Design Solutions in an e-Learning System
Ileana Trandafir, Ana-Maria Borozan and Alexandru Balog
National Institute for R&D in Informatics - ICI Bucharest, Romania
Keywords: e-Learning, learning personalization, learning design, learner model, educational content.
Abstract: This article describes some theoretical and practical aspects regarding the process of personalizing services
and content for an e-learning environment, issued within the national research project “Innovative System
for Personalized and User-centered Learning with Application to Project Management (SinPers)” developed
by the National Institute for R&D in Informatics, the Academy of Economic Studies and the Project
Management Association Romania. This project proved that the learning personalization needs innovative
solutions for three main domains: the design of the teaching-learning process (actors roles, activities
structure and flow, events and conditions specification), the creation and maintenance of an individual
model for each learner (goal, preferences, knowledge level, learning results) and the structuring and
accessing mode of the educational digital content (based on domain and competences ontology, learning
objects, metadata).
1.1 Lifelong Learning - New
Requirements for e-Learning
In an information based society the lifelong learning
becomes an essential process, sustained mostly by
information and communication technologies. The
lifelong learning is a new form of work; the use of
knowledge acquired in school is made at the
working place, and the professional activity is more
and more relying on intensive-knowledge. Learning
becomes inseparable from the working process of
adults. Similarly, the children need new educational
instruments and environments to help them educate
their desire to learn and create. Lifelong learning is
more than “adult education”; it covers and unifies all
phases: intuitive learner (at home), scholastic
learner (at school and university), and skilled
domain worker (at workplace) (Fisher, 2000).
Several basic principles of the learning theory
have been re-evaluated in the last decade, as result
of the new services offered by the information and
communication technologies, as well as because of
the lack of success of the existing e-learning
systems. More and more critics sustain that the
simple use of ICT as support of the existing learning
practices is insufficient; old frameworks, such as
instructionism, fixed curriculum, memorization, out-
of-context learning etc., are not changed by the
technology itself (Attwell, 2007; Dondi, 2007).
New computational environments are necessary
to support new education paradigms such as lifelong
learning, integration of working and learning,
learning on demand, real-life problems, self-directed
learning, and information contextualized to the task
at hand, intrinsic motivation and collaborative
learning. The fulfilment of each user’s individual
needs (expressed explicitly or implicitly) - learning
personalization, educational content re-usability on
large scale - content reusability, assurance of the
communication between e-learning systems as well
as with other human resources management systems
- systems interoperability are the main objectives of
researches in this domain.
1.2 Personalization - An Advanced
Approach in the e-Learning
The learners have different learning styles,
objectives and preferences, which lead to variances
of efficiency and effectiveness of the traditional e-
learning systems from individual to individual. The
Trandafir I., Borozan A. and Balog A. (2008).
LEARNING PERSONALIZATION - Design Solutions in an e-Learning System.
In Proceedings of the Fourth International Conference on Web Information Systems and Technologies, pages 364-369
DOI: 10.5220/0001517403640369
learning personalization becomes an advanced stage
in the e-learning systems evolution.
The study carried out within the framework of
the SinPers project
(www.ici.ro/sinpers/) shows that
the concept of personalization can be interpreted and
implemented in different ways and proves that
learning personalization needs new solutions for a
multitude of aspects, such as: the identification of
the profile, goals and user context; the formalization
of knowledge; the description of learner
competences and learning objectives; the evaluation
of learner’s skill level; feedback return in an
adequate manner.
Within the SinPers framework, the
personalization issue was solved by adopting
innovative solutions in three main domains (figure
1. the modelling of teaching-learning process,
2. learner modelling and
3. digital content modelling.
Figure 1: SinPers - personalization pillars.
A useful support was the adoption of the IMS
standard, which offers a conceptual framework for
all three mentioned areas of expertise (IMS, 1999-
2007). This choice was based on a global evaluation
of the existing e-learning standards (e.g. SCORM,
The modelling of the teaching-learning process
implied the evaluation of several alternative
scenarios having as objective the personalization of
the content and services offered to the learner, that
lead to the creation of a composed scenario
including several steps:
1. the specification of the personal training
options (e.g. entire course, one module,
competence acquirement), personal data (e.g.
studies / qualifications, age, activity domain)
and personal preferences (e.g. learning style,
hardware-software support),
2. pre-assessment of the learner knowledge level
according to its options,
3. personalization of the unit of learning (course,
lesson, module etc.) based on the learner’s
options, knowledge level, profile and
4. unit of learning completion, with specific sub-
phases for a computer assisted course,
5. final assessment and course close-up.
The modelling of the teaching-learning process was
based on the informational IMS model (IMS, 1999-
2007), and was next transposed in XML. The roles
of different actors, learning activities, support
activities and the training environments (learning
objects and services) were defined in accordance
with the proposed scenario.
Essential elements were to define the method,
properties and conditions, on which are based the
personalization mechanisms as well as to control the
process execution. According to the standard, each
learner’s personalization is made in several ways:
Activities Tree Personalization - through
definition of the plays, acts, activity structures
and role-parts,
Environment Tree Personalization - similar
with the activities tree,
Educational Content Personalization (selecting
and sequencing of the learning objects).
Thus, the personalization was specified explicitly (by
defining the conditions that determine the
completion of an act or activity, the plays and the
acts components of the teaching process and the
role-part relations), and implicitly (by specifying the
teaching process workflow, e.g. in a sequence of
activities, where an activity can be accessed only if
the previous activity was completed successfully). In
this case, the status of activities sequence must be
updated for each user. Regardless of the explicit or
implicit personalization method, the basic element is
the learner dossier. The maintenance of the learner
dossier is completed directly by the user through the
actions that were run (the event of completion with
success of a learning activity), or by a different actor
(the trainer during the support activities, e.g. a test
A detailed description of the design steps for the
teaching-learning process is presented in (Trandafir,
process modelling
LEARNING PERSONALIZATION - Design Solutions in an e-Learning System
The learning design (LD) specifications are the
foundation for the SinPers system architecture; some
extra functions have been added: management of the
educational content and administrative services
(figure 2).
Figure 2: SinPers architecture.
3.1 Levels of Personalization
The personalization based on the learner model can
be achieved from five different angles or five
distinct levels, from simple to complex, as follows
(Martinez, 2000):
1. Name-recognized Personalization - is the
easiest solution and consists of the simple
acknowledgment of learners as individuals (e.g.
the learner name appears in the upper part of the
screen or previous activities or accomplishments
are marked);
2. Self-described Personalization - allows the
learners to describe preferences and common
attributes, the initial cognitive status or existing
skills, preferences, or past experiences (using
questionnaires, surveys, registration forms,
comments etc.);
3. Segmented Personalization - uses demographic,
geographic, psychological or other criterias to
group or segment the potential learners into
smaller, identifiable and manageable groups, for
personalization purposes;
4. Cognitive-based Personalization - uses
information about individual learning preferences
or styles, from a cognitive perspective, in order to
provide educational content in accordance with
these attributes of each learner. This
personalization type is more complex that the
previous ones and needs to handle more learner
attributes at each interaction with the system, by
collecting data, monitoring learning activities,
comparison with other learners behaviour and
predicting what the user would like to do or see
5. Whole-person Personalization - assumes
profound understanding of the psychological
factors with major impact on the behavioural
differences in the teaching-learning process (more
profound than based on the cognitive profile). It
requires success in predicting and delivering the
necessary content, so that the learner can achieve
its objectives and - this is more important - to
improve its ability to learn and develop a personal
relationship with the online system. This
approach implies the consideration of multiple
emotional aspects, feelings, intentions that
substantially influence the learner’s behaviour
and evolution. As any individual, the system
learns as well by collecting data, tracking
learner’s progress, and comparing responses with
the correct ones in order to improve the responses
progressively. Therefore, it becomes more precise
over time. This is the most sophisticated
personalization form and requires real time
personalization in order to modify the responses
provided to the learner based on a dynamic
learner model that is changing throughout the
learning experience (as a teacher in class).
The learner model defined in the SinPers project
implements the personalization levels presented in
the table no. 1.
As emphasized in the this table, the learner
model is set up progressively, starting with the data
entered at user’s enrolment and continuing with the
specification of the objectives and preferences, the
assessment of the initial cognitive status and
learning styles (based on interactive pre-assessment
tests) and results tracing.
3.2 The Data Structure of the Learner
At first the static and dynamic properties of the
learner within one unit of learning were
differentiated, by defining two distinct entities:
Unit of
Unit of
WEBIST 2008 - International Conference on Web Information Systems and Technologies
Table 1: The relationships between the personalization
levels and data model.
Data model of
Data gathering /
user interface
enrolment and
registration /
online registration
enrolment and
registration /
online registration
Course objective Selection of the
menu option /
2 Self-described
test / interactive
Age, Activity
domain, Studies,
enrolment and
registration /
online registration
3 Segmented
test / interactive
Difficulty level
enrolment and
registration /
online registration
Unit of learning
personalization /
4 Cognitive-based
Learning style Test to identify
the learning style
/ interactive
5 Whole-person
Learner portfolio
(results, grades
obtained for each
Unit of learning
management /
tracing the learner
progress and
The Learner Profile - containing the personal
properties set; these properties have an
invariant character during the unit of learning
execution and can be updated only at the
completion of the unit of learning,
The Learner Portfolio - containing the
information set regarding the learner activities
and results during the unit of learning,
respectively recording and managing the
learner’s history of the training process, the
scopes and achievements / obtained knowledge.
According to the IMS recommendations, seven
segments have been used to define the personal
properties: identification, goal, qualification-
certification-licence, competency, accessibility,
affiliation, security key. Additional customisation
was performed, with respect to the standard (fig. 3).
Figure 3: Learner data model in SinPers.
The planned and achieved activities and partial or
final results (transcript) are the basic elements of the
learner portfolio.
Detailing the accessibility information as well as
the modality to create specific vocabularies for these
information categories according to course domain
and users categories (e.g. activity domains,
competences, course scope, security levels) are
original elements of this project.
The accessibility holds the most important
information needed to perform the personalization of
LEARNING PERSONALIZATION - Design Solutions in an e-Learning System
the unit of learning, respectively learner preferences
regarding: teaching language, educational objects
format, the technological support used (operating
system, browser), difficulty level (very easy, easy,
average, difficult, very difficult), as well as the
learning style (active/reflexive, sensorial/intuitive,
visual/verbal, inductive/deductive) declared or
established through testing.
The learning styles have been identified based on
recent research studies that analyse the basic two
steps of the learning process: collecting and
processing information, concluding in the most cases
upon the four styles mentioned above.
In order to meet the personalization options, the
content of SinPers “project management” course is
structured as a collection of distinct learning objects
(LO). Their reuse in different contexts and
(re)sequencing in different learning paths requires
the adoption and definition of two essential
domain ontology (the structure of concepts
and the relationships between them),
metadata describing the properties of the
learning objects.
Knowledge is represented on different levels of
abstractization. On the lower level are the LOs,
defined as entities which may be used, reused or
reffered in the learning process specified previuosly.
These are logical containers which represent
resources deliverable through the web, like lessons
(HTML pages), a simulation (Java applet), a test
(HTML pages with evaluation forms) or any other
object provided through web having learning as
Metadata is a collection of attributes of the
objects from the previous level, which are describing
the object type (text, slide, simulation, questionnaire
etc.), the requiered educational level (highschool,
university etc.), language, interactivity level etc.
The third level of abstractization (ontology) is
used for the specification of the domain concepts
and the relations between these. A domain concept
can be represented by one or more LOs (having
different attributes).
The main relationships between concepts are: Is_
part_of and Required_by dictating the hierarchical
relationships between concepts as well as the
constrains defining the mandatory learning order of
the concepts; the relation Suggested_Order can be
added optionally. The link between the concepts and
the learning objects is explicitated by the relation
In order to develop the ontology for “project
management” an internationally recognized standard
was needed, to provide foundation for the definition
of the domain concept and project manager
competences. The standard was ICB - International
Competence Baseline al IPMA (Project
Management Association). The ontology of the
project management course developed by SinPers
project contains 201 concepts and the three types of
relationships mentioned above. Figure 4 presents a
fragment from the domain ontology diagram,
representing the course module “general knowledge
about a project” (Bodea, 2007).
The course ontology has been extended with the
competences ontology, taking into consideration that
a competence involves learning / proving knowledge
referring ‘n’ basic concepts. This approach is
another original element of the project. The
competency ontology (in line with ICB) allows the
identification of a possible gap between the
reference and the actual competency profiles and the
identification of the project management training
requirements. A project management learning
approach based on ontology allows finding the most
suitable training when there a similarity but does not
an exact match between training offers and the
competency gap.
The SinPers research proved that in order to
personalize services and content for e-learning
systems there are needed new solutions for at least
three major areas: design of the teaching-learning
process (actors, activities, conditions, events etc.),
creation and maintenance of an individual model of
each learner (educational requirements, preferences,
knowledge level, pre-requisites etc.), an new
structuring and access mode for the digital content
(domain and competences ontology, learning
objects, metadata).
Within a teaching-learning defined process, a
personalized unit of learning (course, lesson,
module) is composed by an activity and educational
objects tree offered to the learner. These objects are
selected from a digital content warehouse by
comparing metadata with the characteristics and
preferences of the learner and set up in a sequence
according to the relations between concepts and the
activity flow previously defined.
WEBIST 2008 - International Conference on Web Information Systems and Technologies
Figure 4: Project management domain ontology - fragment.
Attwell, G. (2007) Personal Learning Environments - the
future of eLearning? Available from:
Bodea, C., (2007) Ontology-Based Learning in Project
Management, ECEL-Electronic Journal of e-Learning,
Academic Conferences Limited, Curtis Farm,
Kidmore End: England
Dondi, C. (2007) The underground rivers of innovative e-
Learning: a preview from the HELIOS Yearly Report
Available from: http://www.elearningpapers.eu/
Fisher, G. (2000) Lifelong Learning - More
Than Training. Available from: http://
IMS Global Learning Consortium (1999-2007). Available
from: http://www.imsglobal.org/
Martinez, M. (2000) Designing Learning Objects to
Personalize Learning, Available from:
Trandafir, I., Borozan, A-M. (2007) eLearning Design
based on Personalization Requirements in
Proceedings of International Technology, Education
and Development Conference (INTED 2007), held in
Valencia, Proceedings CD
LEARNING PERSONALIZATION - Design Solutions in an e-Learning System