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Intelligent Assistance for

Web-based TeleLearning


Jean Girard, Gilbert Paquette, Alexis Miara, Karen Lundgren

LICEF Research Centre, Télé-université

1001 Sherbrooke St. East, Montreal


Intelligent assistance in telelearning environments, based on knowledge of the learner’s activities and production, is even more important than in individualized 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, illustrated using a working JAVA implementation on the Internet.

Key words

Learning environments and microworlds, Non-standard and innovative interfaces, Student modeling.

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 need to 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 organization of activities.

What is grouped sometimes under terms like “distance education”, “on-line learning”, “telelearning”, “multimedia training” covers a multi-facetted reality in which we can identify six main paradigms:

  • The open classroom integrates multimedia technologies within a traditional setting in order to do a presentation, a demonstration or an experimentation ; it is a networked classroom giving access to campus resources and external databases or resource persons ;

  • The virtual classroom [Wilson and Mosher, 1994 ; Hiltz, 1990] mainly uses videoconferencing to support distant learners and teachers, thereby creating a telepresence type of distributed classroom ;

  • The teaching media [Pea and Gomez, 1992 ; Bourdeau et al., 1994] is focused on the learner’s workstation. It allows access to prefabricated multimedia course on a CD ROM shipped by mail or downloaded from a distant multimedia server ;

  • Web-based training [Hall 1998] is also centered on the learner’s workstation, which serves as a navigation and research instrument to find all kinds of useful educational information grouped into a web site linked with other useful sites;

  • The learning community [Harasim 1990 ; Rigault et Henri, 1990] uses the workstation, not mainly as a way to access information, but as a communication and collaboration device using asynchronous tools such as electronic mail or computer teleconferencing, sometimes coupled  with synchronous activities such as desktop videoconferencing, whiteboards and application sharing;

  • The performance support system (EPSS) [Gery, 1997] provides task-oriented training modules that are added to an integrated support systems within a workplace. Information has a “ just in-time ” quality and training is seen as a complementary process integrated with the work processes.

Each of these models has advantages and drawbacks. The first two models are very popular at the present time but it is difficult to see how these approaches can meet the Education challenge in the knowledge society. We cannot hope to solve such a huge problem by simple information transmission, while busy and mobile persons have to learn to use ever growing masses of information and develop cognitive abilities, interaction capabilities and creativity at a much higher level than what was required in the past. Approaches using “the teaching media ” or “ Web-based training ”, expose the learner to numerous sources of information among which he/she must make choices. The inherent individualization and the non-linear learning processes in these models are a step in the right direction but they are not sufficient by themselves. Learning communities need to be supported to provide for a mix of individual and collaborative activities. Finally, the concept of a performance support system must also be integrated so that learning can be seen as an information processing and problem solving process, situated in real-life activities.

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 individualized 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. We will address some of these questions by presenting, in section 2, the model of a Virtual Learning Centre (VLC) together with an implementation for Web-based Telelearning environments. We will give an example of such an environment and show how the VLC supports learning in this case. Section 3 will address the question of assistance in a VLC, presenting a typology of assistance resources, and some methods and tools to build an advisor for a web-based Telelearning environment.

2- Explora, a Web-based Virtual Learning Centre

The Virtual Learning Center model emphasizes the concept of process-based learning scenario coupled with assistance resources. Basically, the learner proceeds into the scenario, a network of learning activities, using different kinds of information resources to help her achieve the tasks and produce some outcome, 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 [Paquette et al, 1993; 1996; Paquette 1997] how we have built an object oriented model of a Virtual Campus using OMT methodology. Basically, we identify five theoretical actors.

The learning processes are ruled by an actor called the Learner  who transforms information into personal knowledge. “ Information ” here signifies any data, concrete or abstract, perceptible by the senses and susceptible of being transformed into knowledge. “ Knowledge ” means the information that has been absorbed and integrated by a cognitive entity into its own cognitive system, in a situated context and use. Transforming information into knowledge by the learner requires the adaptation of pre-existing mental structures or the creation of new ones, which are always integrated to the entire mental system of the learner. Knowledge so created is integrated into a usage as long as it is used in a process that allows the learner to act in his environment.

Information, the starting point of the learning process, is made available to the learner by another actor called the Informer  or content expert. The informer may be a person or a group of persons that intervene directly by presenting information to the learners. But it may also be a book, a video, a software or any other material or media that makes part of a knowledge domain available as usable learning information. These materials “mediate” some human informers who were at their origin

In a learning system, three other actors generally interact with the learner and the informer. They are the “ facilitators ” of the learner’s knowledge building process. The Designer controls the learning system engineering process, building, adapting and sustaining a learning system (LS) that integrates information sources (human informers or learning materials), communication, interaction and collaboration tools intended for the actors, as well as assistance mechanisms in the form of human trainers or computerized help programs. The Trainer basically rules the assistance and pedagogical advice process, facilitating 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  actor. Finally, the Manager facilitates learning by managing actors and events in order to insure the success of the process, based on the scenarios defined by the designer.

2.2 Interactions spaces and ressources

Depending on the specifics of a telelearning system, the different actor roles may be filled by a smaller or a larger number of people or groups of people or learning materials and resources. In traditional class training, for example, the informer, the trainer and the manager roles are most often played by the same teacher.

Figure 1 shows the five theoretical actors and their interactions. We will consider here only interactions between theoretical actors, whatever their instantiation into particular agents. We will also limit ourselves to interactions in which the learner is involved while learning, at delivery time.

Figure 1 - Actors and interaction spaces

  • Interactions between learners and designer. These are the interactions focused on the relation of the learner to the learning path into which the designer has in a way “mediated” himself in the form of a learning environment with its knowledge, pedagogic, media and delivery models. The interactions address the learners’ self-management of the learning scenarios.

  • Interactions between learner and informer. These are the interactions where the learner, individually, consults information made available by the informer and process them to produce certain results while building personal knowledge. These interactions are of course very different according to the nature of the information source (human or learning material), the media used or the type of information.

  • Interactions between learners. These are interactions using different forms of collaboration or cooperation between learners. One can study the different kinds of interactions involved in team or group management, information retrieval, problem-solving or project development. Also, interactions can be synchronous or asynchronous, face-to-face or at a distance, using different communication tool.

  • Interactions between learner and facilitator. These interactions concern the assistance that the system can provide to the learner on both the pedagogic and the management dimensions of telelearning. We can study these interactions according to the different roles played by the facilitator: animation, feedback, coaching, learner evaluation, logistics, etc. We can also study the access mode to assistance: human facilitator on-line or in presence, contextual help in the learning environment, intelligent advisor or adaptive interface. Finally, types of assistance can vary by their object: learner navigation or self-management, specific or generic information access, peer collaboration or assistance activities.

Each of these interaction spaces groups set of resources, some being used in more that one space, some being generic and other specific to a program, course or learning activity. For example, the assistance space car group help files, a list or resource persons to interact with, guides, frequently asked questions (FAQ) files or computerized advisor agents.

2.3 Host systems and HyperGuides

At delivery time, the learner and the other actors interact within a computerized 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 learning environment and a trainer’s HyperGuide within Explora

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: self-management, information-production, collaboraiton 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 ressources: traces of the learners activities for diagnosis, information on the group of learners, information on the learners production, annotation tools to identify and organize information for assistance.

These ressources are made available through an external floating palette as shown in the window on the right 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 center. It groups resources into five interaction spaces according to actors’ role and course specificity. The ressources can be generic resources developed specifically for the Virtual Learning Centre, they can be generic commercial tools or they can be specific web resources or generic (used in many courses) ones such as a technical FAQ or a virtual library.

The following table shows a distribution of ressources into the five interaction spaces, for the Job search example, for the learner, the trainer and the designer.





Path display

Self-presentation and outcomes

Time table

Learning scenarios navigator

Content navigator


Path display

Training scenarios navigator

Content navigator


Path display

Design scenarios navigator

ISD content navigator


Virtual library

Interesting web sites

Search engine


Learners’ trace

Data on groups of learners

Answers to exercices

Search engine


Information on methods

Learning systems server

Design building blocks


Text editor


Presentation software

Model editor


Diagnosis tool

Evaluation forms

Feedback questionnaire


ISA workbench

Bank of design objects


Learning system editor

Advisor definition tools





Application sharing

Desktop video conferencing

Team path display





Application sharing

Desktop video conferencing

Team path display





Application sharing

Desktop video conferencing

Team path display


Access to trainer/manager

Guided tour

Technical FAQ

Help desd

Course advisor


Access to content experts

Access to course designers

FAQ on tutoring

Tutoring guide


Instructional design advisor

FAQ on ISD methodology

Help resource persons

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 organization’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.

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 advices for a particular Telelearning environment.

3.1 Types of assistance ressources

When the designer plans the telelearning system, he must select, and if necessary develop 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 can be a starting point for the first dimension, but often the organization will distribute roles differently. For example, the trainer and the informer roles car be merged; as a consequence, there be only four assistance spaces to design. On the other hand, the designer’s roles can be split into more specialized roles such as content modeling, 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, collaboration or instructional and organizationnal 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 can be provided; by a human facilitators, access to help desks, FAQ and help files, contextual on-line help or intelligent advisor.

Because of its complexity, to design assistance in a telelearning system is a huge task. To reduce this complexity, we propose to use of 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 computerized resources. Finally, the development of generic assistance resources at the VLC level and the help of advisor building tools should help tackle the heaviest components of the assistance space.

3.2 Modeling a course

With these kinds of problems in mind, we have developed a method for learning systems engineering called MISA. 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 modeling 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 interelated 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, principles that the learner must acquire/build. Both are represented using the MOT representation system.

Actually, in the implementation presented here, we have not yet considered the full potential of such models for advising. We simplify the problem par reducing both models to hierarchical trees. The first one is the instructional structure (IS) compose of a program or a course, their main subdivisions down to terminal learning events, that is learning activities, the main components of a learning scenario. The second one is the knowledge structure (KS) compose of a main knowledge object, decompose in parts and ending with simple objects. These models, and simplified trees, can be displayed by decision of the designer, 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

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 help define conditional principles that will update the user model, display an advice or engage 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 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 this time 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 are principles stating that if certain conditions are met, the model will be updated to some belief or goal level for a 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. Also the goal level can also be shown by graying the end of a bar representing a IU or a KU.

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.

Figure 4 – A progress viewer tool in the VLC

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, look at their progress in the host environment and communicate with others accordingly. An extension of the user model has been designed by the first author. An additional couple of progress level is added to the model for each instructional unit (IU) or knowledge unit (KU) called social belief and social desire Girard and al, 1997. 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 desires to do so. 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 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 advices, to evaluate the learner’s achievements. Also we will work on an advisor for the designer, to help him adapt his scenario according to the characteristics of a design project, to navigate efficiently within the learning systems engineering method and to assess the consistency and the quality of a learning system design.

Finally, we need to improve the design tools to help build such advisors. Defining the actions and advices is a time consuming task. We believe that the approach presented here can help automate and systematize a good part of the job. For example, once the design of the scenarios are 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 mo


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 develop. 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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