Use of Contextualized Attention Metadata for Ranking and
Recommending Learning Objects
Xavier Ochoa
Escuela Superior Politécnica del Litoral
Campus “GustavoGalindo”
Guayaquil, Ecuador
+59342269773
xavier@cti.espol.edu.ec
Erik Duval
Computerwetenschappen Dept., KULeuven,
Celestijnenlaan 200 A,
B-3001 Leuven, Belgium
+3216327066
Erik.Duval@cs.kuleuven.be
ABSTRACT
The tools used to search and find Learning Objects in different
systems do not provide a meaningful and scalable way to rank or
recommend learning material. This work propose and detail the
use of Contextual Attention Metadata, gathered from the different
tools used in the lifecycle of the Learning Object, to create
ranking and recommending metrics to improve the user
experience. Four types of metrics are detailed: Link Analysis
Ranking, Similarity Recommendation, Personalized Ranking and
Contextual Recommendation. While designed for Learning
Objects, it is shown that these metrics could also be applied to
rank and recommend other types of reusable components like
software libraries.
Categories and Subject Descriptors
H.3.3 [Information Storage and Retrieval]: Information Search
and Retrieval – information filtering, relevance feedback.
General Terms
Algorithms, Measurement, Design, Human Factors,
Standardization
Keywords
Learning Objects, Ranking, Recommending, Attention Metadata
1. INTRODUCTION
One of the main reasons to capture and analyze the information
about the interaction between a user and a tool is to improve the
user experience. For example, a online library system could
record the subject of the books that a client has bought before in
order to recommend him new books about a similar subject the
next time he/she logs in, saving him/her the hassle to search for
them [1]. A news web site could record the topic of the news
articles that a user normally read in order to filter out news that do
not interest such user [2]. A collaborative browser could use the
information recollected from the browsing patterns of a given
community to improve the rank of different pages on the searches
of an individual user, member of that community [3]. The
generic name of Attention Metadata[4] has been applied to
describe the information about these interactions.
When the information stored does not only contain the reference
to the user and the action that it performs, but also register when
the action took place, through which tool the action was
performed, what others thing was doing the user at the same time,
what is the profile of the user performing the action, to what
community he/she belongs, etc, it leads to an improved and more
useful form of record, called Contextualized Attention Metadata
[5] (CAM). AttentionXML [6] and its extensions [5] are an effort
to standardize the way in which CAM is stored. This
standardization will lead to the opportunity to share attention
records between different applications. For example, a second
generation of an Attention-Sharing online library could know
which news topics the user is interested in and it could
recommend him/her books related to those topics.
The authors believe that one group of applications that could
greatly benefit from CAM information is the search and find of
Learning Objects. These applications have suffered from an
under-par performance compared to similar applications in other
fields [7] [8]. The main reason for this is the lack of a meaningful
and scalable way to rank or recommend the objects to the users.
Currently, two main methods are used to rank (not even
recommend) Learning Objects: Manual Rating or Metadata
Content Rating. In the first approach, Manual Rating, each
Learning Objects should be rated by a group of experts and/or the
user community. For each search, the returned objects are ranked
based on their average rate. While this is bound to provide
meaningful ordering, it does not scale. For example MERLOT
use this approach, but only 10% of the total content of the
database has ever be rated [9]. The other approach, use only the
information contained in the metadata record to perform ranking
based on the similarity with the query terms. The most common
method used for this is the TFIDF metric[10] that measure in a
Vector Space the distance between the query vector and the vector
composed from the text contained in the metadata record. Given
than TFIDF was designed to work over full text documents and
that metadata records contain very few textual descriptions [11],
normally the ordering is not meaningful for the user. SILO
(Search and Indexing Learning Objects) tools from the ARIADNE
[12] repository use this approach. CAM could be used to
generate a third approach, one in which the human attention
(meaningful) is processed to construct an automated (scalable)
rating and recommending procedure.
Permission to make digital or hard copies of all or part of this work for
personal or classroom use is granted without fee provided that copies are
not made or distributed for profit or commercial advantage and that
copies bear this notice and the full citation on the first page. To copy
otherwise, or republish, to post on servers or to redistribute to lists,
requires prior specific permission and/or a fee.
CAMA’06, November 11, 2006, Arlington, Virginia, USA.
Copyright 2006 ACM 1-59593-524-X/06/0011...$5.00.

The following sections of this work describe in detail what
information should be stored in the CAM record of Learning
Object Applications (Section 2) and the mechanisms by which
such information could be used to generate rating and
recommending metrics (Section 3). It is also discussed how these
mechanisms and metrics could be applied to related contexts
(Section 4) and which research questions need to be addressed in
further work (Section 5). The work finalize with an overview of
related research (Section 6)
2. CAM FORLEARNING OBJECTS
APPLICATIONS
Users interact with a Learning Object through the object’s whole
lifecycle. CAM recorders capture and timestamp all those
interactions in order to provide the information needed to
calculate useful metrics to be used in a next generation breed of
learning object management tools. According to the
AttentionXML extension proposed by Najjar et al at [5], these
interactions are stored inside an Action record. This work will
briefly list the different Actions that should be recorded through
the Learning Object lifecycle. The lifecycle phases are taken from
the enumeration done by Collins and Strijker in [13]. Also, it is
suggested which applications should generate the attention
records.
Creation: In this phase the author creates or assembles the
learning object in its digital form using some sort of authoring
tool. The Creating Action should be captured and it must include
the identity of the created object, its author(s), the authoring tool
used and the list of component-objects [14] reused through the
creation process. This record should be created by the authoring
tool, for example Microsoft Power Point.
Labeling: At this stage the author, an indexer or even an
automated system could add a metadata record that describes the
Learning Object. The Labeling Action must include information
that identify the object, the labeler, the origin of the metadata
(Automated, Semi-Automated, Manual), the metadata format
used, the level of confidence of the information (how sure the
autor is that metadata values are correct) and a unique identifier
for the metadata record. Normally this record should be also
created by the authoring tool at the end of the creation of the
objects, but could also be created by metadata editors as [15] or
automated metadata generators as [16].
Offering: At this stage the author or indexer inserts the object in
a repository or other system that allow the object to be shared
with others. The Inserting Action must include the following
information: Object Unique Identifier, Inserter, Tool Used and
Learning Object Unique Identifier inside the sharing tool. This
record should be created by the sharing tool, being it a Learning
Object Repository or a Peer to Peer sharing application.
Selecting: In this stage the user search, find and select Learning
Objects in the Sharing System. Several Actions should be
captured during this phase. A Searching Action when a query is
performed to find relevant objects. It must include information
that describe the query performed and the objects returned. A
Recommending Action when the system suggests relevant objects
without the user performing a query. It must contain information
a list of the object(s) recommended, the user action that trigger the
recommendation and the tool used to perform the
recommendation. A Browsing Action when the user reviews the
metadata or description of an object. It must store information
that identifies the metadata record browsed and the time expend in
the review. Finally, A Selecting Action when the user chooses an
object by downloading it or accepting the recommendation. It
must contain information that identifies the selected object. All
this actions should also contain information about the user that
performs the action. These records should be generated by the
sharing or recommending tool.
Using: This stage comprehends all the actions that the final user
(instructor or learner) performs with the learning object during its
normal utilization in a learning environment. There are several
actions to be registered. A Publicating Action when the instructor
inserts the object into a Course belonging to some kind of
Learning Management System. It must contain information that
identifies the published object and the context (course, lesson)
where it was published. A Sequencing Action when one or more
objects are included in an instructional design or sequenced
package. It must contain information about the identification (in
an ordered form) of the integrated objects. A ViewingAction, the
object is read or viewed by learners. It must contain information
about the time spent reviewing the material. An Annotating
Action when the instructor or the learner add a comment or rate
the learning object. It must include information about the
comment or the rate given and the identifier of the object. All
these action should also store information about the user that
performs them. Different tools should be in charge of the
generation of the attention records, a LMS for the Publishing
Action, a Learning Activity Management System [17] or SCORM
[18] Packager for the Sequencing Action, a Web browser or
document reader for the Viewing Action and a Rating or Review
system for the Annotating Action.
Lifecycle Actions Main Information Source
Creation Creating author, components Authoring tool,Components
Labeling Labeling
metadata format,
origin, confidence
Authoring tool
or Metadata
generator
Offering Inserting
inserter LOR or
Sharing app.
Searching
query, results LOR’s search
tool
Recommending objects recommended Recommender
Browsing Time LOR orRecommender
Selecting
Selecting
object identifier LOR or
Recommender
Publicating LMS context LMS
Sequencing
list of sequenced
objects
ID tool or
Packager
Viewing Time, tool used Browser orReading app.
Using
Annotating rate or review LMS
Retaining Retaining
decision to keep or
delete
LMS
Table 1. Proposed CAM information to be stored for Learning
Object Applications

Retaining: In this phase, the instructor check for the validity of
the learning object and decides if it is still useful or if it should be
replaced / updated. The Retaining Action should contain
information that identifies the object and the decision taken (keep,
update, delete). This attention record will normally be generated
by the LMS where the object has been published.
A summary of the Actions that CAM should record is presented in
Table 1. Some of these CAM Actions (Creating, Inserting,
Selecting, Viewing) are already produced and stored in different
tools [5]. The others are easy to implement in existing tools
taking in account that most of them (LMS, Metadata Generators,
etc) already produce a log with the user’s interactions. In the next
section, metrics to exploit this Action records to improve tools to
search and find Learning Objects are proposed.
3. RANKING AND RECOMMENDING
METRICS USING CAM
Several ranking and recommending metrics will be proposed.
These metrics will use only two sources of information to be
calculated: the first one is the Learning Object Metadata (LOM)
[19] record that describe each Learning Object; the second one is
the CAM Actions described in the previous section.
3.1 Link Analysis Based Ranking
One of the most famous and successful ranking algorithms at the
present is PageRank [20]. PageRank use the information
contained in the network of links between web pages to calculate
the relative “importance” of a page. It could be summarized as: a
page is important if it is linked by a high number of pages. Also,
the importance increases if the pages linking to it have also a high
importance rank. Unfortunately, this algorithm could not be
applied directly to Learning Objects. While LOM records have a
linking field, it is rarely populated [11]. Also, LOM linking
reflect just a semantic relationship; it does not imply a “vote” for
that object as it is assumed for Web pages.
As an alternative to the explicit linking structure that the web
posses, CAM allow us to create an implicit linking between
Learning Objects and other entities related to them: Authors,
Users, Courses, Learners, etc. For example: Creating Actions can
be converted into a link between an author and an object,
Selecting Actions can be converted into a link between a user and
an object, Publicating Actions can be converted into a link
between a course and an object and also between a user and the
same object. Viewing Actions can be converter into a link
between a learner and an object. As result of this conversion of
CAM to links between different entities, a K-partite graph is
created (a graph with different partitions, where there are not links
between nodes of the same partition). In this graph each type of
entity (Learning Object, User, Course, and Learner) is considered
a partition Figure 1 present diagram of an example of such a
graph.
Once CAM information is represented as a graph, it is easy to use
basic graph algorithms to calculate ranking metrics. Following
there are some metrics that could be developed this way:
x Popularity Rank (PR): Using the information contained in
the Selecting Action (converted already in a 2-partite graph),
it is easy to obtain the number of times that an object has
been downloaded. To calculate just count the number of
incident links that each Learning Object node has from nodes
in the User Partition. This metric is a just a basic way to put
most downloaded objects first in the result list.
)()( objectinDegreeobjectPR
Figure 1. K-Partite Graph representation of CAM
x Author-Corrected Popularity Rank (ACP): Combining the
Creating and Selecting Actions, it could be calculated how
popular an object is based on the number of downloads and
the popularity of the Author. The first step is to create a 3-
partite graph with Users, Objects and Author partitions. Then
the PopularityRank (PC) is calculated for all the objects.
Next, the Author Popularity (AP) is calculated adding the PC
of all the Learning Objects nodes that are linked to the author
node. Finally, the AP is multiplied by a weighting factor and
added to the also weighted PC. This metrics enable new
objects (that do not have any downloads) from a well
downloaded author, appear higher in the list.
¦
i
iobjectPRauthorAP )()( ; if objecti is linked to author
)()()( authorAPobjectPRobjectACP ˜˜ ED
x Weighted Popularity (WP): Selecting, Publicating and
Retaining Actions can be combined to generate a 2-partite
graph between Users and Learning Objects. The links of this
graph will be weighted: if the link is made using the
Retaining information (inDegreeR) it will have more weight
as if the link was made using the Publication information
(inDegreeP). In the same way, Publication links will weight
more than Selection links (inDegreeS). The rationale behind
this metric is that different actions mean different level of
“preference” for an object. If the instructor has use the
object and she is happy with it to keep it for the next
semester is a stronger vote of support than just using it for
the first time or that just downloading it. That difference of
importance is represented in the weight given to each kind of
link.
)()(
)()(
objectinDegreeobjectinDegree
objectinDegreeobjectWP
RP
S
xx
x
JE
D
with J > E > D
O1
O2
O3
C1
C2
U1 U2
A1
A2
User Partition
Course Partition
Author Partition
Object Partition

x Rate of Reuse Rank (RRR): Using the Selecting Action (or
also the Publicating and Retaining Actions as in the previous
metric), the number of times that an object has been
downloaded during a given period of time P (last week,
month, year) can be calculated. The 2-partite graph (Users
and Objects partitions) can be constructed but only taking in
account the Actions occurred in a given period of time. For
example: if the last week is selected as P, This rank will
calculate how often the object has been downloaded (inserted
or retained) on the last 7 days. This value could be
normalized using the age of the object, obtained from the
related Creation action. This metric will help to rank higher
object that have been reused frequently and are relatively
new.
)(
)()(
objectage
objectinDegree
objectRRR ; for links inside period P
x Manual Rank (MR): Using the information that is stored in
the Annotation Action, the number of times that an object
has been positively (or negatively) rated or reviewed could
be considered to calculate a metric. A 2-partite graph (Users
and Objects partitions) is created. The procedure will weight
the link as 1 if it is a positive rate or review, -1 if it is a
negative rate or review. The actual value of the rate is only
used to evaluate if the rate is a positive or negative “vote”,
because different users and system have different scales to
grade. The reviews can only be considered if their
positiveness or negativeness value is included in the
Annotation Action or could be automatically inferred from
the text.
)()()( objectinDegreeobjectinDegreeobjectMR NegativePositive 
These metrics can be calculated off-line because they are not user
or query specific. They calculate an average importance or
relevance of the learning objects based in the agglutination of
attention information. These metrics, and others that can be
developed afterwards, could be integrated in a final ranking
metric. This Compound Popularity Metric (CP) can be calculated
as the weighted sum of the values of the individual metrics. For
example, Google integrates more than 100 of different simple
metrics in order to provide its results [21].
MRRRRWPACPPRCP HGJED 
The weighted coefficients (D, E, etc) should be estimated (not
trivial procedure) to provide an optimal result ordering. Methods
to make these estimates are described in [22] and [23]. Also,
manual rates should be used carefully because the Annotation
Information is optional and could not exist for all the objects
involved in the calculation.
3.2 Similarity Metrics for Recommendation
One property of a 2-partite graph is that it can be folded over one
of its partitions, generating a normal graph with just one entity
and links between its nodes. For example if we have a 2-partite
graph of Users that have download Learning Objects, we can fold
over the Learning Object partition and we will end up with a
graph where the Users are linked between them. Each link mean
that those two users have download the same object at least once.
This new graph could be used to calculate similarity between the
users based on the download patterns. In Figure 2 we can see a
representation of the folding result. The first part of the figure
represents a 2-partite graph with the User and Objects partitions.
The graph shows that, for example, that User 5 had downloaded
Object 2 and Object 3 and User 1 had only downloaded Object 1.
The second part of the figure illustrates the folded version of the
graph. In this new graph, the users have a link between them if
they linked to the same object in the unfolded graph. The more
objects the users have in common, the thicker the line. For
example User 1 and User 4 are linked because they both have
downloaded Object 1. User 2 and User 5 have a stronger links
because they both have downloaded Object 2 and Object 3. This
technique is similar to the one applied in scientometrics to obtain
relations between different authors, based on the papers that have
co-authored[24].
Figure 2. Unfolded and Folded 2-Partite Graph
We present several similarity metrics that can be calculated using
the information contained in CAM Actions detailed in the
previous section.
x Object Similarity based on Number of Downloads: Create
a 2-partite graph with the information of the Select Actions
(when a User download a Learning Object), and fold over the
User Partition. A link between two Objects in the final graph
means that those objects have been downloaded by the same
user. The strength of the similarity is number of users that
have downloaded both objects.
x Object Similarity based on Re-Use: Create a 2-partite
graph with the information from the Publish Actions (when a
Learning Object is inserted into a Course), and fold over the
Course Partition. A link between two Objects in the final
graph means that two objects have been inserted in the same
course. The strength of the similarity is number of courses
that include both objects.
x Users similarity based on Downloads: Create a 2-partite
graph with the information from the Select Actions, and fold
over the Object Partition. A link between two Users means
that they have downloaded the same object. The strength of
the similarity is the number objects that the users have in
common.
x Author similarity based on Re-Use of Components: The
Creation Action information could be use to identify re-use
of learning object components. For example several authors
could use the same picture or diagram inside they
presentations. As the Creation Action store information
about which existing components have been reused (see
Section 2), a 2-partite graph between Authors and
Components can be created and then folded over the

Components partition. The new graph will represent
relationship between different authors. More components
those authors have used in common, the stronger their
similarity.
The similarity metric obtained from the graph could be then
applied in recommendation tools. For example: If a user finds an
object useful, a link to similar objects could be provided (similar
to what Amazon does with books [1]). Also, the similarity
between users can be exploited to recommend Learning Objects to
a user, based on what other users that are in the same community
have recently download (similar to collaborative browsing
applications [3]). To automatically extract the communities from
the graph, an algorithm like EdgeBetweeness [25] can be applied.
The same procedure could be applied to the Author Similarity
graph. The communities of authorship can be automatically
extracted from the graph. The author then can be recommended
with components that have been created by other authors in the
same authorship community.
Beside recommendation systems for Learning Objects, these
similarity metrics could be considered as distance metrics. The
distance metric can be used inside clustering algorithms to
automatically find groups of similar objects. These clusters could
be used to improve the presentation of results of a search, much as
Vivisimo [26] does for Web Pages.
3.3 Personalized Ranking
To be able to personalize the search result order for a given user,
the application should have a representation of such user in a
profile. While this profile could be created explicitly by the user,
CAM information could help the application to learn it form the
user interaction with the tool. For example, the information about
stored in the Select, Publish and Retain from a user could help us
to determine in which objects is he/she interested, and rank higher
objects that are similar to those.
This work proposes the creation of a fuzzy profile that could
easily account for the evolving and not fixed behavior of an
instructor downloading learning objects. Instead of having a crisp
preference for one type of object, this profile will provide
different grades of likeness for several characteristics of the
learning object. This profile is constructed with several Fields.
The Fields could be a subset of the fields considered in the LOM
standard, especially the ones that use a vocabulary or represent a
classification. Each field will contain 2 or more fuzzy sets that
represent the values that the field could have (from the vocabulary
or the classification values). A user could “prefer” in different
degrees 1 or more of the values of a Field. The preference of the
user for each one of the values is calculated based on the number
of objects that the user have download before that contained that
value in the corresponding LOM field. This fuzzy profile has
been derived from research done to produce automatic TV
recordings for PVRs [27] like TIVO.
The fuzzy profile could be easily operationalized to provide a
personalized rank for Learning Objects. First, each field should
have a weighting value (that express how important that field is).
That value could be assigned by an expert or could be calculated
automatically for entropy of the distribution of the field values for
that user. For example if a user downloads objects from a wide
variety of topics, the weight of topic as a good ranking
measurement is low. Instead, if the user only downloads objects
in one language, the weight of that field should be high. Second,
each LOM record from the result list is converted to a similar
representation, using the same fields and a preference value of 1.0
for the value found in the metadata. Finally, the object
representation is operated with the profile in order to obtain how
well the object fits the preference of the user. This operation is
described in the following equation:
ji
i j
jii valueobjectFieldvalueuserField
userobjectnkPersonalRa
).().(
),(
xx

¦ ¦D
For example, lets consider a user that have download 20 objects,
16 with topic Computer Sciences and 4 with topic Physics. Of
those 20, also, 12 are in English, 4 are in French, 4 are in Spanish.
A fuzzy profile that represents that user could be expressed as:
U1 = {(0.8/ComputerScience + 0.2/Physics),
(0.6/English + 0.2/Spanish + 0.2/French)}
The fields weighting terms are 0.9 for Topic and 0.6 for
Language. Lets now considered 2 objects represented also as
fuzzy sets:
O1 = {(1.0/ComputerScience), (1.0/Spanish)}
O2 = {(1.0/Physics, 1.0/English)}
The calculated rank for both objects is:
O1 = 0.9*0.8 + 0.6*0.2 = 0.84
O2 = 0.9*0.2 + 0.6*0.6 = 0.54
O1 will be ranked higher than O2 as it is more similar to the user
profile.
The personalized calculation could be combined with the
popularity ranking described before to create a better ranking
algorithm, the same way as Google personalized Search mix the
standard Popularity measure with information from the user
profile to order the results.
3.4 Contextual Recommending
If the CAM is considered not only as a source for historic data,
but also as a continuous stream of contextualized attention
information, we can use very recent CAM (in the order of seconds
or minutes) to generate recommendations based on what the user
is focusing his/her attention at the moment. For example, the
recommender system could use the information stored in the if the
user has inserted an object inside a Course in a Learning
Management System (LMS), the LMS will generate a CAM
record with contextual information about which object was
inserted and in which lesson of the course. The recommending
system could use that information to present the user with similar
objects to the one inserted or others that have been used in similar
courses, based on the topic of the course or in similarity metrics as
the one explained in the Section 3.2.
The recommending system could also present objects that suit the
application that the user is using at a given moment, based on the
information about the object (LOM record). For example, if the
user is working Microsoft Power Point authoring tool,
presentations, slides, small texts, images and diagrams will be
recommended. If he/she is working with a SCORM Packager,
complete learning objects will be presented instead.
Contextual recommending techniques have been tried before in
several fields [28] [29]. Blinkx [30] is an example of this kind of
applications. It recommends web pages, videos and news based

on the present content of the screen of the user. A similar
application could be developed in a LMS for example, where the
system could recommend to the instructor materials to add to each
lesson, or could recommend the learner with similar or
complementary materials to the one that the instructor has added
to the course.
4. APPLICATION IN OTHER CONTEXTS
While the CAM based metrics proposed in this work were
designed for Learning Objects, they could be easily extended or
adapted to work for other kind of reusable components where
CAM could be collected. For example, given the exponential
grow of open source software libraries that could be reused inside
software projects, programmers are sometimes overwhelmed with
the amount of available choices. It makes sense to develop some
kind of ranking or recommending system that could help the
developers to select the right tools.
To construct the ranking application we can use the same methods
proposed for learning objects. The k-partite graph used to
calculate the popularity metric could be constructed using the
metadata information about the library (who is the author of a
software library) and contextual attention information about how
and when the programmers interact with the library (which
programmers have download it, in which software project they
have been used). Most of this information could be obtained from
open source project repositories like SourceFourge [31] . The
rationale behind the ranking would be: A library that have been
downloaded more often / at a higher rate is more useful. A library
produced by authors with highly useful libraries could also be
useful. A library re-used in many projects is probable highly
useful. This metrics are parallel to the ones described for learning
objects.
Recommending systems for software libraries could also be
constructed in a similar way to the ones proposed for learning
objects. For example, we can fold the Libraries-Programmers 2-
partite graph over the Libraries Partition, creating a graph that
relate Programmers between them based on the Libraries that they
have downloaded/used. Communities could be extracted form the
resulting graph and could be used for recommending a
programmer with new libraries that other members of his/her
community have used in their projects.
The precaution to have when applying this metrics to other
domains is the semantics of the relations that are created with the
graphs. For example, if two learning objects are used in the same
course, those two learning objects must have something in
common (same topic for example), while if two libraries are used
inside the same project, that does not mean that the libraries are
related (you could use a database access library and graphical
interface library inside the same project).
Other contexts where CAM information could be exploited to
rank and recommend elements with a similar strategy as the one
presented in this work are music mixes (component songs or
loops) and news aggregators.
5. FURTHER WORK
This work is just an introduction to how CAM information could
be used to rank and recommend Learning Objects. Several topics
should be solved before a big scale application that use the
metrics presented could be built:
x Collection and Integration of the different CAM sources:
While today exist several applications that generate CAM,
there is not an established multi-application CAM repository
that could be used to collect and integrate attention
information.
x Combination of different ranking strategies: When
different ranking strategies are combined, some weighting
coefficient must be applied. The calculation of those
coefficients is not trivial and should be made using extensive
user feedback.
x Critical mass vs. Closed Community: To be useful, the
metrics should be calculated over a significant amount of
CAM data. But if we integrate data from different
communities to obtain a bigger amount of CAM (for example
attention from different LORs), there will probably not exist
common objects, users or courses that could be used to
generate relations between the communities.
6. RELATED WORK
Broisin et al in [32] propose a framework to capture usage
information about Learning Object from different Learning
Management Systems and Repositories in order to analyze the
usage patterns of the users through a Management Application.
The approach of this paper goes a step further, using the attention
information to calculate metrics that could be used to improve
existing tools. Broisin’s work also uses a simplified format of
attention (basically usage information) in a non-standard format,
limiting the possible use of the information by other systems,
because existing applications should be reprogrammed to produce
that format. This work proposes the use of an extension of
AttentionXML standard to be able to capture the CAM from a
variety of systems that already produce it.
In a related area, digital libraries, Nicholson in [33] propose the
fusion of bibliometrics analysis with user-related data mining to
generate a new field of study, bibliomining. His proposal could
be compared with the one presented in this work: Using the
information about the book and the usage information generated
by the interaction of the users with the digital library system to
improve the user experience. While Nicholson mentions several
ways in which the attention metadata could be used, he does not
detail any specific metric to improve digital library systems.
7. CONCLUSIONS
The current immaturity of the tools to search and find Learning
Objects could be overcome if CAM information is store through
the lifecycle of the Learning Object and used to compute metrics
for ranking and recommendation. These metrics should generate
a meaningful and automated way in which Learning Object could
be ranked. This work presented detailed methods to calculate
various metrics and propose several uses for those metrics. The
proposed calculations could also be applied to rank and
recommend other reusable components from which CAM could
be gathered, as it was shown for the case of open source software
libraries example.
While the metrics are easy to calculate, and some initial data is
also present, more research is needed to be able to assemble a
large scale system that could gather the necessary amount of CAM
in order to render the calculationsmeaningful.

8. REFERENCES
[1] Linden, G.; Smith, B. and York, J. Amazon.com
Recommendations: Item-to-Item Collaborative Filtering.
IEEE Internet Computing, 7, 1 (2003), 76-80.
[2] Shepherd, M.; Watters, C. and Marath, A. Adaptive User
Modeling for Filtering Electronic News. In Proceedings of
the 35th Annual Hawaii International Conference on System
Sciences, 2002. HICSS. (2002), 1180- 1188.
[3] James, S. Outfoxed Collaborative Browsing,
http://www.getoutfoxed.com. Retrieved on May, 2006.
[4] Najjar, J., Meire, M. and Duval, E. Attention Metadata
Management: Tracking the use of Learning Objects through
Attention.XML. In Proceedings of World Conference on
Educational Multimedia, Hypermedia and
Telecommunications. (2005). 1157-1161.
[5] Najjar, J., Wolpers, M. and Duval, E., Attention
Metadata:Collection and Management”, WWW2006
workshop on Logging Traces of Web Activity, Edinburgh,
Scotland, (2006).
[6] AttentionXML, AttentionXML specifications,
http://developers.technorati.com/wiki/attentionxml.
Retrieved on June, 2006
[7] Duval, E. and Hodgins, W., A LOM research agenda. In
Proceedings of WWW2003: Twelfth International World
Wide Web Conference, (2003), 659-667.
[8] Ochoa, X. Learning Object Repositories are Useful, but are
they Usable? In Proceedings of IADIS International
Conference Applied Computing. (2005), 138-144
[9] Duval, E. LearnRank: the Real Quality Measure for
Learning Materials. Policy and Innovation in Education -
Quality Criteria, (2005)
[10] Aizawa, A.An information-theoretic perspective of tf–idf
measures. Information Processing and Management, 39,
(2003), 45-65.
[11] ISO/IEC JTC1 SC36. International LOM Survey: Report.
http://mdlet.jtc1sc36.org/doc/SC36_WG4_N0109.pdf
(2004).
[12] Ariadne Foundation. Ariadne Foundation.
http://www.ariadne-eu.org (2005).
[13] Collis, B. and Strijker, A. Technology and Human Issues in
Reusing Learning Objects. Journal of Interactive Media in
Education, 4, (2004).
[14] Verbert, K. Jovanovic, J. Gaãevic, D. and Duval, E.
Repurposing Learning Object Components. OTM 2005
Workshop on Ontologies, Semantics and E-Learning, (2005).
[15] IEEE-LOM Editor, http://www-i5.informatik.rwth-
aachen.de/i5new/staff/chatti/LOMEditor/index.html.
Retrieved June 2006.
[16] Cardinels, K., Meire, M., and Duval, E. Automating
metadata generation: the simple indexing interface. In
Proceedings of the 14thWWW conference, (2005), 548-556
[17] Dalziel, J. Implementing Learning Design: The Learning
Activity Management System (LAMS), ASCILITE (2003)
[18] ADL, SCORM Standard, http://www.adlnet.gov/index.cfm,
Retrieved March, 2006
[19] IEEE. IEEE Standard for Learning Object Metadata.
http://l tsc.ieee.org/doc/wg12/ (2002).
[20] Page, L., Brin, S., Motwani, R. and Winograd, T. The
PageRank Citation Ranking: Bringing order to the Web.
Technical Report, Computer Science Department, Stanford
University (1998)
[21] Google Technology, http://www.google.com/technology/.
Retrieved, August 2006.
[22] Radlinski, F. and Joachims, T. Query Chains: Learning to
Rank from Implicit Feedback, Proceedings of the ACM
Conference on Knowledge Discovery and Data Mining.
(2005).
[23] Fan, W., Gordon, M. and Pathak, P. A generic ranking
function discovery framework by genetic programming for
information retrieval, Information Processing and
Management. 40 (2004) 587–602
[24] Nascimento, M., Sander, J. and Pound, J. Analysis of
SIGMOD's co-authorship graph . ACM SIGMOD Record,
32, 3. (2003). 8-10
[25] Girvan, M. and Newman, M. Community structure in social
and biological networks. Proc. Natl. Acad. Sci. 11. (2002).
[26] Vivisimo Clustering Engine. http://www.vivisimo.com.
Retrieved August 2006.
[27] Pigeau, A., Raschia, G., Gelgon, M., Mouaddib, N. and
Saint-Paul, R. A fuzzy linguistic summarization technique
for TV recommender systems. The 12th IEEE International
Conference on Fuzzy Systems, 2003. FUZZ '03. 1 (2003)
743-748.
[28] Google AdSense, https://www.google.com/adsense/.
Retrieved August 2006.
[29] Fan, W., Gordon, M. and Pathak, P. Incorporating contextual
information in recommender systems using a
multidimensional approach. Information Processing and
Management. 40, 4. (2004). 587-602.
[30] Blinkx Contextual Search. http://www.blinkx.com.
Retrieved August 2006.
[31] Sourceforge, Open Software Repository.
http://www.sourceforge.net. Retrieved August 2006.
[32] Broisin, J., Vidal, P. and Sibilla, M. A Management
Framework Based On A Model Driven Approach For
Tracking User Activities In A Web-Based Learning
Environment. EDMEDIA, (2006) 896-903
[33] Nicholson, S. The basis for bibliomining: Frameworks for
bringing together sage-based data mining and bibliometrics
through data arehousing in digital library services.
Information Processing and Management. 42, 3 (2006). 785-
804.