Intelligent Assistance for
Web-based TeleLearning
by
Jean Girard, Gilbert Paquette, Alexis Miara, Karen Lundgren
LICEF Research Centre, Télé-université
1001 Sherbrooke St. East, Montreal
gpaquett@licef.teluq.uquebec.ca
http://www.licef.teluq.uquebec.ca
Abstract. Intelligent assistance in telelearning environments is even more important than in individual
tutoring systems because of the inherent complexity of distance education. But the problem here is quite
different and provides large areas of unexplored territories especially in the full exploitation of the multiple
data sources captured from the interaction of the different actors involved in a telelearning event. We will
address some of these questions by presenting first a model of a Virtual Learning Centre (VLC) and an
implementation for Web-based training called Explora. A VLC focuses on the interaction spaces between
five theoretical actors: the learner, the informer (content expert), the trainer, the manager and the designer.
We will give an example of such an environment and show how the VLC supports learners and the other
actors in such a case. Then we will focus on a three-dimensional assistance space in a VLC based on a
typology of assistance resources. Finally, methods and tools to build an intelligent advisor for a web-based
Telelearning environment will be discussed using an operational JAVA implementation on the Internet.
Key words. Learning environments and micro-worlds, Non-standard and innovative interfaces, Student
modelling
1.
The Case for Advisor Agents in a Virtual Learning Centre
We live in societies coping with an exponential growth of information. In the knowledge
society, new competencies and higher level skills are required. The rapidly evolving
availability of multimedia telecommunication is a challenging answer to this knowledge
acquisition and knowledge building gap. But we have to integrate many types of
resources to really enhance learning. We see a telelearning system as a society of agents,
to use Marvin Minski’s term, some of them providing information, others constructing
new information, stills others helping collaboration between agents or providing
assistance to the other agents on content, pedagogical process or organisation of
activities.
What is behind terms like “distance education”, “on-line learning”, “telelearning”,
“multimedia training” is a multi-facetted reality from which we can identify six main
paradigms: the open classroom integrating technologies in traditional classrooms, the
virtual classroom [1,2], the teaching media, focused on multimedia courses on a CD
ROM, Web-based training 3, On-line learning communities [4,5 and Electronic
performance support system (EPSS) [6].
Intelligent assistance, based on knowledge of the learner’s activities and production, is
even more important in telelearning systems because of their complexity. But the
problem is quite different than in individualised ITS research. It provides large areas of
unexplored territories especially in the full exploitation of the multiple data sources
captured from the interaction of the different actors involved in a telelearning event.
2.
EXPLORA, a Web-Based Virtual Learning Centre
Our Virtual Learning Centre model 10 emphasises the concept of process-based
learning scenario coupled with assistance resources. Basically, the learner proceeds into a
scenario, a network of learning activities, using different kinds of information resources
to help her achieve the tasks and produce some outcome: a problem solution or new
information that can be used in other activities. The assistance resources for each task are
also planned at design time. The assistance can be distributed among many agents:
trainers interacting through email or teleconferencing, other learners, contextual help or
intelligent advisors.
2.1 Actors, roles and agents
We have described elsewhere [7,8,9] how we have built an object oriented model of a
Virtual Campus using software engineering methodology. In our Virtual Learning Centre
architecture, we identify five actors.
The Learner transforms information into personal knowledge.“ Information ” here is any
data, concrete or abstract, perceptible by the senses and susceptible of being transformed
into knowledge. “ Knowledge ” means the information that has been integrated by a
cognitive entity into its own cognitive system, in a situated context and use.
The Informer (the content expert) makes the information available to the learner. It may
be a person or a group of persons presenting information to the learners, but also a book,
a video, a software or any other material or media.
The Designer is the actor building, adapting and sustaining a learning system (LS) that
integrates information sources (human informers or learning materials), and also selfmanagement, assistance and collaboration tools for the other actors.
The Trainer facilitates learning by giving
advice to the learner about his individual
process and the interactions that may be
useful to him based on the learning
scenarios defined by the designer.
Finally, the Manager facilitates learning by
managing actors and events, for example
creating groups or making teleservices
available in order to insure the success of
the process, based on the scenarios defined
by the designer.
INFORMER
TRAINER
Assistance
Information
Collaboration
LEARNER
Assistance
Navigation
MANAGER
DESIGNER
Figure 1 - Actors and interaction spaces
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2.2 Interactions spaces and resources
Figure 1 shows the five theoretical actors and their interactions. We will limit ourselves
to interactions in which the learner is involved while learning, at delivery time.
Interactions between learner and designer. These are the interactions where the learner
interacts with the learning environment into which the designer has in a way “mediated”
himself by creating it. Here, the learner is preoccupied with the self-management of the
learning activities, of their input resources and of the products he has to build.
Interactions between learner and informer. These are the interactions where the learner,
individually, consults the information made available by the informer, and process them
in the production space to produce certain results while building personal knowledge.
Interactions between learners. These are interactions using different forms of
collaboration or cooperation between learners for team work, group discussion,
collaborative problem solving, etc.
Interactions between learner and facilitators. These interactions concern the assistance
that the system can provide to the learner on both the pedagogic (from the trainer) and the
management (from the manager) dimensions of telelearning. We will study these
interactions in the next section.
2.3 Host systems and HyperGuides
At delivery time, the learner and the other actors interact within a computerised learning
environment. This host system presents the content of a learning event, proposes
activities, identifies resources to achieve them and describes productions to make.
Figure 2 – A host telelearning environment and a trainer’s HyperGuide within Explora
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Figure 2 present such a web-based host system that intends to help learners build
knowledge about the job market, their job objectives, and methods to apply while
searching for a job.
Actors need different point of view on the host system in each interaction space: selfmanagement, information-production, collaboration or assistance. For example, in the
information space, a learner will need different input resources such as a list of related
web sites, descriptions of job categories, questionnaires to identify his skills or qualities,
etc. In the same information space, a trainer needs other information resources: traces of
the learners activities for diagnosis, information on the group of learners, information on
learner productions, annotation tools to identify and organise information for assistance.
These resources are made available through an external palette as shown in the floating
window on the right of figure 2. This is what we call an HyperGuide. It is an actor’s
environment for a course or program supported by the Virtual learning centre. It groups
resources into five interaction spaces (self-management, information, production,
collaboration and assistance) according to an actors’ role and course specificity. The
resources can be generic tools developed specifically for the Virtual Learning Centre,
they can be generic commercial tools or they can be web resources (used in many
courses) such as a technical FAQ or a virtual library.
The following table shows a possible distribution of resources into the production and the
assistance spaces, for the Job search example, for the learner, the trainer and the designer.
Learner
PRODUCTION
Text Editor
Speadsheet
Presentation Software
Model Editor
Trainer
PRODUCTION
Diagnosis Tool
Evaluation Forms
Feedback Questionnaire
ASSISTANCE
Access to trainer/manager
Guided tour
Technical FAQ
Help desk
Course advisor
ASSISTANCE
Access to content experts
Access to course designers
FAQ on tutoring
Tutoring guide
Designer
PRODUCTION
ISA Design Workbench
Bank of design objects
WebCT Authoring Tool
Learning System Editor
Advisor Definition Tools
ASSISTANCE
Instructional design advisor
FAQ on ISD methodology
Help resource persons
Table 1 – Example of the distribution of resources in the Virtual learning centre
These resources are external to the web course but some of them, for example the
learner’s trace, content navigator or intelligent advisors are linked to a course by the
designer using the learning system editor and the advisor definition tools.
There are many advantages to such an architecture. The Virtual Learning Centre is at the
learning organisation’s level, thus avoiding duplication and facilitating evolution and
reuse of resources from one course to another. It also speeds up the design process
because each individual web-course if freed from all the generic resources and the
circulation of information management between different actors.
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3.
Assistance in a Virtual Learning Centre
Whatever the agents, human or computer-based, the assistance must be « intelligent »,
that is, informed of the user, of the kind of tasks he is involved in, of the information he
has consulted or produced, of the interactions and collaboration with others, and finally
of his use of the assistance resources. In another words, the central question of ITS
research, the user model, reappears in telelearning systems, but in a different way.
We will first to present a typology of possible assistance resources, including advisor
agents. Then we proceed with the preliminary step of course modelling and integration
between the HyperGuide and the Web course. We finally present the way the user model
is constructed and updated at delivery time and give some examples of the pieces of
advice for a particular telelearning environment.
3.1 Types of assistance resources
When the designer plans the telelearning system, he must select or build different kinds
of resources for the assistance space. Assistance resources can be addressed to different
actors, for different purposes and by different means, yielding a three dimensional
assistance space.
The preceding discussion gives a framework for these decisions.
 The theoretical actors to which assistance is addressed is the first dimension of the
typology. Often the organisation will distribute roles differently. For example, the
trainer and the informer roles car be merged; as a consequence, there might be only
four assistance spaces to design. On the other hand, the designer’s roles can be split
into more specialised roles such as content modelling, pedagogical design and
learning material production, each of these actors having different assistance needs.
 The second dimension of assistance, its object, can be related to each of the
interaction spaces: self-management, information, production, collaboration or
instructional and organisational assistance. The assistance can be given to help users
progress through the activities, acquire consistent information and knowledge,
engage in peer collaboration or use efficiently the available assistance resources.
 Finally, the third dimension identify in what way the assistance will be provided: by
a human facilitator, by an access to help desks, using FAQs and help files, providing
contextual on-line help or intelligent advisor agents.
Because of its complexity, to design assistance in a telelearning system is a huge task. To
reduce this complexity, we propose to use a design method that help identify the
appropriate resources based on a good understanding of their possibilities and limitations.
Also, the integration of multiple assistance resources and agents, especially human
facilitators reduces the load on heavily computerised resources such as an ITS. Finally,
the development of generic assistance resources at the VLC level with the help of advisor
building tools should help tackle the heaviest components of the assistance space.
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3.2 Modelling a course
With these kinds of problems in mind, we have developed a method for learning systems
engineering called MISA 11. The MISA method presents the ID processes and tasks
according to an engineering perspective analogous to software engineering.
The method is a complex process decomposed at several levels, into sub-processes. Each
sub-process has its inputs and its 30 main products and 150 sub-tasks well defined, the
whole process generating a learning system as its final output. This method innovates by
using cognitive modelling techniques to represent knowledge, as well as pedagogical
models, learning materials model and delivery models. These four aspects of a learning
system, are clearly differentiated but they also are interrelated through specific
associations in each of six main phases, making the engineering process visible and
structured, thus facilitating quality control of the processes and their products.
For the assistance space, we need to focus here on the design of the knowledge model
representing the content to be learned and the instructional model representing the
learning events (program, course or activities). The terminal learning events are called
learning units for which we design an instructional scenario corresponding to a target
population of learners. These learning events form a network into which the learner will
navigate. Similarly, the knowledge model is a network of facts, concepts, procedures and
principles that the learner must acquire/build.
Actually, in the implementation presented here, we have not yet considered the full
potential of such MOT models for advising. We simplify the problem par reducing both
models to hierarchical trees. The first one is the instructional structure (IS) representing a
curriculum, its main subdivisions down to terminal learning events, that is learning
activities which are the main components of a learning scenario. The second one is the
knowledge structure (KS) compose of a main knowledge object, decomposed into related
parts and ending with simple objects. These models can be displayed as generic VLC
tools. On figure 3, such a tool is presented for the instructional model navigator.
Figure 3 – VLC tools: an instructional model viewer and a collaborative path finder
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To integrate a course like the one in figure 2 to the VLC, we need to link each unit in the
IS and the KS, and progress level within them, to actual web pages or sections in the host
system. This will enable the system to capture the user’s action and build a user model.
Then this user model will offer an alternate way for any user to navigate in the course by
selecting a learning event in an instructional model viewer (figure 3 - left), asking to
display the corresponding pages or sections. Also, another VLC tool (figure 3 – right)
enables a user to see another’s progress in the same web course, to help collaboration as
well as tutoring.
3.2 The user model
The integration of a course into the VLC is done through a simple interface where the
designer of the advisor describes the IS and the KS. Also, another design tool helps
define conditional principles that will update the user model, display an advice or engage
in a dialogue with the user. Finally a management tool identifies the learners and
facilitators assigned to the course, making possible, though JAVA scripting, to navigate
into pages visited by a trainee or a co-learner.
With the help of these tools, the designer of the advisor will go through the following
steps.
1- Define the IS as a hierarchical list of instructional units IU1, IU2,....., IUn and the
KS as a hierarchical list of knowledge units KU1, KU2,....., KUm
2- Define for each IU or KU a set of ordered symbols called progress levels Ip1, Ip2,.....,
Ipk ; Kp1, Kp2,....., KpL and assign user events in the host system to each couple
formed of a IU or KU with one of its progress level.
3- Define how the acquired or desired progress levels will be defined at the beginning
and how they will be updated according to the user’s actions.
4- Define the conditions that will fire each advice or action and state the piece of advice
or describe the action using a symbolic language.
At any time the system evaluates the user’s actions in the host system and assigns, for
each IU or KU, an estimated progress level considered to be acquired by the user, called
his belief, and a targeted progress level called his goal. The user model at time t is simply
the set of all beliefs and goals assigned at timet to each IU or KU.
User-Model (t) = BIU(I,t) , GIU(I,t)I=1,n  BKU(J,t) , GKU(J,t)J=1,m
where BIU(I,t) and GIU(I,t) are the acquired and desired progress level for IU
number I at time t
and BKU(J,t) and GKU(J,t) are the acquired and desired progress level for KU
number J at time t
The user model is updated essentially in three ways: by the designer’s predefined rules,
by querying the user at run time and by some action the user can take to modify the
model.
First, the designer will predefine basic actions on the model, that is principles stating that
if certain conditions are met, the model will be updated to some belief or goal level for a
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certain unit (IU or KU). Actually, the following actions can be taken: add or suppress a
progress level in a unit; move up or down the progress level in a unit, query the user with
a message and offer a set of possible answers.
A second way to update the model is when the user
is queried. Upon certain answers, other questions can
be asked, until the end of a predefined decision tree.
Then, depending on the path of the user’s answers,
the system will be able to update the model
according to the designer’s definition in such a case.
Finally, the user can see at any time the different
belief and goal levels assigned to him for any IU or
KU. The VLC tool shown on figure 4 is one way of
doing this. According to some evaluation of the
distance between a progress level and the next one,
and the number of progress levels for a IU or a KU,
different weights can be assigned to progress levels.
In this way, the system can display a bar diagram
showing the proportion of progress in each IU or KU
according to the belief level.
Figure 4 – A progress viewer in the VLC
Such a display can help the learner orient his actions. Also, he can disagree with the
system. The tool of figure 4 enables him to change the values of any acquired or goal
progress level.
4.
Extending the advisory system
We will now conclude this paper by identifying extensions to the actual implementation
we have outlined here. There are three directions in which we want to move.
The first one in to extend the actual advisor to support collaboration. Right now, the
JAVA implementation enables a user to see the other users’ learning path, look at their
progress in the host environment and communicate with them accordingly. An extension
of the user model has been designed by the first author by adding another couple of
progress level to the model for each instructional unit (IU) or knowledge unit (KU) called
social belief and social desire 12. These values identify the believed capability of a user
to interact with others at a certain level for a given IU or KU, and also his intention to do
so (is it a goal?). Based on this extension of the user model, a syntax has been defined to
update this model, making it possible to advice on collaborative issues such as the
selection of peer learners, the selection of tasks on which to collaborate, or the
identification of knowledge on which to exchange.
The second set of tasks we now face is to design advisors for other actors than the
learner, particularly for the trainer and the designer. The actual learner’s advisor is based
on the use of VLC viewers that help the learner use his user’s model and act accordingly.
Such tools will have to be tailored for the trainer’s role; for example, to help him make
accurate diagnosis of the learner or groups of learners, to provide meaningful pieces of
advice, to evaluate the learner’s achievements. Also we will work on an advisor for the
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designer, to help him adapt his scenario according to the characteristics of a design
project, to navigate efficiently within the learning systems engineering method (MISA)
and to assess the consistency and the quality of a learning system design. We will here
extend previous work on the Epitalk architecture 13.
Finally, we need to improve the design tools to help build such advisors. Defining the
actions and pieces of advice is a time consuming task. We believe that the approach
presented here can help automate and systematise a good part of the job. For example,
once the design of the scenarios is done using a method like MISA, the IU and KU
hierarchical lists can be produced automatically, default progress levels and updating
actions can be proposed to the designer, action and advice frames can also be proposed.
When this is done, we would like also to replace the hierarchical lists for the IS and the
KS with richer instructional or knowledge models.
Aknowledgments
The authors wish to underline the contribution of Claude Ricciardi-Rigault, Chantal Paquin, Ileana de la
Teja, Fréderic Bergeron and of all the other researchesrs who participate in the various Virtual Campus
projects at LICEF and have helped these ideas to mature. Also, a special thank to the Quebec Information
Highway Fund, the TeleLearning Network of Centers of Excellence and the Office for Learning
Technology (OLT) who have contributed to the funding of these projects.
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