Keith Maycock, Sujana Jyothi and John Keating
National University of Ireland, Maynooth
Maynooth, Co.Kildare Ireland
SCORM learning object, Multiple Representation Approach, Exploratory Space Control, Cognitive Trait
This paper proposes extensions to the architecture of any Learning Management System (LMS) that utilizes
the Sharable Content Object Reference Model (SCORM), to incorporate Multiple Representation Approaches
(MRA) and Exploratory Space Control (ESC) features, when interacting with a learner. The learners profile
will consist of the traditional student modeling features such as students goals, preferences and knowledge.
The profile will also incorporate a Cognitive Trait Model (CTM) to measure the learners cognitive abilities.
The LMS provides functionality for dynamic login to reduce the time spent getting to know the learner. If a
course is changed to ensure that the course is MRA compliant to suit the cognitive needs of that learner, the
transformation that course encored is stored in a Learning Experience Repository (LER) for future reference.
In effect, the learners profile becomes the author of educational content throughout the learning experience,
ensuring that the content delivered will suit the cognitive ability of each learner to increase the throughput.
ESC techniques are used throughout the LMS interaction with the learner once a learning experience has
concluded to offer suitable links to other related courses. This paper also discusses various factors that must
be taken into account when developing a LMS, for example, teaching styles, different types of students and
learning styles. The proposed extensions will enhance the learning experience for individual users.
Currently, in higher education, there are roughly 70
million students worldwide. This number is expected
to more than double before the year 2025 to over 160
million students (LittleJohn, 2003). The only possi-
ble solution to cater for the expected influx of peo-
ple entering into higher education is to automate the
process of learning. However, this is not an elemen-
tary task. If we look at the results of a number of stud-
ies carried out on the performance of individually tu-
tored students against the performance of an average
student in a typical classroom environment, we find
that, the speed with which different students progress
through instructional material varies by a factor of 3
to 7 (Gettinger, 1984). An average student in a typi-
cal classroom environment asks on average 0.1 ques-
tions every hour in contrast to an individually tutored
student asking on average 120 questions every hour
(Graesser and Perso, 1994). Furthermore the achieve-
ment of individually tutored students will exceed that
of classroom students by as much as two standard de-
viations (Bloom, 1984) - an equivalent which is equal
to raising the performance of 50 percentile students
to that of 98 percentile students. These results show
the vast range of differences between the learning ca-
pabilities of each learner. It is very important when
developing an education environment to take into ac-
count the environmental contexts. These contexts in-
clude the nature of the subject discipline and the level
of its learning; the characteristics of the learning ma-
terial and the role of the human teacher (Patel, 1998).
Support should also be available for dealing with a
learner’s learning profile. The profile should consist
of the entire learner’s educational history, learner’s
goals, preferences and cognitive ability.
In November 1997, the Department of Defense
(DoD) and the White House Office of Science Tech-
nology Policy (OSTP) launched the ADL initiative.
The mission of the ADL is to provide access to the
highest quality of education and training, tailored to
the individual needs of each user, anytime anywhere
(ADL, 2004). The ADL initiative borrowed from
many different specifications and standards such as:
Maycock K., Jyothi S. and Keating J. (2006).
In Proceedings of WEBIST 2006 - Second International Conference on Web Information Systems and Technologies - Society, e-Business and
e-Government / e-Learning, pages 287-292
DOI: 10.5220/0001257702870292
LTSC (LTSC, ) and IMS (IMS, ) when develop-
ing the Sharable Content Object Reference Model
(SCORM). SCORM is used to produce and deploy
courses that can be tracked and delivered to a stu-
dent by a Learning Management System (LMS) in
a standardized way. An LMS is software that auto-
mates training event administration through a stan-
dard set of services that launch learning content, keep
track of the learner’s progress and sequence learning
content. SCORM courses are fully defined within
a SCORM content package. The SCORM manifest
is inside the content package. The manifest con-
sists of metadata, organisations, resources and sub-
manifests. The metadata is used to describe in full the
version of SCORM and type of course. The organisa-
tions section details the sequencing information of the
various learning objects that are encapsulated within
the content package. The resources section is fully
described using XML metadata elements to describe
the content that is being delivered. Sub-Manifests can
also be used to create structured courses with different
layers of dept.
One of the problems that we perceive with most e-
learning educational systems is that authors of educa-
tional material are likely to have different ideas on the
best teaching practices which, can hence hinder the
development of a learner’s learning experience. De-
veloping an educational system around the SCORM
would easily be able to overcome the problem of the
teacher being in full control of the learning experience
as the hierarchical learning activities and the corre-
sponding sequencing information are fully described
within an activity tree (Maycock and Keating, 2005).
The activity tree is not a static structure and is free to
change with the requirements of the author of educa-
tional media. Once the learning experience has initi-
ated the learner’s profile becomes the author for the
duration of the learning experience and is capable of
changing the educational media to adapt the specific
learners needs immediately. After the learning experi-
ence has concluded, the learner’s profile is returned to
the learner. Enabling learners to store their own pro-
file locally, enhances the learning experience, as the
learners would be free to utilise any LMS and imme-
diately initiate a learning experience, based on their
learning profile. The next section details a learning
profile that is suited to automatically control a learn-
ing environment utilising SCORM.
Most of the existing student models are focused on the
specific domains with which they interact with, for
example, the domain concepts competence and do-
main skills required. Such student models, are called
performance based student models and include the
student competence state models (Staff, 2001) and
process state models (Martin, 1999). To create a
truly adaptive learning environment across multiple
domains the cognitive traits of a learner should be
catered for. The cognitive traits that are associated
with learning are working memory capacity, induc-
tive reasoning ability, information processing speed
and associative learning skill.
Working memory capacity also known as Short-
Term Store (STS) facilitates temporal storage of re-
cently perceived information, allows active retention
a limited amount of information, (7 +/- 2 items), for a
short period of time (Miller, 1956). The range of in-
formation perceived to be active is almost double and
should be catered for by a learning system. Induc-
tive reasoning ability is the ability that allows us to
construct concepts from examples (Kinshuk and Mc-
Nab, 2005). Inductive reasoning is seen as one of the
important characteristics of human intelligence. It is
strongly recognised that inductive reasoning ability
can be extracted from most aptitude tests and is the
best predictor for academic performance. Information
processing speed determines how fast learners can
acquire new information correctly. Adapting to the
information processing speed would enable a learn-
ing environment to reduce the possibility of cognitive
overload. Associative learning skill is the skill to link
new knowledge to existing knowledge. Students with
high associate learning skill should be given content
that has been adapted to existing relevant information
already encountered in past learning experiences.
Our proposed profile consists of two distinct tiers.
The first tier consists of information entered by the
learner, detailing personal information to allow a
LMS to personalize content. The second tier con-
sists of the learner’s cognitive traits and educational
history. This latter tier of the profile is automati-
cally updated by the LMS after learning experiences.
The profile will be contained within an XML file and
stored in a repository of personal profiles. Storing the
personal profile as an XML file enables the LMS easy
access to the profile and is easily incorporated into
a SCORM learning object at the start of a learning
experience, as discussed in (Maycock and Keating,
2005). Each of the cognitive traits that are being mon-
itored will be assigned a numeric value in the range of
-1 to 1. After a learning experience has concluded, a
graphical representation of the current learning object
is generated, mirroring the structure of the SCORM
learning objects activity tree with associated meta-
data. This graph is stored in the educational history
section of the profile under past experiences.
We propose adapting the Cognitive Trait Model
(CTM) to supplement a traditional performance based
student model in our proposed learning environment.
CTM was developed by Taiyu Li, Kinshuk and Ashok
Patel (Taiyu Lin and Ashok, 2003) at Massey Univer-
sity in New Zealand, and facilitates the construction
of a long-term student model, based on invariant cog-
nitive traits. Once the CTM has successfully identi-
fied the characteristics of a learner no more training is
2.1 Cognitive Trait Model(CTM)
Learner modeling motivates the creation of systems
that are adaptive to each of the learner’s interests,
preferences and background knowledge in order to
provide personalised instruction to a particular learner
The Cognitive Trait Model (Taiyu Lin and Ashok,
2003) may be adopted to produce a student profile that
is easily incorporated into the SCORM content pack-
age as discussed in (Maycock and Keating, 2005).
The CTM is composed of a user Interface module, a
Learner-Behavior model, Interaction model, Learner-
Performance model and Trait Analyser as seen in 1.
Figure 1: Cognitive Trait Model.
The Interface Module depicts the system-learner
interaction. It is an important component of this
model and acts as a communication medium, and as
an external representation of all the system’s models.
This acts as a mediator between the learner and the
system. The Learner-Behavior Model records learner
interaction associated with different cognitive traits
such as the working memory capacity, inductive rea-
soning ability, information processing speed and as-
sociative learning. It generates a graphical represen-
tation of the current SCROM learning object after the
learning experience has concluded. The objective of
the Interaction Model is twofold: to deliver an adap-
tive unit of learning for each learner and to observe
the behavior of the learner while the learner interacts
with the learning material. Every student interaction
recorded in the behavior model has one interaction
entry. This model produces an XML file that is di-
rectly associated with the behavior of each student.
The Learner-Performance Model is used to identify
the weight of each of the cognitive trait affected by
the learning experience.
Figure 2: Components of a Trait Analyser Derived from -
Taiyu Lin, Kinshuk & Ashok, P.(2003) (Taiyu Lin and Ashok,
The inputs of the Trait Analyser are from the three
components of the CTM namely the Learner behavior
model, the Interaction model and the Learner perfor-
mance model. The main criterion of the Analyser is
to analyse the learner based on the cognitive traits and
the other information known about the learner. The
trait analyser is composed of three different compo-
nents: the pattern detector, the individualised tem-
perament network and the CTM updater as shown
in 2. The pattern detector examines the records of
the student’s current actions that are produced by the
LMS to recognise patterns that are associated with
different cognitive abilities. There are many different
types of manifestations that symbolize different traits,
for example navigational linearity, reverse navigation,
excursions, simultaneous tasks, retrieval of informa-
tion from long-term memory, and long sequences of
calculation or procedures. The individualised tem-
perament network is used to adjust the CTM accord-
ing to the results obtained from the pattern detector
and the CTM updater is the communication channel
that communicates with the CTM. After an analy-
sis is carried out the CTM is updated and communi-
cates back to the interface module to incorporate some
adaptive functionality. A combination of the adaptive
techniques in neural network will be used to track the
navigation patterns of the learners and their cognitive
traits. A modification to the cognitive trait model is
possible during the implementation of the techniques.
Multiple Representation Approach (MRA) (Kinshuk
and Kashihara, 1999) and Exploratory Space Control
(ESC) (Kinshuk and Lin, 2003) are used to fine tune
learning experiences. The following section details
the advantages of both techniques and illustrates how
these techniques are incorporated into our proposed
learning environment architecture.
3.1 Multiple Representation
Approach (MRA)
MRA is used to change the presentation of domain
knowledge concepts, in terms of the complexity and
granularity, to suit the learner’s cognitive ability and
progress through a learning experience. It enhances
the educational system’s design to suit the learner’s
perspective. There are various types of multime-
dia objects, each stimulating different cognitive re-
sponses. Audio stimulates imagination, video clips
stimulate action information, text conveys details and
diagrams convey ideas. Generating MRA compliant
learning objects in a learning environment can reduce
the cognitive load by using similar multimedia objects
to convey domain concepts. If any media objects are
omitted during the MRA process they must be avail-
able to a user on specific request, reducing the possi-
bility of losing any relevant information.
There are three different types of filtering used in
MRA: restriction, extension and approximation. Re-
striction is used when a learning object contains an
excessive number of media objects, thereby causing
cognitive overload. A subset of these media objects
maybe selected to produce an MRA compliant learn-
ing object conveying the current domain concept. If
several different MRA compliant learning objects are
available then the combination of media objects offer-
ing the best learning experience suited to that learner’s
cognitive ability maybe selected. When the number of
media objects is insufficient to produce an MRA com-
pliant learning object extension maybe used. An LMS
will search learning object repositories to find suitable
learning objects that will enhance that learning ob-
ject and make it MRA compliant. When the structure
or content of a SCORM learning object is changed,
a transformation occurs within that learning object’s
activity tree, as discussed in (Maycock and Keating,
2005). MRA extensions are seen also as transforma-
tions and are saved in a Learning Experience Repos-
itory (LER) for further search and discovery during
different learning experiences. If a learning object
was poorly designed, and the complete learning ob-
ject cannot be made MRA compliant, the largest mul-
timedia rich subset is selected. The process of exten-
sion is then carried out on the reduced learning object.
The number of learning object repositories are in-
creasing. Many of these repositories are freely avail-
able online (Repositories, ). Several organizations
that have generated learning objects and host there
own repository or have provided guidelines, tem-
plates, or frameworks for objects that are stored in
their repository. SCORM is fast becoming the stan-
dard for producing learning objects and has been
adopted by many different LMSs. SCORM is cen-
tered around developing small granular learning ob-
jects with minimal content associated with the learn-
ing material, ensuring maximum reusability. With ex-
pansive metadata our learning environment will ef-
fectively be able to generate new content to suit the
cognitive needs of each user by manipulating the
graphical structures that have been generated mirror-
ing each learning object’s activity tree.
3.2 Exploratory Space Control
ESC limits the learning space to reduce the cognitive
load of each learner and to make sure that learners do
not get lost in hyperspace (Kinshuk and Lin, 2003). In
our proposed system, ESC is used in the exploration
of further reading once a learning experience has con-
cluded. The exploration elements catered for are the
learning content and navigational paths. When deal-
ing with the learning content, the ability of the student
to interpret the content exactly as the content devel-
oper expected, is a very complex task and depends on
the learner’s cognitive ability. There have been many
studies carried out on how learners perceive instruc-
tional material, in particular, Phenomenography (Lau-
rillard, 2002) (Marton and Booth, 1997) (Ramsden,
1988) (Ramsden, 1998). This is successful at illu-
minating how students deal with structure and mean-
ing. These studies have led to the identification of two
contrasting approaches to studying content, i.e. an
atomistic approach and a holistic approach. Learners
utilizing a holistic approach interpreting some con-
tent retain the concepts that are trying to be conveyed
but may suffer some cognitive overload. Learners uti-
lizing an atomistic approach lose the structure of the
content being delivered, hence, may have a different
interpretation to the actual meaning. The ESC server
uses MRA techniques when searching for a suitable
course for any given learner. As SCORM is focused
around producing reusable learning objects the struc-
ture of learning objects can be adapted to suit the indi-
vidual cognitive needs of a user and ensure that only
suitable courses are offered. These elements are fully
controlled by the learner’s learning profile and the ef-
fects of the cognitive abilities can be seen in (Kinshuk
and Lin, 2003).
Figure 3: Proposed outline architecture for LMS model.
Figure 3 represents the outline architecture of our
LMS with MRA and ESC servers incorporated. A
learner logs into the LMS from a remote platform and
the learner’s profile is uploaded to the LMS server.
Once the learner selects a course it is fetched from the
learning object repository and is passed to the MRA
server to ensure that the course is MRA compliant,
thereby suiting the cognitive ability of that learner. If
the course does not suit the individual cognitive needs
of that learner, suitable filtering operations are carried
out. The transformations that occur during filtering
operations are stored in the learning experience repos-
itories for further reuse. Once the learning experience
has concluded the LMS consults the learner’s profile
and the ESC server to offer suitable further reading.
Combining a CTM with traditional performance stu-
dent models and taking advantage of MRA and ESC
techniques enables our proposed extended LMS func-
tionality to allow a learner’s profile to become the
author of education material during learning experi-
ences. We believe that extending the SCORM man-
ifest and building learning experience repositories is
the next step in achieving an automated one-to-one
tutoring experience.
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