to use our 
prototype to study its acceptance and practical usefulness 
for our students. 
 
The present work is somehow similar, in aim, to 
initiatives connected with applications of the Semantic 
Web to e-learning (see e.g. [6]). Our approach however 
does not rely on the typical Semantic Web technology, 
and its implementation is available today. Moreover, 
often the use of metadata is aimed at helping author 
discovering resources for reusing them as material for 
producing new  lectures (see e.g. [7]). Our approach is 
rather to aid students to find material that presents 
different points of view or different ways to explain thing. 
 
 
3. CONCLUSION 
 
We described a way to automatically generate glue 
that interconnects “knowledge fragments”. We started 
from the consideration that frequently in on-line learning 
initiatives the focus is on providing self-contained 
courses, which often have little or no relation with other 
courses. Even when the relation is present, it is typically 
static; that means than new material that is added to the 
repository can relate to existing material while the reverse 
is not true. Also, finding and implementing such relations 
requires human interventions and is time expensive. 
Therefore we designed a system that can discover 
relations and exposes them, provided that metadata 
describing the content of the learning objects exist and 
that they are defined according to an agreed-upon 
ontology. 
We described the logical architecture of the system, 
and presented an implementation that indexes lectures on 
Computer Science at our university, according to an 
ontology that we describe in detail elsewhere. 
Our system can be interfaced with any learning object 
repository, even distributed multiple ones. At present our 
system relies on a centralized metadata repository, but we 
suggested some possible extensions in the direction of 
peer-to-peer distributed architecture. 
 
REFERENCES 
 
[1] Nilsson, Mikael,  Palmér Matthias and Naeve 
Ambjörn, Semantic Web Metadata for e-Learning - Some 
Architectural Guidelines, WWW2002 International 
Conference 
[2] Naeve, Ambjörn, Nilsson, Mikael,  Palmér Matthias, 
E-learning in the Semantic Age, Proceedings of the 2nd 
European Web-based Learning Environments Conference 
(WBLE 2001), Lund, Sweden 201  
[3] Ronchetti, M., Saini, P., Ontology-based metadata for 
e-learning in the Computer Science domain, to be 
published. 
[5] Giuliani, A., Tesi di Laurea in Informatica, Universita' 
di Trento, 2003 
[5] Computing Curricula 2001 Computer Science, ACM 
Journal of Educational Resources in Computing, 1(3):1-
240, Fall 2001.  
[6] Stojanovic, L. Staab S., Studer R. E-learning based on 
the Semantic Web In WebNet2001 - World Conference 
on the WWW and Internet, Orlando, Florida, USA, 2001 
[7] Lytras M.D., Semantics for E-learning: An advanced 
Knowledge Management oriented metadata schema for 
leaning purposes, Procedings of the E-Learn 2002, 
Montreal, Canada, 2002 
 


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