Centre de recherche LICEF, Télé-université, Montréal gpaquett@teluq.uquebec.ca
Abstract
We propose a study of interactivity and interactions in a Telelearning system from a knowledge engineering approach. The viewpoint is pragmatic in a sense that its purpose is to develop a methodology for the design of significant interactions between the actors of a Telelearning system. The viewpoint is also cognitive and pedagogic, focusing on what should go on behind the scene before the learning materials and the communication infrastructure are selected or developed. We analyze the central process where pedagogic scenarios are built according to a knowledge and skills model, to derive a set of prescriptions for designers, to help them build meaningful interaction capabilities into their Telelearning systems.
Interactive multimedia is not a new phenomenon, but the recent dissemination of the CD-ROM and the Internet has opened new possibilities for education that are not yet fully understood, while its potential is arising more and more interest.
New interaction possibilities and pitfalls
The inherent interactivity of networked multimedia is a welcome contrast to the passivity of traditional audio-visual material, delivered face-to-face or at a distance. But the enormous challenge to fully uncover the educational potential of networked multimedia is rising many questions: what kind of educational process, what kind of tools, what kind of media, what kind of delivery model, and finally what kind of interactivity can we expect?
Invited Conference at TeleTeaching 98, IFIP World Congress, Vienna-Budapest, August 1998 1

These questions bring us back to the necessary priority to be given to pedagogy in its relation to technology and media.
Currently, a popular delivery model is the Virtual classroom based on real-time videoconferencing: the teacher is in front of a camera, having some multiplexing facilities to display fixed or video images, and she is broadcasted to a number of distant locations where the learners receive this information. This model requires costly equipment as well as the learners’ and teachers’ simultaneous presence; more fundamentally, it limits the learner’s interactions and initiative down to a level that can be as low (some would say lower) as what happens in large auditorium courses.
Another form of TeleTraining has been used for decades in open universities or workplace institutes.
The self-training delivery model is based mainly on printed documents and, occasionally, on audiovisual and software materials transmitted to the learner by mail. Using this individual training “kit”, the learner achieves the learning activities proposed to her, with minimal support from a tutor using phone, fax or email. More recently, some course materials are distributed on interactive CD-ROMs or on Web pages but this new development does not fundamentally change the basic model.
While more respectful of the student’s pace and style of learning, this model is often characterized by the loose integration of the media and their low interactivity. The inherent rigidity of printed, audiovisual or CBT documents precludes frequent adaptation of the course material. More important, the dominant pedagogic paradigm is based on the transmission of information, an approach more and more obsolete in the knowledge society where people need new skills to acquire, process and communicate information. Furthermore, the mainly individual learning scenarios isolate the learner from collaborative activities that could support her persistence in the course, while preventing the development of essential interaction and knowledge acquisition capabilities.
One must also question the use of multimedia environments. Let us take the example of chess programs providing a virtual player, some of them now on the web, together with game analysis and strategic advice to the human player. These systems can maximize significant learning interactions in the domain of chess and also help towards the development of generic problem solving skills.

However, certain of these programs embed multimedia capabilities as a spectacle. For example, when a piece is to be taken, one must assist in a combat between the attacker and the defender, until the latter “dies” and disappears from the screen. The media quality of the scene is quite fascinating the first time, but at the same time, one has a funny feeling of awkwardness, being totally distracted on what is supposed to be going on. This “media noise” reduces the level of productive interaction with the program and, as a consequence, impedes the learning potential of the activity.
Shifting paradigms
These few examples only illustrate some of the possible negative impact of particular use of networked multimedia. While TeleLearning is an essential solution to the exponential growth of knowledge in our societies, its widespread use is still limited by the methods and the tools actually available to the designers and the users of distance education.
This is a global challenge, where pedagogical, technical, social and economic dimensions must be fully taken into account to design the new distance education tools, methods and models needed to shift some of the actually dominant paradigms:
 from sequential or hierarchical “page turners” to real semantic networks of information closely related to a knowledge model of the domain and generic skills to be learned;
 from transmission of information to actual processing of information by the learner to solve problems, achieve situated tasks, simulate a process, develop a project;
 from strictly individual learning to collaborative team work, group forums and debriefing discussions to establish a synthesis of a domain knowledge;
 from flashy media presenting impressive technical display to images, sounds and data well integrated into a structured representation assisting learners in their knowledge building activity.
An engineering viewpoint on interactivity
Our viewpoint on interactivity in Telelearning systems is first an engineering viewpoint. We are mainly interested in the processes and methods enabling designers to build fully interactive systems that can be recognized heuristically as having as fair probability to enhance fruitful learning. In the
Invited Conference at TeleTeaching 98, IFIP World Congress, Vienna-Budapest, August 1998 3

first section, we will define what we mean by a Telelearning system and the engineering of such a system. We will then identify five theoretical actors that interact in such a system. This will give us a framework for categorizing and discussing interactions.
Next, we will focus on the cognitive and pedagogical dimensions of interactions. We will deliberately put aside interesting questions like screen displays, quantity of information, types and norms for media elements and other media centered decisions. Even though such decisions are related to pedagogic goals and are important quality issues for learning materials, we believe they must be subordinated to more fundamental decisions. Consequently, in the second section, we will center the discussion on the fundamental process of knowledge and skills modeling and its use in designing pedagogical scenarios. This is where the interactions between actors of a Telelearning system are defined at a macro-level.
Then we come to a point where a set of heuristic principles can be defined. In the last section we will outline a set of principles, based on the framework described in section 1, to help in the design of interactions in a Telelearning system. The questions of the navigational and self-management structure of the knowledge model and the pedagogical scenarios, of information access and processing by the learner, of interaction between peer learners and, finally, of interactions between learners and trainers or managers will all be addressed.
We will first present briefly an Instructional Design method called MISA, developed at our research
Center
1
, that will help us define a framework for the study of interactions.
1 MISA, the Telelearning systems engineering method presented here, aims to apply cognitive science principles to the field of instructionnal design of learning systems. MISA is particularly well suited to the design of technology-based telelearning and self-training environments but is however intended for designing any general learning environments. It results from a five-year effort that produced the first version of the method as a computerized workshop. The method was thereafter validated with instructional designers and content experts in nine organizations and was rebuilt according to results and observations gathered during the validations. [7,8,9].

1.1 From Instructional Design to Telelearning Systems Engineering
Instructional Design is a complex set of processes of human communication since many specialists contribute: content experts, pedagogy specialists, media specialists and management specialists. But first of all, it is a complex problem solving process as defined in cognitive sciences [4] and sometimes studied as such in the Education Sciences field [3,11,12,14]. Instructional Design (ID) can be defined as a methodology that aims to resolve a particular class of problems: training and learning problems [2].
The MISA method presents the ID processes and tasks according to an engineering perspective analogous to software engineering. It is a complex process decomposed at several levels, into subprocesses. Each sub-process has its inputs and its products 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, a learning material model and a delivery model. These four aspects of a learning system are clearly differentiated but they also are interelated through specific associations making the engineering process visible and structured, thus facilitating quality control of the processes and their products.
The learning system concept produced by MISA is quite comprehensive. It makes it possible to develop single learning events (course, module, activity) as well as to develop complex networks of learning events such as a program or curriculum composed of several courses. The methodological approach aims to take into account all types of academic, industrial or business training without prejudging the types of media support (print, audiovisual, multimedia, tutorial, teleconferencing, telematics, computerized advisor system) needed to facilitate learning, nor the tools or technological and organizational infrastructures necessary to use them.
As shown on figure 2, a learning system is composed of the learning system blueprint and the
“physical” learning system that includes the learning, training or administrative material developed according to the blueprint.
The blueprint of a learning system is itself composed essentially of four models:
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
A knowledge and skills model represents graphically the learning system’s content; sub-models are assigned to the different learning events or learning units that are the core of the pedagogical model;

A pedagogical model represents graphically the network of learning events (programs, courses, modules, activities) and the learning units, the smallest learning events for which we define a learning scenario for a target population and a corresponding training scenario for the facilitors
(trainers, tutors, presenters, managers). The scenario is a smaller network grouping learning activities and resources available to the learners or facilitators as input or products of the activities, together with precedence links or path constrains specification.
LS engineering
I/P
Learning system
I/P
LS delivery
Knowledge and
skills model
C
Learning system blueprint
C
C
C
Media model
I/P
C
Develop the learning materials
I/P
"Physical"
Telelearning
System
I/P
I/P
Pedagogical model
I/P C
C
Associate knowledge and skills to
Learning units
I/P
Learning units
C
Learning scenarios
C*
C
Training scenarios
Learning events network
Delivery
Model
Tools and
Teleservices specifications
C C
Technological infrastructures specifications
C
Organizational infrastructures specification
Figure 1 - Learning system concept

A media model defines how resources are assembled into pedagogical materials on different types of support, outlining the structure of these materials: storyboard, metaphor, units and media elements, organizational and media rules, as well as links to the technological tools and

infrastructures that permit their use. It prepares directly the development of the “physical” learning system.

Finally, a delivery model defines specifications for teleservices, tools, network infrastructure, as well as organization principles for group composition, evaluation management, implementation and maintenance of the learning system.
1.2 Actors in a telelearning System and their interactions
In a TeleLearning system, five theoretical actors
2
interact: the learner, the informer, the designer, the trainer and the manager.
The learning process is 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 the origin of the material’s information content
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
2 For more details on LICEF’S Virtual Campus model, see [6]
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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.
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. In traditional class training, for example, the informer, the trainer and the manager roles are most often played by the same teacher. At Téléuniversité, the professor is mainly a designer and also an informer towards other facilitators such as the tutor or the manager.
1.3 A Framework for analyzing interactions and interactivity
The preceding discussion is very useful to identify the types of interactions that can be integrated in a fully interactive telelearning system. 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.

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 system with its knowledge, pedagogic, media and delivery models. The interactions address the learners’ navigation in the learning system, but also the self-management of the learning scenarios by the learner.

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 being accessed.


Interactions between learners.
These are interactions using different forms of collaboration or cooperation between learners. Interaction in small teams focused on achieving a task is very different from those involved in discussion forums. One can also study the 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 tools and infrastructure.

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.
We now turn to a central part of the MISA methodology focusing on the construction of TeleLearning
Scenario. This discussion is necessary in order to give us a basis on which interaction design principles can be developed.
2.1 A Central Telelearning system engineering process
From our viewpoint on interaction, the central engineering process is the one that builds a pedagogical model from the analysis of the specific domain knowledge and the generic skill model.
Let us first summarize the process in five major steps:
1.
The learning system is decomposed into a network of learning events down to the smallest events called learning units.
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2.
With each learning unit is associated a knowledge and skills sub-model representing the content to be acquired by the learner; this sub-model organizes knowledge around a main knowledge unit and a generic skill associated to this main knowledge unit.
3.
With this skill is associated a generic process; this generic process can be chosen and adapted from a library of generic process descriptions associated to each component of a taxonomy of skills such as the one presented below.
4.
The procedures in a skill’s process description, with their inputs and products, form the backbone of the learning scenario; later on, specification of collaborative and evaluation activities, as well as different types of resources for activities will be added to obtain a complete scenario.
5.
The principles ruling the procedures in a skill’s generic process will form the backbone of the facilitator’s or assistance scenario; afterwards, different ways to gain access to assistance resources will be define.
This general process is of course non-linear. Its goal is to provide a systematic approach for pedagogical scenario building, based on knowledge and skills development. Before we use it to define interaction principles, let us illustrate the different steps of this process.
2.2 Knowledge and skill model of a learning unit
Let us examine a very simple learning unit on the subject of searching information on the Internet.
We will first associate to this learning unit a knowledge and skill sub-model of a course global model, displayed on figure 2.
We use a representation technique and a graphic editor called MOT
3
. In it, knowledge units are classified into facts, concepts, procedures, principles and generic skills, each represented by different shapes holding the name of the knowledge unit. These figures are related by seven types of links:
3 MOT, a knowledge representation system using typed objects and links has been developed specifically to facilitate the engineering of learning systems. Its originality likes in the integrated view of three abstration levels: facts, abstract knowledge (concepts, procedures, principles) and metaknowledge including the notion of “generic skills”. For a discussion of this representation system see [5,9]

specialization (S), instantiation (I), composition (C), precedence (P), input-product (I/P), regulation
(R) and application (A).
The main knowledge unit on figure 2 is a procedure (oval shape). It defines the main purpose of the learning unit « Search for information on the Internet ». The main procedure is decomposed into subprocedures (using C links). One of them is “Execute the request”: it has a « Request » input concept
(rectangle shape) and produces (I/P link) a list of « Interesting Web sites ». These are in turn used as inputs to another sub-procedure, “Identify interesting information”, which precedes (P link) the final procedure: “Transfer information in a text editor”. The “Refine the request” sub-procedure is regulated (R link) by principles helping a user to refine a request. Finally, an application (A) link on the main procedure shows that the learner will have to exercise a generic skill: «Simulate a process».
Search subject
I/P
Build a search request
Simulate a process
P
Open a browser and a
search engine
I/P
I/P
Request refinement principles
Request
A
Search for information on the Internet
I/P
Information
identified
C
I/P
C C
C
I/P
R
I/P
Identifiy interesting information
Refine the request
P
Transfer information in
a text editor
I/P
I/P
Interesting
Web sites
Execute the request
Figure 2 – An example of a knowledge and skills submodel.
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2.3 Representing skills as generic processes
Every generic skill is chosen from a taxonomy of skills
4
such as the one displayed on the next table.
According to this taxonomy, « Simulate a process » is a variant of the reproductive application skill.
Each of the skills in this taxonomy is described very precisely by its inputs and its products and by a detailed generic process showing how the inputs are transformed into the products. The “Simulate a process” skill is compared below to the “Construct a process” skill which is a variant of the creative, modeling/construct skill in the taxonomy.
First level skills
Receive
Reproduce
Produce/create
Self-manage
Second level skills
Focus attention
Integrate
Instantiate/refine
Transpose/translate
Apply
Analyze
Repair
Synthesize
Evaluate
Self-control
Third level skills
Identify
Memorize
Illustrate
Discriminate
Make explicit
Use
Simulate
Deduce
Classify
Predict
Diagnose
Induce
Plan
Model/Construct
Initiate/Influence
Self-adapt/Control
4 This taxonomy is an attempt to synthetize work in artificial intelligence on active metaknowledge [10], in software engineering such as KADS generic tasks [13], with work in the education sciences on the taxonomy of educational objectives [1] and the taxonomy of knowledge and skills [12]. For a detailed description of this, see [5]

Skill
Simulate a process
Construct a process
Input
A process, its procedures, inputs, products and control principles.
Definition constraints to be satisfied such as certain inputs, products and/or steps
Product
A trace of the procedure : set of facts obtained through the application of the procedures in a particular case
A description of the process: its inputs, products, sub-procedures with their input and output, and control principles.
Generic process
-Choose input objects
-Select the first procedure to execute
-Execute it and produce a first result
-Select the next procedure and execute it
-Use the control principles to control the flow of execution
-Give a name to the procedure to be constructed
-Relate it to specified input and product
-Decompose the procedure
-Continue to a point where well understood steps are attained.
From these descriptions of the two generic skills, we can see that a pedagogical scenario on the same subject of “Information search on the Internet” but with a different skill objective such as “Construct a process” would be very different from the one based on the “Simulate a process” skill. In the first case, a kind of walk-through of the process is sufficient, while in the second case, we could need a project-based scenario where learners and engaged in a more complex problem-solving activity.
Process description
I/P
Simulate a process
I/P
Exectution trace of the process
I/P
Produce an instance of
each input object to the main procedure in the process (the case to simulate)
I/P
Inputs to the first procedure
I/P
Process analysis principles
R
Find the next applicable terminal procedure that can act on the available products
Execution principles specific to the process
R
P
Execute the procedure according to any principles that
applies to it
Trace description principles
R
I/P
Assemble the trace: results from the execution path
I/P
Products of the procedure
I/P
P P
P
P
There are other low level
procedures to execute
There are no more low level
procedures to execute
Figure 3 – Graph of a generic process: “Simulate a process”
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The description of both processes in the preceding table is however just a summary, insufficient to lead to a precise pedagogic scenario. The MOT graph in figure 3 provides a more precise definition of the “Simulate a process” skill, adding to it more details such as inputs and outputs of sub-processes, together with some of the control principles that heuristically could help achieving the generic task associated with the skill.
Still these principles would have to be stated to make the skill’s description more accurate. For example, the “Process analysis principles” would state conditions to identify applicable “next procedures”. On the other hand, the “Execution principles specific to the process” would state conditions to recognize execution principles for each procedure in the specific process being simulated. Finally, the “Trace description principles” would control the trace assembly sub-process by stating conditions to identify and present the generic skill’s products.
Although we have shown only three groups of principles in the generic process above, each procedure or concept in it could potentially lead to other sets of principles. Together, all these principles could form what we could call a “generic strategy” regulating the generic process. This strategy can afterward be use by the learners to help in the execution of the skill and/or by the facilitators to assist the learners in this process.
2.4 Building a learning scenario from a skill’s generic process
We will now use the preceding generic process to build a learning scenario where learners will simulate the “Search information on the Internet” process. To do this, we lay out a graph loosely corresponding to the generic process. As shown on figure 4, the graph is instantiated in a way that the vocabulary of the specific application domain (the Internet) is used. It is also formulated in an learning activity “assignment style” displaying seven activities.
Note that globally, based on the generic process input and product, the scenario starts on a description of the process to simulate and ends on producing essentially a trace report. The following table shows the relation between the learning activities in the scenario and their correspondence in the generic process description. Note also the loop between activities #4 and #5 that can only end when the learner is satisfied according to certain quality principles that would has to be stated.

Information on the process
"Search for information on the Internet"
I/P
Activity #1
Open a browser and a search engine
Activity Report
C
I/P
Subject
I/P
I/P
Activity #2
Choose a subject
Activity #7
Report on your search process
Information on what is a request
I/P
I/P
Activity #3
Build a first search request on the subject
Activity #5
Refine the first request
I/P
I/P
Request
I/P
I/P
Web sites identified
I/P
Activity #4
Execute a search request
I/P
P
Activity #6 Transfer
interesting information in a text
editor
Figure 4– A learning scenario: simulate the “Search the Internet” process
Activity in the scenario Correspondence in the generic process
Activity #1: Choose a subject
Activity #2: Open a browser and a search engine
Activity #3: Build a first search request
Activity #4: Execute a search request
Activity #5: Refine the first request
Activity #6: Transfer interesting information
Activity #7: Report on your search process
Inputs to the overall process, defining the case to be simulated
First applicable procedure to execute
Next applicable procedure to execute
Execute the chosen procedure
Next applicable procedure
Next applicable procedure to execute
Assemble the trace
Of course the scenario is not yet complete. For example, we could add resources that help learners achieve their tasks, such as a tutorial on the structure of a request or on a final report form. Also, we
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might specify some collaboration assignments and maybe a description of the evaluation principles that will be use to note the learner’s work.
But the important thing here is that the generic process will form the backbone of the learner’s assignments. In that way, we make sure that he exercises the right skill, that is simulating a process, while working on the specific knowledge domain.
From the preceding discussion of the scenario building process, we will now state a set of heuristic principles for engineering telelearning systems based on the framework outlined in section 1.
3.1 Learner-designer interaction for navigation and self-management
We will first examine the interactions between the learner and the designer actor. At delivery time, the designer is essentially mediated through the knowledge model and the pedagogical model he has built into the Telelearning system. From an engineering viewpoint, this kind of interaction corresponds to the learner navigating into the data (specified by the knowledge model) using the process proposed in the learning scenarios.

Principle 1 - The knowledge model in a learning unit must be of sufficient grain size and well
structured using precise links. This first principle is very important to start with because small knowledge units, isolated knowledge units or a list of unstructured subjects will not leave enough room for meaningful interactions. A learning unit must generally group a sufficient number of interrelated knowledge units: not a single small concept, but a concept with its main components and the procedures where it is used; not a single small procedure but also its inputs, products and control principles; not a single principle, but a set of related principles linked to the procedures they regulate or the concepts they define implicitly.

Principle 2 - Knowledge in a learning unit must be related to skills. Knowledge specific to a domain and metaknowledge (knowledge about knowledge) are being constructed at the same time

10,12

. A learning unit without an associated skill is like a set of data without any process acting

on it. A skill brings dynamism to the knowledge to which it applies, favoring deeper interaction of the learner with the knowledge assigned to the learning unit.

Principle 3 - The learning scenario in a learning unit should be described as a generic process, corresponding to the skill associated to the learning unit.
The learning scenario must propose problem to solve, tasks or projects to achieve, instead of static knowledge to contemplate. In other words, it must engage the learner in information processing activities directly related to the skills in the learning unit’s knowledge model. For example, if we want to develop skills like classification, diagnosis, induction or modeling, we should propose classification, diagnosis, induction and modeling problems or projects to the learner.

Principle 4 - Scenarios must be open to different learning paths.
The information process in the scenario should not be too linear or detailed to a point where the learner cannot develop his own strategies, decision or management principles. For instance, the activities can be structured in the form of a network where the learner can follow different paths. Then, an important interaction will consist in the learner managing the succession of his activities within the scenario. Help to the learner should be given contextually by the assistance system though coaching or contextual help instead of being built in the activity assignments. In that case, the scenario might be centered too much on the designer or the trainer, thus reducing the level of interaction of the learner with the knowledge.

Principle 5 - Scenario must be adaptable. To go further in that direction, the scenario can be designed with adaptability principles. For example the learner, alone or with the help of a trainer, could be authorized to rearrange the order of the learning activities, to add or delete some of the resources to be used and finally, to adapt the collaboration or the assistance modalities provided in the scenario description. That way, the learner constructs his own personalized scenario. These interactions with the scenario encourage the learner to evaluate regularly his progress, to selfdiagnose his results and sometimes, to radically redefine his strategies. This metacognitive activity is essential to learning in a specific domain, but also to learn how to learn better.
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3.2 Learner-informer interaction for information processing
Lets us now examine the interactions of the learner, individually, with the informer actor being essentially mediated in the teletearning system by the learning materials and, from time to time, by an on-line professor. From a system engineering viewpoint, this type of interaction corresponds essentially to information acquisition that triggers information processing activities through wich the learner can construct personal knowledge and also, communicate transformed information to others.

Principle 6 - Learning scenarios must propose rich and diversified information sources. Search for information is a significant learning activity. It is important that the learner be offered a sufficient diversity of information sources, not only to “cover” the knowledge or different learning styles, but also to be engaged in an information selection process to choose the information most related to the work to be done in the learning activities. This implies enough resources to cover the learning unit’s knowledge model with a certain level of redundancy and a variety of media, to let the learner choose interaction modes suited to her needs.

Principle 7 - Information resources must provide for bi-directional communication. One-way information transmission face-to-face or integrated into interactive multimedia or Web sites is a limited way to provide for information. Bi-directional information services between the learner and the information source can be often more effective. A good example is a teleservice called
FAQ (frequently ask questions). This asynchronous on-line service is generally managed by one or many content experts. In relation to his learning activities, a learner can ask for information through the FAQ. His request is matched to a FAQ list. If it is there, he gets an answer immediately. If not, the content experts will look up the request list regularly to check for unanswered questions and provide for new answer to be added to the bank.

Principle 8 - Learning scenarios must define clear information goals. The large diversity of information sources can decrease the motivation of a learner in her quest for useful information.
To counter this “lost in space” effect, it is essential that the learner has a clear view of the information she is looking for. Such a target can be given in the learning activity assignment or by a list of promising information sources provided by an informer. However, this list should not be seen by the learner as restrictive, preventing useful interaction through information search outside the initial list.


Principle 9 - The scenarios must offer tools for information search, annotation, and structuring.
Once the information search is started and the resource base has been identified, information search, annotation and structuring tools can be assigned to activities in the learning scenarios where they will be particularly useful. Search tools help the learner filter the right information according to the goals of the search. Annotation tools help assign short texts to a document where the learner comments on its usefulness for her activities. Information structuring tools can take the form of a graphic editor where the learner builds a knowledge model grouping the information he has retained with references to the source of this information.

Principle 10 - The Telelearning system must offer production tools well adapted to the task involved by each learning activity.
The most crucial part of the learner’s interaction with information is the one where he will process the information to build a product of a learning activity. For this, he must have application tools of different kinds, well adapted to the task such as text editors, spreadsheets, DBMS, presentation, planning or modeling tools. The choice of tools associated to a learning unit depends very much on the skill, the generic process on which the activity is based. For example, a planning generic process will necessitate a spreadsheet or a project management tools, while a taxonomy construction process will command a graphic modeling editor
3.3 Learner-learner interaction for peer collaboration
The interaction of a learner with her co-learners adds the important collaborative dimension to a
Telelearning system. From a systems engineering viewpoint, these interactions concern essentially the management of different agents in a learning activity, the distribution of responsibility between them and their coordination.

Principle 11 - In a learning scenario, collaborative and individual activities must sustain and
build on one another. Team or group collaboration provide for important learning interaction where the learners, through communication with equals, can express and clarify their evolving ideas, validate their emerging hypotheses and enrich mutually their growing solutions, models and theories. These collaborative interactions will be richer if they are well prepared individually by
Invited Conference at TeleTeaching 98, IFIP World Congress, Vienna-Budapest, August 1998 19

each participant, and if they are followed by other individual activities where each learner can investigate by herself more thoroughly the team or group conclusions.

Principle 12 - The collaboration modes must be adapted to the generic process that characterizes a learning unit.
The mode of team interaction and coordination depends strongly on the generic process retained as a basis for a learning scenario. For example a generic process like induction can lead to a collaboration mode where the learners will take charge individually, or with a colearner, of certain themes, followed by a partial synthesis in small teams, and finally by a thorough discussion by the whole group. On the other hand, a collaborative problem-solving activity should match the general resolution process: it could start by a discussion in small teams to analyze the problem statement, followed by a brainstorming to generate possible solutions to the problem. Afterwards, each solution approach could be distributed for analysis to individual or two-person teams. A group discussion would finally end the process, aiming to identify all the valid solutions.

Principle 13 - Collaboration must use well-coordinated synchronous and asynchronous
interactions. Real time interaction, in presence or at a distance is useful to start a collaboration process and, from time to time, to manage the learning activities or to exchange ideas using all the sensorial attributes of face-to-face or telepresence meeting. However, synchronous interactions have their limits. They consume a lot of time during which many participants can become passive or unmotivated. More fundamentally, they require an immediate and spontaneous response which forms the essence of their value for learning. But not all participants are equally suited for such interaction, and certainly, very few are able to engage in it at the same time, without being given time to analyze the others’ statements. So the learning scenarios should assign the largest amount of time to asynchronous activities where the learners communicate with each another over a longer period of time, using different tools like email, computer assisted teleconferencing, audio and video mail, when they are ready for interaction. Asynchronous interaction between learners should be the default mode, punctuated by a certain number of synchronous meetings at strategic moments.

Principles 14 - The collaboration model should identify management activities and tools for peer
coordination. Fruitful interaction between learners cannot occur without well-defined

management activities and tools such as group agendas, communication software, groupware, individual and collaborative work plan viewers or self presentation materials made available to all learners. Assistance with collaboration from the facilitators must also be well planned in the system. For example, a trainer acting as a group discussion animator, in synchronous as well as in asynchronous meetings, is absolutely essential for meaningful interaction. A manager can also help learners form subgroups and synchronize their individual and collaborative activities.
3.4 Learner-trainer/manager interaction for assistance
Finally, we state some principles regarding the interaction of the learner with the facilitators, the trainer and the manager actors. Facilitators are resources providing help and assistance to the learning process, whether it is given by mean of a person or integrated to the computerized Telelearning environment in the form of help files, a contextual help system or an intelligent advisor or tutoring system.

Principle 15 - Assistance interactions in a learning scenario must correspond to principles
regulating the corresponding generic process. The assistance resources should be directly associated with each procedure in the generic process associated with a learning activity. For the example of the preceding section, the assistance would be based on principles helping to simulate a specific process, showing how to analyze the specific process to be simulated, choose appropriate input objects, select appropriate procedures to apply, and finally show how to assemble a trace of the simulation of the specific process. These principles define the interventions of the trainer into the learning scenario. They can be used by the learner in different ways: as a check list to consult from time to time, as an intelligent advisor tracing the learner and providing advice to him based on one of the principles, or as a guide to the trainers’ or manager’s interventions towards the learner.

Principle 16 - Provide assistance scenarios with multiple facilitators.
It can be counterproductive to rely on a single assistance mode. For example, a unique human trainer can become omnipresent or ill-synchronized to the needs of certain learners, both situations reducing the quality of interaction. At the other extreme, a completely computerized assistance system can
Invited Conference at TeleTeaching 98, IFIP World Congress, Vienna-Budapest, August 1998 21

frustrate some learners who will not necessarily find the level of help they need. A combination of assistance from human facilitators with help from the computerized environment is preferable in most cases. The help or intelligent advisor system can offer a first level assistance readily available that that can lead to deeper, but less frequent, interaction with a human trainer or manager. In a Telelearning system, it is often fruitful to give special roles to human tutors. One tutor can give advice on the sequence of activities of each learner or team, while another gives advice on the use of learning materials and tools, the third acting more as an animator for group discussions and other collaborative activities.

Principle 17 - Assistance should be given carefully, mainly at the learner’s initiative. In the planning of a Telelearning system, it is important to design the assistance sub-system in a way that the trainer or manager will not constantly disrupt the learning activity. A facilitator or an assistance resource should intervene, most of the time, when being requested by the learner. But he must also be provided with a set of intervention principles that indicate when to intervene at his own initiative, especially in certain extreme cases occur where the learner is heading for directions that could compromise the quality of learning.

Principle 18- The type of guidance from the assistance system should be mainly heuristic and
methodological. An algorithmic guidance corresponds to principles so specific and precise that they try to take in account every possible move that could lead the learner to error, thus preventing her from making strategic choices. A typical example of this is the so-called
“problems” which are in fact exercises where a unique solution is achieved by a systematic application of precise rules.
At the other extreme, a heuristic form of guidance orients the facilitator’s intervention towards methodological help suggests to the learner fruitful ways towards a solution. Typically, the human trainer or the intelligent advisor system will suggest the building of tables or graphs, to decompose the problem or more generally, to propose an analysis process based on his knowledge of the generic process on which the learning scenarios are based.
Conclusion
At the end of this presentation, the author hopes to have convinced the reader of the interest of an engineering approach, based on knowledge representation of the content and the learning activities, to

help analyze and plan interactions behind the scene, to build more meaningful interactions into our
Telelearning systems.
[1] BLOOM Benjamin S. Taxonomy of Educational Objectives: the Classification of Educational Goals. D. McKay, New
York 1975.
[2] GOËL V., PIROLLI P. "Design within Information-Processing Theory: The Design Problem Space", AI Magazine,
Spring 1989.
[3] MERRILL D. Principles of Instructionnal Design. Educationnal Technology Publications, Englewood Cliffs, New
Jersey, 1994.
[4] NEWELL A., & SIMON H,. Human problem solving. Englewood Cliffs, NF: Prentice-Hall, 1972.
[5] PAQUETTE G. Représentation des méta-connaissances, applications en ingénierie des systèmes d’apprentissage »,
Rapport de recherches du LICEF, janvier 1998.
[6] PAQUETTE G. The Virtual Campus : Virtual Learning Centres for XXIst Century Organizations. Proceedings of the
IFIP Workshop on the “Virtual Campus” (Ed: F. Verdejo), Chapman and Hall, 1997.
[7] PAQUETTE G., AUBIN C., CREVIER F. Design and Implementation of Interactive TeleLearning Scenarios.
Proceedings of ICDE’97 (International Council for Distance Education), PennState University, USA, June 1997.
[8] PAQUETTE G., CREVIER F., AUBIN C. ID Knowledge in a Course Design Workbench. Educationnal Technology,
USA, volume 34, n. 9, pp. 50-57, November 1994.
[8] PAQUETTE, G., GIRARD J. AGD: a course engineering support system, ITS-96, Montréal, June 1996.
[9] PAQUETTE G. La modélisation par objets typés: une méthode de représentation pour les systèmes d’apprentissage et d’aide a la tâche. Sciences et techniques éducatives, France, avril 1996.
[10] PITRAT J. Métaconnaissance, avenir de l’Intelligence Artificielle. Hermès, Paris, 1991.
[11] REIGELUTH C. Instructional Theories in Action: Lessons Illustrating Selected Theories and Models. Hillsdale,
NJ, Lawrence Earlbaum, 1987.
[12] ROMISZOWSKI A. J. Designing Instructional Systems. Kogan Page London / Nichols Publising, New York, 1981.
[13] SCHREIBER G., WIELINGA B., BREUKER J. KADS – A Principled Approach to Knowledge-based System
Development. Academic Press. San Diego, 1993
[14] TENNYSON, R D. Cognitive Learning theory Linked to Instructional Theory. In Journal of Structured Learning,
Vol. 10(3): 249-258, 1990.
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