Learning evolution and software agents emergence

Proceedings of ITS’96, Lecture Notes in Computer Science Series, Springer Verlag, Berlin.

Learning evolution and software agents emergence Guy Boy European Institute of Cognitive Sciences and Engineering (EURISCO) 4, avenue Edouard Belin, 31400 Toulouse, France Tel. (33) 62 17 38 38; FAX (33) 62 17 38 39 Email: [email protected] Abstract. New information technology (IT) is a major challenge to human adaptability. A crucial issue for the integration of new IT in the education system is the enhancement of its role of preserving cultural heritage, improving knowledge transferal and social integration. Software agents are computer programs that can be used to improve learning. Learning is described by five attributes: pleasure, learning how to learn, efficiency, allowing for errors in order to learn, and memory retention. These attributes guide the design of software agents that extend and support understanding, motivation, memory and reasoning capabilities. We will provide examples of agents that add pragmatics to current educational materials. They improve cooperative learning and cooperative design of pedagogical documents. These issues are discussed in the context of a critical analysis of the French educational system and the emergence of new information technology and software agents. Keywords. Software agents, active documents, pragmatics in learning systems, computer-supported cooperative learning, educational memory.

1 Introduction When I was asked to write this position paper for ITS’96, I was both extremely honored and puzzeled due to the fact that I am not a main-stream specialist on the topic. As a scientist, my fields of investigation are human factors and computer science with a particular emphasis (specialization) in the aerospace domain. As the Director of European Institute of Cognitive Sciences and Engineering (EURISCO), I coordinate applied research efforts on training in aeronautics. As a father of two children, I am very interested in the current evolution of the integration of new information technology (IT) in a local education system. In this paper, I will try to clearly distinguish between what is already known and what is plausible, but I will take the opportunity to touch on some important issues related to intelligent tutoring systems (ITSs) as I see them. The first question that comes to my mind is: what is intelligent in intelligent tutoring systems? Is intelligence in the system itself? Is intelligence in the interaction between the system and its user? Is intelligence the only capacity of people that would be enhanced by a properly designed ITS? For me, a smart system is a system that is natural to use and enhances my capabilities without too many surprises. As Norman already pointed out: Technology should serve us (Norman, 1993). When I try to figure out the evolution of basic tasks during the last two milleniums, I observe very little change content-wise. We still need to eat, sleep, work for food, take care of our children and grand parents, fight for our freedom, etc. However, the

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nature of our artifact-based activities has drastically changed. These activities have become more cognitive as the number of artefacts has increased. Human beings need to extend their abilities for reasons such as survival, knowledge increase, or conquest. If I had to give only one attribute of human intelligence, I would say adaptability. People are able to adapt to almost anything: cold and hot weather, unexpected situations, etc. They can make decisions using very imprecise, incomplete and uncertain information. Today, new information technology is a major challenge to human adaptability. A crucial issue for the integration of new IT in the education system is the enhancement of its role of preserving cultural heritage, improving knowledge transferal and social integration. It can be used for at least three reasons: • • •

to develop autonomy and individual learning; to remove barriers caused by social or geographical isolation; to open the education system to the external world and facilitate synergy with local resources.

Information technology is taken within a broad scope including its integration and use at school, home, work and public places for instance. The main issues that will be developed in this paper are the following: • • •

Will computer-mediated IT give birth to a new educational system? What would the role of human beings in this educational system be? What will be the repercussions of this evolution on the way human learn?

Humans are often the victims of new information technology because they do not assimilate or integrate it in the right way, and/or at the right time. The use of new IT leads to the creation of new cognitive functions enabling the management of knowledge and action. A major issue is the integration of computer technology with the current external memories as extensions of the human memory. Computer technology enables knowledge management and storage. The education system is certainly a good example of a generator of corporate knowledge that is reused for the benefit of students, teachers and parents. This paper introduces a concept of educational memory (EM), i.e., corporate memory (CM) for the education system. CM work currently developed at EURISCO is multidisciplinary and multidomain. It is currently focused on the construction of CM concepts for the aeronautical industry (Attipoe & Boy, 1995; Boy, 1995; Durstewitz, 1994; Israel, 1996). In many ways, CM problems encountered in the industry domain are very similar to the CM problems encountered in the education domain, even if the productivity issues are not quite the same. CM is also related to the development of Intranets. Intranets will enable massive information transfer within an organization. But they do not solve the major problem of existence or availability of the right information at the right time in the right place, and in the right understandable format. In this perspective, we propose the use of software agents as intelligent assistant systems (Boy, 1991a) that are computer programs facilitating human-computer interaction, as well as human-human communication through new IT. Agents are taken in the sense of Minsky's terminology (Minsky, 1985).

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The agent-orientation of human-machine interaction is not new. Autopilots have been commonly used since the 1930's. Such agents perform tasks that human pilots usually perform, such as following a flight track, maintaining an altitude, etc. Transferring such tasks to the machine modifies the original task of the human operator. Thus, the job of the human operator evolves from a manipulation task (usually involving sensory-motoric skills) to a supervisory task (involving cognitive processing and situation awareness skills) (Sheridan, 1992). Software agent technology enables users to center their interactions at the content level (semantics) partially removing syntactic difficulties. It also enables users to index (contextualize) content to specific situations that they understand better (pragmatics). The evolution of learning technology shows that we are heading towards the construction of pedagogical tools that add pragmatics to current educational materials. Creating software agents involves new cooperation and coordination processes that were not explicitly obvious before. I will present my view on computer-supported cooperative learning (CSCL). A specific case of cooperative learning in physics will be given. I will then focus on the requirements for an educational environment based on the construction and exchange of documents. Examples of software agents for cooperative learning will be provided.

2 How can we improve the education system in France? François de Closets describes learning in the French education system as follows (de Closets, 1996): • • • • •

learning is a job in itself: we spend at least twenty years of our life learning at school, and for some of us at university; learning is a social issue that takes a large percentage of our national budget; learning is a very precious right and an obligation: children need to go to school; learning has become a selection process that promotes the best students and eliminates the others; learning takes place during a limited period of time before entering the work place.

I would like to give a different perspective to this description of learning. In addition to the fact that learning is a necessity for survival and should be a life long experience, I propose five attributes of learning: • • • • •

pleasure; learning how to learn; efficiency; allowing for errors in order to learn from them; memory retention.

Learning should be a pleasure. It seems that rigid education systems do not encourage the pleasure of learning. We need to be motivated to learn. Today, children are motivated to buy things and to watch television for instance. They are less motivated to learn, read, discover scientific matters, discover music, etc. Adults too. How can we make people discover the pleasure of learning? Recently, the emergence

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of Internet raised new issues in this direction. People who are involved in Internet know that it is empowering to explore new sites where they can find information that they would not imagine existed. There is a need for studying the future of organized knowledge supported by new IT (Floridi, 1995). Learning should be learning how to learn. Did we learn how to learn? Do our children learn how to learn? No. In France, it is implicitly assumed as a given that learning is part of our intellectual capabilities, and learning how to learn is not necessary. Teachers should try to encourage learning how to learn by providing appropriate artifacts that help students to learn. Most of these artifacts are based on experience. They are very rarely discussed and transfered. They deal with pragmatics. We say that this teacher is good because he/she knows how to present the right artifact at the right time according to students’ needs and reactions. Students need to learn how to state problems. «!The essence of intelligence is to act appropriately when there is no simple pre-definition of the problem or the space of states in which to search for a solution!» (Winograd & Flores, 1986). Learning should be efficient. Most people stop learning when they find that it is not efficient. They usually think that they are wasting their time because they do not see much improvement in the quantity and quality of the results. Usually, students stop learning because they are not motivated and discouraged when confronted with unecessarily complexified matters. Learning should be made simple enough to afford rapid understanding. Learning should be more situational. Again, students should be pleased with themselves after completing successfully an exercise for instance. Of course, there are complex things to learn. They are often boring to learn because they are poorly presented. These should be made more attractive using metaphorical artifacts that break down the complexity. Learning should allow making errors and learning from errors. Education systems are often designed for only good students mostly because they are designed by a national elite for training the future national elite. Many students rarely make errors because they learn what they are told to learn and repeat it in the right way (like robots). They obtain diplomas and eventually become part of the national elite. The other students make errors and feel bad about them. There is no room in the education system for errors. I claim that errors are good for people. Experiencing errors is enriching and should be better investigated. There are errors that need to be made and recovery strategies that are good to practice. Life is not linear. People will experience problems all troughout their life. They should be armed to solve them. For this reason, errors are good for contextualizing learning. Learning should improve memory retention. What things that we learn should or should not be remembered? How is this possible and effective? Today, information is extremely volatile. The quantity of information that we have every day on television, for instance, is too much to be remembered in depth. People do not make effort to remember everything because not everything is relevant. In contrast, there is important information that needs to be remembered. For example, there are many people who studied a foreign language for several years at school, and who, ten years later, are unable to speak it. Memory needs to be reactivated in order to persist.

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Activation can be improved using appropriate methods. The art of memory taught by the Greeks relied on specific indexing mechanisms that I would like to recall here.

3 The art of memory and the Descartes’ dream The art of memory as an indexing mechanism The art of memory, invented by the Greeks, is not used today. People have almost forgotten it after the invention and practice of printing. This art enables someone to memorize loci (locations) and images!imprinted on his/her memory. It is usually considered as a mnemotechnique. A locus is easily remembered, e.g., a house, a balcony, an angle, etc. Images are forms, distinctive signs or symbols of things that we need to remember. The art of memory is like internal writing. Even if it is not necessary, people who know the letters of the alphabet are able to write and read. Similarly, people who know the mnemotechnique are able to put what they have heard into specific loci and repeat it by heart (Yates, 1966). If we want to remember many things, we need to have a number of loci. A major condition is that the loci must be organized into a series that needs to be remembered in order. This way, one can go forwards or backwards from any locus. Yates considers that the loci have attributes such as: put distinctive signs every five loci; create these loci in isolated places; create memory loci that are different from each other. The art of memory is a particular indexing mechanism that enables people to invent loci and images (indices) that help remember things. Emotional events tend to facilitate the formation of such loci and images. Images can be shocking and unusual, beautiful or ugly, funny or rude. Good stories create emotions that are likely to create useful indices that will facilitate remembering. «!The reason that we remember the stories teachers tell is that human memory is set up to retrieve and tell stories, as well as to capture the stories that others tell. The story is a unit of memory. Furthermore, good stories contain good images, novel ideas, or particularly poignant passages that enable our memories to create indices that make retrieval of these stories easier. Storytelling depends on being reminded of a good story to tell. And, reminding depends on having labeled the stories we have heard or have created well enough so that when those labels appear naturally in the course of a day, we can use them to find relevant stories.!» (Schank & Jona, 1991). Descartes and the mathematized world Despite the very rich background of the art of memory, our current world is dominated by rationality that was first introduced by René Descartes in 1619. The Discourse on the Method1 developed by Descartes consists of: • • •

accepting only what is so clear in one’s own mind as to exclude any doubt; splitting large difficulties into smaller ones; arguing from simple to the complex; and

1 «!Discours de la Méthode pour bien conduire sa raison et chercher la vérité dans les

sciences.!»

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•

checking, when one is done.

Highly selective exams leading to the best French Universities (Grandes Ecoles) are essentially based on the ability of students to solve complex problems using analytical conceptual tools derived from Descartes’ dream, i.e., according to Descartes, his «!method!» should be applied when knowledge is sought in any scientific field (Davis & Hersh, 1986). This trend has induced an elitist selection process where the same content is proposed to very different students from primary schools to universities. There is none or very little adaptation of the education programs to students. Students must conform or fail. For instance, modern mathematics introduces young people to set theory. They have been invented by clever scientists and technocrats. Students are now selected according to their understanding of such mathematics, but they often do not know how to calculate a simple restaurant bill. In our diploma-oriented society, there is no mercy for people who do not have the «!right!» diploma. Furthermore, people who have the right diploma are not necessarily cultivated, they may be even unemployed, and worst, they may not be happy at all (de Closets, 1996). Making two different perspectives complementary Computers are the latest tools that have emerged from this mathematized world introduced by Descartes. Today, computers are everywhere: at work, in administration, in amusement places, at home, etc. Computer games have become so popular that children have skills that many adults could not pretend to have, e.g., rapid reactions, moving target traking, computer commands discovery by exploration, etc. These are positive arguments in favor of computers. On the negative side, computers define a mathematics-based world where reality is made of simulations. People may accommodate to the simulated world and not to the real world. In the Cartesian approach, learning consists of problem decomposition. Knowledge is divided into chunks that can be learned individually. Quantitative assessments can be made. They are used to incrementally select students. In the art of memory approach, it is difficult to assess students progress using mathematical criteria. This approach deals with real world situations and problems. Investigations are performed in a open world. In contrast, the Cartesian approach deals with more academic problems dealing with closed world situations (otherwise mathematics could not be applied). New IT provides a chance to combine these two different approaches. Software agents are likely to facilitate cognitive linking between humans and machines. On the one hand, they enable people to enhance their memory capabilities with the crucial condition that people master these external memory extentions. Otherwise, there is a risk of relying too much on software agents and forgetting more than remembering. On the other hand, they provide analytical means to enhance reasoning.

4 Human-centered tools for learning and teaching Software agents: evolution, emergence and rationalization Norman (1993) refers to the motto of the 1933 Chicago World’s Fair: «!Science Finds, Industry Applies, Man Conforms.!» This was a machine-centered view that was acceptable since artefacts were built to fit people’s bodies. This was sometimes

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complex, however designers often found the right mathematics to solve related problems. Today, artefacts need to fit people’s minds. There is no mathematics for representing and solving social and cognitive problems. Norman proposes a new human-centered motto: «!People Propose, Science Studies, Technology Conforms.!» This is a new trend to make technology humane. I would like to propose an alternative view addressing this socio-technical issue: Humans and Societies Evolve, Tools Emerge, Science Rationalizes.! First, instead of talking about technology, let us talk about tools that are built to enhance human capabilities. Tools emerge from a process of trial and error. This concept of emergence is essential since isolated people have difficulties creating new tools without a social context. New tools usually emerge because they are needed and appropriate technology is available. Scientists rationalize both evolution and emergence. Scientific results are inputs for the evolution, but they are not the only ones. In particular, people’s background and external events play an important role in the evolution of humans and societies. Today, in most industrialized countries, human activities have become more cognitive. We have moved from doing to thinking. Interaction with modern tools is less energy-based, and more information-based. Human-machine interaction is mediated by computers. This interpretation is crucial for the design of new learning tools since people will need to learn how to interact with computer agents. In particular, students will need to learn how to communicate and cooperate with software agents in order to enhance learning. Evolution of computer-based learning tools The initial learning technology focused on individualized instruction, i.e., standalone tutoring. The current view has evolved to the point where training and education must support inquiry-based learning, collaboration and learning as it is integrated into doing and using. What is a learning environment? Let us try to summarize the evolution of learning technology from conventional computer-based training to cooperative learning. Computer-based training (CBT) concerns training where students and instructors use computers to improve conventional training. Each instruction method is based on a model (Boy, 1993). This model involves knowledge that needs to be learned, the student and the way knowledge will be conveyed to the student. Thus, there are at least three major issues in CBT: (1) knowledge representation and elicitation; (2) student modeling; (3) computer-student interface. Domain knowledge representation can be more or less formal according to its degree of complexity. It is important to capture student knowledge in order to improve training. In addition, student background and personality need to be taken into account. The computer-student interface should include both domain and student knowledge.

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Intelligent tutoring systems (ITS) have been studied for almost two decades. They involve CBT and include several human-like features in their software. An ITS has explicit models of tutoring and domain knowledge. It is more flexible in its system's response. The major problem in the ITS approach is due to the philosophy of the industrial age where the current model supports the fact that learning is knowledge transfer. This model does not work today because we need to be change-tolerant, as the world changes every day. In the information age, we need to go from facts to process, and from isolation to cooperation (Soloway, 1993). Interactive learning systems should enable the student to manipulate cognitive artifacts (Norman, 1992a) from several perspectives or viewpoints. Viewpoints can be shallow (interface level) or deep (interaction level). For instance, an airplane artifact can be seen from several viewpoints: a picture or a text explaining how it should be used (user viewpoint); the way it is built (engineering viewpoint); or how much it costs (financial viewpoint). Cooperative learning systems provide students with access to other people's ideas and concepts (SIGCUE, 1992). They make it possible to exchange, discuss, negotiate, defend and synthesize viewpoints. By using cooperative learning systems, we drastically depart from the usual one-directional way of learning. Students are placed in a dynamic environment where they can express their own viewpoints, and incrementally adapt initial viewpoints to more mastered concepts. In addition, cooperative learning systems are mediating tools that enhance cooperation between students, teachers, parents and other people involved in the education system. In this paper, we define a computer-supported cooperative learning (CSCL) environment as an external memory where knowledge is exchanged via electronic documents. Learning is an active and constructive social process. An essential aspect of knowledge is that it is contextualized. This is the reason why knowledge is so difficult to acquire and represent. It is vivid. The paradox is that when we think that we have formalized it (e.g., written on paper), it is already deactivated! We have to recontextualize it to adapt it to a new situation and make it vivid again. Contextualization can be seen as indexing in the sense of connecting chunks of knowledge. The contextualization process is facilitated when people learn by doing. It follows that learning technology needs to be highly interactive. Integrating software agents into active documents In section 2 of this paper, we provided five attributes to learning. These attributes can be used to design and refine software agents as human-centered tools for learning and teaching in the same way as Nielsen’s attributes to test usability of computer user interfaces (Nielsen, 1993). One way to avoid the need for extra training is to produce software agents that can be naturally used by people. The CID project is an example of integration of software agents into active documents (Boy, 1991b). Direct manipulation improves the design and use of active documents. A user-centered answer to facilitate the integration of CSCL environments is to satisfy conditions such as consistency of knowledge, internal consistency of the system that insures human reliability, context-sensitivity to

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the task, expert advice when it is needed, etc. Current documents are constructed from a variety of knowledge sources. They may have various formats according to the target and the available technology. The form and content of a document are both task-dependent (context of use) and domain-dependent (content). One of the main difficulties in designing active documents is to anticipate a very large number of contexts of use. Context of use is usually related to other entities such as situation, behavior, viewpoint, relationships among agents, discourse, dialogue, etc. Contextualization is extremely difficult using the conventional paper technology. It is made easier using computer technology when appropriate software agents are available or easy to construct. If active documents are understood by the user without external help, then they are self-explanatory. Complementary documents are commonly used to understand original documents. In active documents, explanations should be formalized and transferred into a software agent that will help the user to better understand. For instance, in physics lab exercises, diagrams are presented to the students with missing parts that the students need to add in order to complete a consistent electrical circuit. On paper, these diagrams are presented to the student with a text explanation to explain what he/she needs to do. Using the computer, the same diagrams are active, so that by clicking on each part of them, hypertextual information (text or graphics) appears and explains what to do.

5 External cognition on an example In the following example, software agents are added to existing documents to enhance their usability. Software agents provide pragmatics to the existing documents where syntax and semantics are already defined and will not be modified. This feature corresponds to the French unified school program. Even if this approach fits well with the French education system, we think that the separation of semantics and pragmatics is a general and useful concept for the design of active documents, i.e., electronic documents that include software agents. An example in physics Let us take an example of a formal course on electrical tension. In this example, we show how a conventional physics exercise can be transformed into an active document by the addition of appropriate software agents. A conventional page describing the notion of potential difference or tension follows (Figure 1). Teachers may add appropriate agents such as denotation agents that show relevant parts of graphics explained in the text. These agents associate text descriptions to corresponding graphical objects, and conversely. For instance, by dragging the mouse on the sentence "We observe a river water current", the denotation agent shows the relevant part (Figure 2). In the same way, a definition agent can be programmed to establish the correspondence between a text description and a mathematical formula. When the text "altitude difference between two points" is activated by putting the mouse on top of it, a mathematical formula appears in context. The context is defined by the corresponding picture and the denotation of the two points A and B (Figure 3).

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NOTION OF POTENTIAL DIFFERENCE OR TENSION We observe a river water current. The altitude difference between two points of the river causes the existence of a water current between these two points. In the same way, we observe an electrical current in a closed circuit. The potential difference between two points of the circuit causes the existence of an electrical current between these two points. This analogy is displayed in the following figure:

Figure 1. Basic pedagogical document.

NOTION OF POTENTIAL DIFFERENCE OR TENSION We observe a river water current. The altitude difference between two points

of the river causes the existence of a water current between these two points. In the same way, we observe an electrical current in a closed circuit. The potential difference between two points of the circuit causes the existence of an electrical current between these two points. This analogy is displayed in the following figure:

Figure 2. Use of a denotation agent.

An analogy agent gives the equivalence between various entities such as VA and Z A. By dragging the mouse on top of VA, the altitude ZA is highlighted and shows the analogy (Figure 4).

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Figure 3. Use of a definition agent.


Figure 4. Use of an analogical agent.

These are very simple software agents that enhance the pragmatics of already designed physics courses. In this particular case, agents are basically hypermedia links between objects. Objects can be overlaid on top of graphical or textual parts of a conventional document to create active documents. There is a tool box of agent types that the teacher can choose to program his own agents by analogy. Agent types can be: denotation, definition, analogy, suggestive question, problem solving

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(decomposition of a problem into sub-problems), video management, evaluation, hypermedia link to another active document, etc. Once the teacher has chosen an agent type in the tool box, a procedure helps him/her to design the corresponding agent by clicking on appropriate objects or locations on the screen. When in use, both students and teachers browse at their own speed active documents related to the lesson of the day. Individual backtracking is possible and encouraged. Eventually new agents can be created to enhance understanding of the concept to be learned. Students practice exercises by solving problems presented in active document exercises. In these documents, problem statements are put in context using agents in the same way as presented above. Suggestive questions guide the students. Hypermedia links to other relevant documents enable the student to remember concepts previously learned. An evaluation agent records students’ paths in the various active documents, as well as the answers to the questions posed. By the end of a session active documents are collected and analyzed by the teacher either on-line with the students, or off-line. An educational memory in use Typical active documents such as those described above can be exchanged between teachers, students, parents, schools and homes. An educational memory is not a dead body of information but an actively growing accumulation of beliefs that have been put together (related or not) by people involved in the education process. These beliefs may evolve with time according to tests. An active document cannot become a stable and trustworthy knowledge entity 2 until it has been adequately communicated to and approved by other people. This is a reason to enhance the educational memory interactivity both within the education system itself, and with other parties such as industry and the civil organizations. The educational memory can be seen as a large set of interconnected active documents that are logically and historically organized. This logical and historical organization is performed using contextual descriptions of the documents as described previously. It also includes a classification of software agents. This classification is incrementally acquired using a concept clustering process applied to software agents constructed by teachers. The block representation handles the construction and re-construction of such documents' organization (Boy, 1991b). Active documents should have appropriate indexing mechanisms. In the CID system, we have already developed an indexing mechanism that is suitable for incrementally updating descriptors of documents and attaching context to these descriptors. A descriptor is a partial description of the document that defines a particular semantic direction of search. A document is always partially described. This is why information retrieval is an abduction process (Peirce, 1931-1958). Abduction is the selection of a hypothesis from a predifined set. Context is added to the descriptors within a document to reduce the uncertainty characterizing the information retrieval process. Context is usually added either positively or negatively to descriptors after successful 2A trustable knowledge entity is guarantied to work in a given context of validity. This is the

case of physics formula such as Newton's law "f=ma" to measure forces at the surface of the Earth.

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or unsuccessful document retrievals. When a document is retrieved, it not only provides content knowledge, but also contextual information such as who designed it, why it was designed the way it is (design rationale), who used it, who did not like it (user feedback), etc. Let us take a scenario of active document search and reuse. First, a physics teacher decides to give a course on the notion of potential difference and tension. She decides to retrieve active documents generated by other people. She makes the assumption that using the educational memory, she will find interesting active documents that she can reuse and adapt to her course. She tries to describe what she needs by specifying a list of descriptors such as "potential difference" or "tension". After a first information retrieval attempt, she gets more than 100 active documents. She does not have time to examine the whole set. She then decides to add some context to the descriptors by specifying "tenth grade" and "physics course". She then gets 7 documents that she can browse. She sees that some of the documents mention that the evaluation feedback provided by other teachers on 4 of them is not acceptable. She decides not to consider these anymore. To decide which one of the 3 remaining documents she will keep, she reads the annotations provided by other teachers and uses the documents themselves. Once she has used the selected active document, she provides feedback information on her own use of it. She may say that some children could not understand some parts of it. Thus, she has made some modifications that are included and contextualized in the current active document. The document is returned to the educational memory. In addition, a physics teacher may design a set of software agents that he/she can send to the educational memory for experimentation. Other teachers may use them and give their feedback. We think that this is a way to converge towards a normalization of pragmatics in the teaching of physics. The main problem is for teachers to carefully annotate the active documents that they create, modify or use. In the current project, we try to better understand the human factors involved in the use of such an educational memory, as well as the underlying mechanisms that are required to support it.

6 Conclusion and perspectives In this paper, we assumed that technology is not a panacea for education. But, it can serve the proximal cause for mobilizing folks to actions (Soloway, 1995). Three main concepts have emerged: active documents, software agents and organization. Active documents are used as repositories of pedagogical knowledge. Both teachers and students should be able to easily create active documents, as well as modify old ones. To facilitate active document design and publishing, libraries of software agents need to be created and maintained 3. Software agents are observers, information processors, and proposers. They can be active entities added to conventional documents 3A major issue is the interoperability of software developed in a specific software environment.

Software agents should be platform-independent. Furthermore, the combination of objectoriented techniques (a software agent is a software object) and component-based software has some essential benefits listed by Rappaport (1995): reuse, extendibility, customization, distributability, and standardization. An example of standardization of agent-based software is given in (General Magic, 1994).

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transcribed into an electronic form. Some of them observe user's interactions. They are able to produce actions from the data they have acquired from the user. The action performed by a software agent ranges from the activation of other agents to the execution of (computational) operations. Software agents are easy to manipulate and relate to each others. They provide vivid behavior for a user interface. They can be visible (audible), or invisible (inaudible). When they are sensorial they have a presentation shape (usually called a metaphor) on the screen, or a sound, and a behavior. Otherwise, the user does not know that they exist. In the field of electronic documentation, agent adaptivity has been shown to be extremely useful in information retrieval (Boy, 1991b). In this case, software agents are knowledge-based mechanisms that enable the management of active documents. By manipulating active documents, it is anticipated that the education organization will evolve. It will produce a shareable memory that can be capitalized by the corpus of the teaching profession. In the aeronautical domain, Airbus Training has implemented a procedure used by instructors that enables them to provide experience feedback, i.e., instructors ask for improvements or corrections of flaws in training tools based on the experience they have on these tools. Experience feedback is based on positive or negative experience that is interpreted by training specialists to generate or modify corporate knowledge. A corporate memory of the description of the various pedagogical tools is maintained using this procedure. The main point of such an organization is the optimization of the end product destined for the students. The education system needs to better understand the notion of a product designed by a team of people for the needs of an evolving society. What do we want our children to learn? For doing what? Design rationale of educational products and experience feedback should be more explicit. In these conditions, teachers will be able to communicate about concrete descriptions of their pedagogical requirements by exchanging, using and refining software agents. The usability of an educational product could be tested using the learning attributes defined in section 2 of this paper. It is widely recognized that humans will experience several changes in their professional life, because of technology changes as well as job changes. Training is no longer only a matter of an initial learning phase, but has become a life-time continuous process that can be based on intelligent assistance (Boy, 1991a) or performance support. Even if initial training (including theoretical courses) enables the acquisition of conceptual frameworks, intelligent assistance based on software agents can be seen as hands-on training with the possibility of zooming into deeper knowledge. Back to the future «!Celestial navigation capitalized on the European virtues of mathematical theory and on instruments of high technological sophistication. In contrast, navigation in Oceania emphasized the deliberate refinement of people’s intuitive sense of direction and the learning of direct perceptual cues from the natural environment. From a seaman of Oceania, making a voyage is conceptualized as being within a pattern of islands, the positions of which are represented in his cognitive map!» (Oatley, 1977).

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Like the Polynesians who used dynamic cognitive maps to navigate across the Pacific more than 1,000 years ago, could we use software agents to extend our short-term and long-term memories to handle our «!navigation!» in our modern world? The art of memory may take a larger place within this framework in the future.

Acknowledgements Many thanks to Jeffrey Bradshaw, Meriem Chater, Jean-Gabriel Ganascia, Mirella Huttunen, Rachel Israel, Alain Rappaport, Stéphane Sokorski and Helen Wilson for many useful and vivid exchanges on the topic.

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