John Self
email: jas@cbl.leeds.ac.uk

The defining characteristics of intelligent tutoring systems research: ITSs care, precisely

This paper argues that, despite the changes in philosophies and techniques that have occurred since ITS research began, there are continuous threads running through this research which define its essential and distinctive nature. In particular, ITSs are computer-based learning systems which attempt to adapt to the needs of learners and are therefore the only such systems which attempt to 'care' about learners in that sense. Also, ITS research is the only part of the general IT and education field which has as its scientific goal to make computationally precise and explicit forms of educational, psychological and social knowledge which are often left implicit.

INTRODUCTION

The aim of this paper is to review the evolution of the defining characteristics of intelligent tutoring systems research, namely the search for compassion and precision in the design and implementation of computer based learning systems. It is easy to get a superficial view of the way the ITS field has changed. We could, for example, just list the most commonly used keywords in the titles of papers at the ITS 88 and ITS 98 conferences: ITS 88: expert system, student modelling, problem solving, architecture, planning, ... ITS 98: training, agents, student modelling, learning environments, collaboration, ... This immediately tells us that we have moved on from rather general design issues to more applications (especially training rather than school or university learning) with new technologies (especially agents) and new styles of systems (environments and collaborative systems). However, I will risk neglecting new developments, by seeking a longer-term and possibly deeper view, in order to show that, as technologies come and go, there is some underlying continuity and coherence to the field. I will also risk using only examples of my own and my colleagues' work. This is because I want to show that in some respects we have made rather little progress and these examples illustrate this very well.

Figure 1 gives an outline of the paper. I will tackle each of the five ovals in turn.

OVAL 1: FOUNDATIONS (1974-77)

The problem of student modelling has been an issue in ITS research from the beginning (about 1970) until today. The [START_REF] Self | Student models in computer-aided instruction[END_REF] The paper developed the distinction between descriptive (or declarative) and predictive (or procedural) student models. This in itself is no great surprise because it was written at the time of the great 'declarative v. procedural controversy' in AI. It was natural to explore its possible implications for student modelling. A student model had previously been regarded as a data structure, representing characteristics of a student and his or her problem-solving performance. If, however, it were to be regarded as a program, describing the steps by which a student solves problems, then it could be used not only descriptively but also predictively, to predict how a student would solve problems in the future (assuming the model were accurate, of course). This would provide potential benefits in terms of monitoring problem-solving performance, providing detailed feedback and developing adaptive tutoring strategies. Of course, the difficulties of such a proposal were also acknowledged, namely, that it would require that we have a sufficiently accurate psychological model of learning to maintain the fidelity of the student model and that it would require addressing the issues being raised in the then fledging research areas of automatic programming and machine learning.

Thus, what I will call the first thread tieing ITS research together was apparent from the beginning, namely: Thread 1: Who Cares Wins (eventually) [For the benefit of non-British readers, I probably need to explain that this is a variation on the expression "Who Dares Wins", which I understand is the motto of the British secret services and is much used in TV game shows and the like.] It seemed rather self-evident that a computer system which cared about an individual student would be better than one which did not. This focus on modelling individual students seemed to be a distinguishing feature of AI-based computer-based learning systems and we can be reasonably proud of the fact that our field was the first to recognise the need for what is now more broadly called user modelling, a term which didn't come into general use until about 1980.

The use of the word 'care' is deliberate. I suggested this in the not-too-serious final session at AIED 97 and now propose it more seriously. Our critics have pushed ITS work off the high moral ground by using positively-loaded terms such as 'constructivist', 'authentic learning', 'learner-centred' and so on to describe alternative approaches. A student model is what enables a system to care about a student. Of course, system designers and teachers care about students too -but somehow our critics have implied that we care less because we want our systems to care more. A system without a student model cannot care about an individual student. If the use of a student model is a defining characteristic of an ITS then ITSs care; non-ITSs do not. Of course, we are using 'care' in a rather special sense. We mean care about what the student knows, misunderstands, wants to do, etc. But there is no harm in basking in the positive glow associated with the term.

The 1977 paper [START_REF] Self | Concept teaching[END_REF] further considered the possible benefits of predictive student models. Basically, it proposed to optimise the learning/teaching process through analysis rather than experimentation. The idea is illustrated by Figure 2. A student using a computerbased learning system passes through a series of situations s 1 , s 2 , .. s m via a sequence of events. In situation s m there is a set of possible succeeding events. How may a choice be made between them?

Figure 2. Adaptation through student modelling

We could, in principle, analyse the sequence of situations and events that have already occurred to determine a student model S m for situation s m . We could then take each possible event e mj in turn and, using a model of learning, determine the student model S mj which would exist if that event were to happen. We could then consider each of the putative student models S mj and determine which of them might be preferred. Then we could adapt the system so that the event which would lead to that student model actually did occur or was more likely to occur.

This proposal was followed through using two contemporary models of learning, with the following results: gain over a random strategy concept learning 28% vocabulary learning 172%

A reader paying inadequate attention may misinterpret these figures. These are not figures showing improvements in learner performance as a result of using some computer-based learning system. Indeed, there are no students involved at all. These are entirely theoretical predictions. They say that, if the model of learning is sound, then an adaptive strategy would be better than a random one by the percentages shown. Of course, it is a very big 'if', but the challenge is to make it sufficiently small that the predictions are useful. It might be useful to know, for example, that according to a particular theory of learning an adaptive strategy would provide learning gains which would be so small that an implementation would not be costeffective.

A further thing to note about these predictions is that they were obtained by computer simulation and not by any kind of formal or mathematical analysis of the models of learning. Mathematics-envy might lead us to hope for axiomatic theories from which predictions may be formally derived. However, theories for any significant kind of learning are likely to be too complex for this to be possible. In any case, there is no fundamental difference between predictions derived by formal analysis and those derived by computer simulation.

This now provides the second thread running through ITS research:

Thread 2: Design is Theory (eventually)

The nature of design is, of course, a controversial question, so I will leave this thread hanging in the air for the moment.

In summary, at the end of the 1970s, the mood of the ITS field was optimistic. A number of demonstration systems had been developed, leading to new contributions to AI itself. The distinctive nature of ITS research had been established. A concerted development programme would now surely show the benefits which would result.

OVAL 2: REACTIONS (1985-92)

The 1985 paper [START_REF] Self | A perspective on intelligent computer-aided instruction[END_REF] presented an alternative to the prevailing expert system and student modelling approach to ITS design. [The 1985 date perhaps needs emphasising: it is well before constructivists and situationists began telling ITS researchers that they were misguided. By then, we already knew we had problems carrying out our programme.] In particular, the paper took on board the growing realisation that student modelling was difficult by considering how we might manage without one. In fact, now I re-read the paper, it is rather more radical: it proposes dispensing with the domain knowledge and tutorial strategies as well! It was (I think now) not a serious proposal towards an ideal computer-based learning system -it was more of a thought experiment to see how we might do things differently.

If one dispenses with the three traditional ITS knowledge bases we have a system which is much like a student -it doesn't know much about the domain or how to learn it. Therefore, there is not much left for the system to do but collaborate with the human learner to try to learn this knowledge. So that, essentially, was the proposal -to build a system to function as a 'computer collaborator' which engaged the student in a genuine shared discovery learning process. The system was not to be 'distracted' by any prior domain knowledge or hidden didactic intent of its own. Since this was to be a shared learning process, the system itself would need to learn from the joint activities, using machine learning techniques.

I regret to say that, try as I might, I cannot find in the paper any educational rationale for this proposal. It is motivated entirely by perceived practical problems: since we were failing to build adequate representations of student models, domain knowledge and tutoring strategies let us try to manage without them.

This proposal, unfounded though it was on any theoretical principles, led to a series of projects, up to Dillenbourg's People Power project, described at ITS 92 [START_REF] Dillenbourg | People Power: a human-computer collaborative learning system[END_REF]. Using Vygotsky's theories of social learning and other influential contemporary work on socially distributed cognition and dialogue (argumentation, negotiation, etc.), he managed to provide a convincing educational basis for computational co-learners. Over the period, the role of machine learning became de-emphasised as it became clear that the co-learner did not need powerful learning techniques, as human learners did not have them either. While all of this is mainstream today, at ITS 92 it was rather novel. At that conference, it was one of only two papers considered to present 'alternatives to one-on-one tutoring'.

However, I have moved out of chronological step. The earlier ITS 88 paper on "bypassing the intractable problem of student modelling" [START_REF] Self | Bypassing the intractable problem of student modelling[END_REF] has been well-referenced but unfortunately by many who have read only the title (although I am grateful for that). The aim was not to say that student modelling is intractable and should be avoided (the paper was poorly titled!) but to argue that the nature of student modelling needed to be reconceived, away from the form which was being heavily criticised at the time, to make it more tractable. The first thread -the aim of 'caring' systems, supported by student models -remained strong for me.

The pessimistic interpretation of the paper was in keeping with the mood of the ITS field at the end of the 1980s, which one might describe in one word as 'opprobriated' (if it were a word). A great deal of opprobrium was being heaped upon ITS research from all directions, including from within itself, for many leading figures (such as Brown, Wenger, Clancey, Sleeman and Soloway) now considered that AIED research was misguided, relying as it appeared to do on out-moded philosophies of knowledge and learning.

OVAL 3: THEORY (1989-92)

Personally, I retained the earlier optimism -so much so that I day-dreamed further about the possibility of there being firmer theoretical foundations for ITS design [START_REF] Self | Theoretical foundations of intelligent tutoring systems[END_REF]. Design is, of course, a mixture of art and science but for ITS design it seemed to be nearly all art. ITS design did not seem intrinsically more difficult than, say, aircraft design, which is supported by aeronautics -"a blend of beautiful theory and empirical fine tuning" [START_REF] Shevell | Fundamentals of Flight[END_REF]. Aircraft design has progressed through many centuries of visions and a few decades of serious experimentation to largely depend on the theory of aeronautics and specialised test environments. Would ITSs ever be built by a blend of beautiful theory and empirical fine tuning?

Figure 3 (from [START_REF] Self | Theoretical foundations of intelligent tutoring systems[END_REF] summarises the proposal. Conventional ITS design proceeds through the (informal) derivation of imprecise design principles from imprecise theories of learning and interaction. From these principles, we then (informally) derive experimental systems, which are empirically evaluated. Through many iterations, the results of these evaluations lead to revised theories, principles and systems. Instead, we might have precise, axiomatic theories from which we may (formally) derive principles and systems, which, if the theories are sound, must (by definition) be reliable and hence only need to be demonstrated. In practice, of course, no theory would be sound enough to not require empirical fine-tuning and, in reality, we could imagine this process being applicable to only certain components of the whole ITS design, leading to a mixture of theory and empiricism.

Figure 3. The ITS design process

The early ACT tutors provided the most refined design methodology of the time and an interesting case study. The ACT theory of cognition and learning (1985 vintage) provided an exceptionally detailed theory but one which nonetheless was not detailed enough to provide rigorous derivations of design principles. Informal argumentation led instead to eight, rather imprecise, principles, which themselves are rather indirectly related to the implemented systems [START_REF] Anderson | Cognitive principles in the design of computer tutors[END_REF]. The infeasibility of any rigorous design process was, of course, recognised and indeed it was the whole point of the ACT tutor programme to provide a test-bed for the ACT theory and to use empirical studies to feed back into a revised theory.

The following simple illustration indicates the alternative approach. First, we need to formalise a theory of learning, perhaps as a set of statements describing how the cognitive state of a learner changes as a consequence of instructional actions or other events. In general, then, it might be defined by a set of axioms of the form:

State(s,s1,t) & Event(e,x) -> State(s,s2,e(x,t))
i.e. if the student s is in cognitive state s1 in situation t and e is an event with parameters x then she will be in state s2 in the situation reached after event e, that is, e(x,t).

For example, an impasse-based theory of learning from examples might have the following axioms (simplifying, of course):

1. State(s,empty,t) & Event(present-positive,[p,f,a]) -> State(s,Believes(s,f->a),present-positive([p,f,a],t))
i.e. if the student knows nothing then if we present a positive example p which has feature f and where action a was used to solve the problem, then the student will come to believe that any problem with feature f can be solved by applying action a. i.e. similarly, if the positive example has two features f and g then the student will believe that action a is appropriate for problems with both features.

3. State(s,s1,t) & Event(question,[q,fs]) & Impasse(s1,q,fs) -> State(s,generalisation(s1),question([q,fs],t))

i.e. if the student is presented a question q with features fs which leads to an impasse then she generalises her cognitive state (the basic assumption of an impasse-based theory of learning being that the student will attempt a generalisation to overcome the impasse).

4. State(s,s1,t) & Event(question,[q,fs]) & Solves(s1,q,fs) -> State(s,s1,question([q,fs],t))

i.e. if the question is solved then the student's state is unchanged.

We also need definitions of the predicates Impasse and Solves and the function generalisation, which might include, for example, the axioms: 5. Impasse(Believes(s,f&g->a),q,f) i.e. if the student believes only the rule f&g->a and the question has only feature f then she reaches an impasse.

6. Solves(Believes(s,f->a),q,f) i.e. if the student believes f->a and the question has only that feature then she will solve the problem.

generalisation(Believes(s,f&g->a)) =

Believes(s,f->a) v Believes(s,g->a)

i.e. by generalising, the student will believe that one or other feature alone is sufficient for the action.

A set of such axioms and associated definitions constitutes, in our terms, a theory of learning. We also need to define the set of possible events. Let us imagine that we have just two example problem solutions p1 and p2, the first with feature f1 and the second with features f1 and f2, both where action a1 was applied. (To be concrete, p1 could be 745-127 with f1=borrow-next-left, p2 could be 85-27 with f1=borrow-next-left and f2 = borrow-leftmost, and a1 = borrow). If we assume two similar problems could also be presented as questions, we have four possible events: 8. Event(present-positive,[p1,f1,a1])

9. Event(present-positive,[p2,[f1,f2],a1]) 10.Event(question,[q1,f1]) 11.Event(question,[q2,[f1,f2]])
Let us assume the student has no prior knowledge (her cognitive state is empty at time t0):

12.State(s,empty,t0)

A set of such axioms, defining a learning theory, the events available and the initial state, can now be used to formally derive a plan for achieving some goal. For example, imagine that we wished to determine what the learner may believe after any two events (perhaps we anticipate that a class session has time for only two examples or questions and we wish to determine which ones are most productive). This goal can be expressed as:

State(s,x,e2(x2,e1(x1,t0)))
i.e. what cognitive state x might the student reach after any two events e1 and e2?

From the twelve premises above, just three solutions may be derived:

State(s,Believes(f1->a1),question ([q1,f1],

present-positive([p1,f1,a],t0)))

i.e. if we present the positive example p1 followed by question q1 then the student will believe f1->a1.

State(s,Believes(f1&f2->a1), question ([q2,[f1,f2]],present-positive ([p2,[f1,f2],a1],t0)))

i.e. if we present the positive example p2 followed by question q2 then the student will have an over-specialised rule.

State(s,Believes(f1->a1) v Believes(f2->a1), question([q1,f1],present-positive ([p2,[f1,f2],a1],t0)))

i.e. if we present the positive example p2 followed by question q1 then the student will be confused as to which feature requires a1 to be used.

This simple formulation re-expresses the argument presented (informally) in VanLehn (1987) that we should present three-column subtraction before two-column subtraction. The proof is general (in the sense that it could be applied to any similar problem-solving situation) and the conclusion could be presented as an 'instructional guideline' rigorously derived from a particular theory of learning, in this case, that examples should make it clear what features of a problem necessitate a particular action. The derivation of such a proof is a general theoremproving process and could, in principle, be carried out by automatic means. The general aim, then, is to express the theory of learning, events, learner attributes and instructional goals formally so that an instructional theory may be presented as a set of conclusions derived rigorously from assumptions, rather than as a set of vaguely expressed guidelines.

The above illustration is much too simple to convince anyone that the methodology is feasible. Premises to describe the effect of some instructional actions are missing; the description of the learner and goals are too simple; we should represent some kind of probabilistic analysis, as the effects of events are not so definite; and so on. However, simplified though it is, the specific conclusion drawn is one which was originally presented as justification for developing a detailed learning theory to be implemented as a computer program so that its properties may be studied. When the premises become more realistically complex, derivations will require computational assistance and thus there is no fundamental difference between an axiomatic analysis such as the above and a computational simulation. The issue is which mode of description is most clear, concise, precise, convincing, and ultimately successful in leading to the specification of ITS designs.

The suggestion that ITS design should be based on formal theories was not taken seriously, even by me, papers on the topic (e.g. [START_REF] Self | Computational mathetics: the missing link in intelligent tutoring systems research[END_REF] having a rather frivolous tone. The suggestion that we develop more formal and precise theories of what we are doing, to guide our designs -rather than implement systems based on our intuitions and then see how well they work -did not, and does not, appeal. It is much more fun to just try things out. The term 'computational mathetics' was introduced only with the apology that it is "impressively pretentious". Originally, it was a private joke -a term intended to keep gullible people quiet. If you are asked what your interests are and answer "AI" and "education" then everyone will give you the benefit of their thoughts on those topics. If you say "computational mathetics" they will go away and bother someone else. I recommend it to you. Now, however, I believe that (what is meant by) computational mathetics is an important objective for ITS researchand from a scientific point of view, the most important one.

Occasionally, less gullible people will ask "what's that?" on hearing "computational mathetics" and it is necessary to have an answer, which is summarised by the following dictionary entries: mathetic, adj. pertaining to learning. mathetics, n. the study of matters pertaining to learning. computational mathetics, np. the computational study of matters pertaining to learning, i.e. the use of techniques, concepts and methodologies of computing to study (and support) learning.

The first word is in all good dictionaries. The second should be. The third is a phrase invented in rough analogy to computational linguistics. If we compare: linguistics mathetics psycholinguistics psychomathetics sociolinguistics sociomathetics computational linguistics computational mathetics applied linguistics applied mathetics ... ... then we can see that the analogies to perfectly respectable sub-fields of linguistics all exist in our own field and that many of our disagreements are due to confusions about the sub-fields we are working in.

Over the years, we carried out a few feeble attempts at computational mathetics:

• frameworks for student modelling, using modal logics [START_REF] Self | Formal approaches to student modelling[END_REF] • open learner models, using belief revision (Paiva, Self amd Hartley, 1995)

• cognitive diagnosis, using GDE, the general diagnosis engine [START_REF] Self | Model-based cognitive diagnosis[END_REF] • DORMORBILE, using meta-programming, discussed briefly below [START_REF] Self | Dormorbile: a vehicle for metacognition[END_REF] • process models of learning, using situation calculus (Akhras and Self, to appear)

This work has been characterised by a naive hope that, just as aeronautics was based on existing notations of physics, mechanics, hydraulics, etc., so computational mathetics might be created from existing notations within artificial intelligence.

OVAL 4: SOME CURRENT PROJECTS

In this section I would like to briefly describe some current projects at Leeds. I am sure my colleagues are focussed only on the timeliness of their research but I like to imagine that there is some thread of continuity running through these projects and earlier work.

Interactive diagnosis based on conceptual graphs

This project [START_REF] Dimitrova | The interactive maintenance of open learner models[END_REF] continues the line of work which considers the learner to be a participant in the learner modelling process. The aim is to move from menubased collaborative diagnosis towards supporting a more negotiational dialogue about the learner model and enabling the learner to directly manipulate the model. This project is being carried out in the domain of non-native English speakers learning English scientific or technical terminology (e.g. the terms of computer science or financial management). Conceptual graphs are being used as a graphical external representation, providing the means through and with which learners communicate.

Mediating strategic conflicts in group problem solving

There has been a lot of work on cognitive conflicts (under the heading of 'belief revision' in AI) but possibly of more relevance to ITS are 'metacognitive conflicts', that is, conflicts over strategic, monitoring and reflective processes. This project [START_REF] Tedesco | Mediating meta-cognitive conflicts in a collaborative problem-solving environment[END_REF] considers a group of students addressing some planning task, such as deciding upon a sequence of actions to achieve some goal, involving perhaps the creation of a PERT network. The aim is to formalise descriptions of the various metacognitive conflicts that arise and use these as the basis for designing a system to mediate discussions among the group.

Studying, teaching and understanding research methodologies

The aim of the STURM project (Treasure-Jones, Pengelly and Self, 1999) is to develop a computer-based advisor for a student given a rather open-ended essay assignment, involving some research of web-based materials and the structuring of an essay, for example, an arts undergraduate asked to write an essay on "In what sense were Beethoven and Schumann romantic composers". The student's research strategies are modelled using the Dormorbile framework, particularly the higher-levels of the framework, enabling the system to provide relatively domain-independent strategic advice.

Web-based distance learning from case studies by groups

The case study method, involving open-ended problems based on realistic situations which are tackled by groups over extended periods, is a well-established educational activity but one which has been relatively neglected in ITS research. It may, however, be better suited to webbased distance learning than the kinds of narrow problem-solving of conventional ITSs. Students may browse the web for material related to the case study and then come together for on-line discussions on the ideas developed, leading to a coordinated set of off-line and on-line sessions. This project [START_REF] Rosatelli | Supporting distance learning from case studies[END_REF] aims to support the group through a general 'seven steps' methodology for case study activity [START_REF] Easton | Learning from Case Studies[END_REF]. The system's role is to monitor the group's transition through the seven steps and to promote the group discussion.

Collaborative learning as realised in simple simulated agents

The aim of this project [START_REF] Burton | Clarissa: a laboratory for the modelling of collaboration (under review)[END_REF] is develop a computer-based laboratory for studying collaboration, in order to determine the determine the functionality of agents acting as collaborators with human learners. A theory of collaboration as the dynamic distribution of dialogue roles is the basis for a computational model which can be explored to help determine how students and simulated collaborative should behave in order to benefit the most from collaborative activity.

Virtual learning environments (for firefighter decision-making)

There are always inflated expectations about the educational applicability of a new technology, when it is necessary to pause to reflect on the precise educational benefits that the technology may provide. In the case of 3D VR, it seems to be most relevant when it is essential to provide a sense of presence for learners developing decision-making skills in stressful, dynamic situations, for example, a group of firefighters. As this is a group training process, involving collaboration and communication, and there is a need for agents to simulate members of the group, there are overlaps with the projects mentioned above.

Networked interactive media in schools

This project (with the University of Duisburg and MediaWorld of Germany and INESC of Portugal) is the most technology-driven of this set, being funded by the European Union to explore the potential of innovative, intelligent interfaces in primary schools (4-8 year olds).

The aim is to introduce networked computers and interactive whiteboards into ordinary classrooms to support a group of children cooperating in a process of 'multimedia authoring' to create, for example, stories [START_REF] Hoppe | Collaborative learning in open distributed environments -pedagogical principles and computational methods[END_REF]. There will be a need for explicit (animated) agents within the stories and for implicit agents monitoring and supporting the group interactions.

OVAL 5: A PROCESS MODEL OF LEARNING

Finally, I will consider a recent project (Akhras and Self, to appear) in which many of the themes come together to provide a rather novel view of the nature of adaptive learning environments and in which we investigate whether the twin threads of ITS research, compassion and precision, really are irreconcilable with the views of constructivists, who hold ITSs in disdain.

One of the basic assumptions of constructivism is that knowledge cannot be objectively defined and statically represented (as it is in conventional student models). Instead, it is individually constructed from what learners do in their experiential worlds. By means of acting in a world, learners assimilate new concepts to their previously constructed cognitive structures or modify their cognitive structures to accommodate interpretations of the new experiences. It follows that knowing and doing cannot be separated and that the activity and context of an experience become an integral part of the meaning of that experience. Therefore, the focus is on the process by which knowledge is constructed rather than on a target domain knowledge to be acquired.

The more recent discussions of the nature of learning and knowledge concern the properties which a process of learning should have to be conducive to learning. This then leads to suggestions that designers should design systems which enable such desirable properties to hold. The perceived properties of a process of learning depend on what the student already believes and what she does while interacting with the system. Therefore, systems may need to be able to adapt to try to ensure that desirable properties hold. To enable such adaptations, a system needs to model the properties of the interactions between system and student.

Overall, the aim is to model the process of learning as one that happens over time, through interactions between cognitive structures and context, and through activity. We need to describe the environmental contexts in which interactions occur and the events which are possible in a situation -their preconditions and their effects. A series of events creates a sequence of situations which we may consider to be a 'course of interaction'.

Various properties of a course of interaction may now be defined. Of the many properties that may be defined, some may be considered 'better' than others. That is, learning theorists may have argued that, or perhaps provided experimental evidence that, a course of interaction with a particular property is more likely to lead to learning than a course of interaction without that property.

After a particular course of interaction, we have passed through a sequence of situations {s 1 ,s 2 ,..s m } by means of a series of events {e 1 ,e 2 ,..e m-1 } (Figure 4). In the situation s m reached, a set of events {e m1 ,e m2 ,..e mn } are possible. If a particular event e mj were to occur, then a new situation s mj would be reached. The (potential) course of interaction {s 1 ,s 2 , .. s m , s mj } would possess a set of properties P mj = {p 1 ,p 2 ,..p r }. We may say that the situation s m 'affords' each of these properties p i through event e mj .

Clearly, some of the potential courses of interaction will possess more desirable properties than others. Therefore, the system could seek to adapt itself to ensure than an event occurred which led to the desired properties. In general, there is a set of potential properties available, each of which is more or less desirable than others, and there needs to be some heuristic strategy for guiding the student towards events which, on balance, are more likely to lead the learner to encounter contexts which allow a continuing learning experience. This general scheme has been elaborated in considerable detail in [START_REF] Akhras | Reasoning about process in intelligent learning environments[END_REF], where complicated constructivist properties of cumulativeness, constructiveness, self-regulatedness and reflectiveness are defined. You may notice more than a passing similarity between Figure 4 and Figure 2. What has changed? We now have no explicit representation of the student's knowledge, beliefs, etc. (that is, no student model as usually conceived). Instead we focus on the events that have occurred. We say that if a 'good' course of interaction occurs then the student is more likely to learn -but we do not attempt to represent precisely what will be learned. This seems closer to the spirit of constructivism in that the 'content' of learning is intimately related to the 'activity' of learning, and what is actually learned is a construction of the learner from the activities she has carried out. In our case, we have no target knowledge we are aiming for -we 'simply' seek to ensure that the learner passes through courses of interaction considered conducive to learning.

What has happened to the conventional tripartite division of ITSs? The architecture that emerges is not one which is fundamentally opposed to the standard ITS architecture or any other. It is simply one which focusses on the different set of issues which arise from the different philosophy. In particular, it is clear that the attempt to develop intelligent systems to support learning is not inherently contradictory to a constructivist view of learning. In order to clarify how this is so we may re-consider the traditional three ITS components.

The model of domain knowledge

An ITS designer tends to assume that knowledge can be described in terms of facts, principles, and so on, which can be represented symbolically and hierarchically and learned in an incremental fashion. Therefore, he invests his effort on developing complex representations of such knowledge. Constructivists emphasise that learners construct their own knowledge through interpreting their experiences in interactional contexts. Therefore, the designer of a constructively-oriented learning environment focusses not on knowledge representations but on the nature of situations, contexts and interactions. This leads to a consideration of the 'content' of contexts and of the dynamics of the learning process. However, a 'situation model' may well contain representations of aspects of the domain of knowledge which a learner may access during interactions. These representations may appear similar to the models of domain knowledge in ITSs but their purpose may be very different. They are not descriptions of target knowledge but descriptions of resources which are available in a learning situation. From this perspective, a model of domain knowledge may be seen as a subset of the broader notion of a situation model.

The model of the learner's knowledge

Typically, the student model of an ITS is determined by analysing the student's interactions with reference to the model of domain knowledge in order to determine gaps or errors which may form the basis for instructional interventions. If, however, as constructivists argue, the student's individual constructive process (leading to personal constructions perhaps unrelated to any target knowledge) is more important than the particular product of any learning process, then our model of the learner should focus more on the interactive process, extended in time, taking into account the learner's actions, the contexts in which they occurred, and the learner's cognitive structures at the time. Developing such an 'interaction process model' enables us to consider the kinds of regularities of interaction sequences which lead to properties which benefit or hinder learning. As we have indicated, the learner's cognitive structures may form part of the descriptions of the time-extended interactive process, for the significance of interactive events may depend on individual cognitions. However, the aspects of cognitive structures to be considered are of a different nature and assume different roles than in standard ITSs, as they are taken in relation to the context and activity that constitute learning interactions. Thus, again, the notion of an interaction process model is, in a way, a superset of (rather than in opposition to) that of an ITS-style student model.

The model of teaching knowledge

ITS designers consider that their systems should, more or less deterministically, determine instructional plans by interpreting their student model with respect to a curriculum structure based on the model of domain knowledge. Constructivists would argue that the learning process is too unpredictable to be amenable to analysis by pre-specified structures and that learning sequences emerge from interactions between the learner and the environment as influenced by the opportunities that become available. Therefore, according to the latter view, the pedagogical role of the system is not to determine instructional events but to provide profitable spaces for interaction to the learner based on some model of the affordances of potential situations. In the previous section, the 'affordance model' focussed on the particular properties of courses of interaction afforded. This was because, according to the constructivist view, the process of learning was more important than the product. However, if one had an objectivist view of knowledge one could develop an affordance model in terms of the 'items of knowledge' which may be learned through particular events (so, for example, an ITS event such as the presentation of remediational feedback might be considered to afford the learning of the item of knowledge remediated). So, again, we see the affordance model as broader than, not opposed to, the model of teaching as curriculum-based planning.

Figure 5. The components of ITSs

So, at the risk of over-simplifying, we may present a tripartite model of the architecture of computer-based learning environments which includes the standard ITS architecture as a subset (Figure 5). Like all simple diagrams, it risks misleading the reader -in this case, by suggesting that the design and implementation of such systems is much, much harder than designing ITSs (which are hard enough) because the ITS architecture is shown as a small subset. In fact, the content of these components may be much simpler to implement than those we strive for in ITSs. For example, the kind of student interaction process model we may need, based on properties of courses of interaction, may be easier to construct than the high-fidelity cognitive models based on detailed considerations of knowledge structures which we need in conventional ITSs.

CONCLUDING COMMENT

I hope to have indicated that, despite the changes in philosophies and technologies, there are continuous threads running through ITS research which define its essential nature. While I have characterised the mood of the ITS field at the end of previous decades as optimistic and then opprobriated, perhaps an appropriate word for the end of the present decade is 'opportunistic'. The field is no longer so pure that it will resist co-opting philosophies and technologies wherever they come from. This naturally leads to an active, multi-faceted research field but it masks the underlying coherence. The real opportunity for the next decade is to tie together the two threads -to design systems that care about students and have a degree of computational precision -and thereby provide a unique scientific and technical contribution.

  paper assumed as its starting point the tripartite architecture for ITS -the what (domain knowledge), who (student model), and how (tutoring strategy) components which are now part of the encyclopedia definition of what an ITS is. The basic architecture of ITSs was thus already established some 25 years ago.

Figure 1 .

 1 Figure 1. Outline of the paper

Figure 4 .

 4 Figure 4. Adaptation through interaction modelling


Wouter Van Joolingen
email: wouter@ilo.uva.nl

Cognitive tools for discovery learning 1

Cognitive tools, defined here as instruments that support or perform cognitive processes for learners in order to support learning, can bridge the difference between open learning environments, like discovery learning environments and traditional supportive instructional environments. This article discusses a definition of the concept of cognitive tool and its use in learning. Two examples of cognitive tools for discovery environments are presented, and it is made clear how these tools can serve as hooks for anchoring intelligent instruction. Finally design issues for integrating cognitive tools in a discovery environment are discussed.

INTRODUCTION

This article addresses the issue of supporting discovery learning processes. Discovery learning is seen as a promising way of learning for several reasons, the main being that the active involvement of the learner with the domain would result in a better structured base of knowledge in the learner as opposed to more traditional ways of learning, where knowledge is said to be merely transferred to the learner. Of course, a continuum exists from 'pure discovery' to 'pure expository learning', and in fact neither of the extremes will be found in educational practice. The same continuum can be found in computer environments for learning, from (expository) learning environments presenting information to the learner to open-ended environments like computer simulations or web based inquiry environments giving much freedom to the learner. This continuum gets a meaning at the moment that we want to interactively support the learner in gaining knowledge from a learning environment. Where in a system controlled, expository environment, the question is which information to present based on the interaction with the learner, in a discovery environment the question is how to assist the learner in selecting and interpreting information from the learning environment. The shift in locus of control from system to learner forces us to rethink the means of supporting the learner. Hence we also have to think differently of the role and location of system intelligence as an aspect of the learning environment.

In this article the role of learner support by a discovery learning environment and the role for intelligent system components in such environments will be investigated. The concept of cognitive tool as introduced by [START_REF] Lajoie | Computers as cognitive tools[END_REF], will be used as a central idea in describing supportive means in discovery environments.

The approach discussed in this article is to support learners in performing discovery skills with cognitive tools. Cognitive tools are defined as instruments included in a learning environment allowing learners to make cognitive processes, like discovery skills, and their results explicit. Cognitive tools therefore can play a supportive role in discovering a domain. Central themes that will be discussed in this article are:

• How cognitive tools can be designed, starting from a theory on discovery learning. A design theory for cognitive tools is necessary to be able to offer genuine support for discovery learning. It will be discussed how a dual search space model of discovery can lead to the choice and design of cognitive tools. • Characteristics of cognitive tools in terms of their impact on the learning process.

Cognitive tools have intended and unintended effect on discovery learning processes. They influence the way discovery processes are carried out and the learner's freedom. • The integration of cognitive tools in a simulation environment. It will be discussed why integration is important and how this integration can be effectuated in a simulationbased learning environment.

In this article a number of examples of cognitive tools will be presented and discussed, as they appear in the SimQuest authoring environment for simulation-based discovery learning, as well as in other discovery environments.

The article starts with elaborating on the concepts of discovery learning and cognitive tools. In following parts of the article, the two concepts will be related and it will be shown that cognitive tools provide hooks for introducing system intelligence to support discovery learning without disrupting the nature of discovery. Finally, the SimQuest system is introduced as an authoring system explicitly introducing the concept of cognitive tools for discovery learning into a practical application.

Discovery learning

Discovery learning is a type of learning where learners construct their own knowledge by experimenting with a domain, and inferring rules from the results of these experiments. The basic idea of this kind of learning is that because learners can design their own experiments in the domain and infer the rules of the domain themselves they are actually constructing their knowledge. Because of these constructive activities, it is assumed they will understand the domain at a higher level than when the necessary information is just presented by a teacher or an expository learning environment.

In practice, it has been very hard to find solid evidence for this hypothesis. For instance, when learners are confronted with a simulation of a physics domain, and are asked to discover themselves which physical laws can explain the phenomena that can be observed, many learners will not reach a satisfactory result. The reason for this is not necessarily that the idea of discovery learning is not a good idea at all, but it indicates that learners need more than just the domain to learn about it. Apart from access to domain information, they need assistance in selecting and interpreting this information to build their knowledge base.

In research on scientific discovery learning, it has been found that in order for discovery of learning to be successful, learners need to posses a number of discovery skills (De Jong & Van Joolingen, in press), including hypothesis generation, experiment design, prediction, and data analysis. In addition, regulative skills like planning and monitoring are needed for successful discovery learning [START_REF] Njoo | Exploratory learning with a computer simulation for control theory: Learning processes and instructional support[END_REF]. Apart from being supportive for learning about the domain at hand, these skills are usually also seen as a learning goal in itself, as they are needed in a complex information society. Lack of these skills can result in ineffective discovery behavior, like designing inconclusive experiments, confirmation bias and drawing incorrect conclusions from data. In its turn, ineffective discovery behavior does not contribute to creating new knowledge in the mind of the learner.

Hypothesis Space

Experiment space In order to improve the effect of discovery learning, and to find beneficial effects of the basic principle of knowledge construction, one can try to support these discovery learning processes. However, as mentioned above, supporting discovery is not trivial as the very nature of discovery requires that the learner has sufficient freedom to select and interpret information meaning that any kind of support that limits this freedom in principle disrupts the nature of the discovery process.

In order to find a way out of this paradoxical situation, first discovery learning processes will be discussed in more detail in order to find the ways in which they may be supported. These models of discovery can then form the basis of supportive measures, like the cognitive tools introduced in the following sections.

Models of discovery learning

Models of discovery learning go back to the work of [START_REF] Newell | Human problem solving[END_REF], [START_REF] Simon | Problem solving and rule induction: a unified view[END_REF], Qin and Simon (1988) and [START_REF] Klahr | Dual space search during scientific reasoning[END_REF]. In this view the two problem spaces reflect a space of rules, or hypotheses, that are possible descriptions of the domain, and an instance or experiment space, which represents the data that can be collected in the domain. The two searches in these spaces are related: hypotheses can direct the search in experiment space and results of experiments can influence the search for new hypotheses. [START_REF] Klahr | Dual space search during scientific reasoning[END_REF] found two different search strategies in these dual search spaces. One starts with a certain hypotheses and uses experiment space search to find confirming or rejecting evidence for this hypothesis. When hypotheses are rejected, first a new hypothesis is formed before new experiments are generated to put this to the test. This strategy is labeled a theorist strategy. The opposite experimenter strategy starts with collecting data before a hypothesis is stated. [START_REF] Klahr | Dual space search during scientific reasoning[END_REF] found that the main selection criterion between these strategies lies in the prior knowledge of the discoverer. When a learner can state the correct hypothesis, perhaps as one of a list before the search starts he or she is likely to use a theorist strategy. Van Joolingen and De Jong (1997) extended the theory put forward by Klahr and Dunbar. They explicitly represented the knowledge of the discoverer in terms of subspaces of hypothesis space. The main concepts they introduced are the learner hypothesis space and the learner search space (see Figure 2). The learner search space contains all hypotheses the learner possibly knows of, in terms of variables in the domain, and relations that may exist between those variables. The learner search space consists of those hypotheses that the learner actually may consider as candidate for the actual relation that holds in the domain. For instance, when the learner knows of the variables of position and velocity and knows of an exponential relationship, an exponential relation between position and velocity would be in the learner hypothesis space. Would the learner's domain knowledge (or something else) tell the learner that such a relation would not make sense, the same relation would not be part of the learner search space. This difference in status between various -possible -hypotheses in the hypothesis space can be used to describe or explain differences in learner behavior. The basic assumption is that it will be more difficult to apply search operations that cross a border of a subspace in hypothesis space than to remain within a subspace. For instance to apply a search operation which moves from within the learner search space to outside this space, requires the learner to consider a relationship which did not come to mind at first investigation, whereas a search operation within the learner search space only shift focus from one alternative considered to another. Of course, the border is never "crossed" because as soon as a search operation generates a hypothesis, this will be considered to be inside the learner search space. Van Joolingen and De Jong (1997).

De [START_REF] Jong | Scientific discovery learning with computer simulations of conceptual domains[END_REF] provide an overview of research performed in this area which shows that learners can have all kinds of problems with discovery learning processes. The theoretical approach to discovery learning as outlined above provides some insight into why discovery learning may be troublesome for learners, and hence be not as successful as its advocates may claim. First of all the learning processes as identified by [START_REF] Njoo | Exploratory learning with a computer simulation for control theory: Learning processes and instructional support[END_REF] may themselves be problematic. For instance, their research indicates that both transformative and regulative processes may cause problems for learners. Transformative learning processes relate directly to the search processes in the dual search space theories discussed above. Learners may lack knowledge of the search processes themselves, for instance not know the idea of generalization of hypotheses, or they may have insufficient knowledge of what kind of hypotheses to state or what kinds of experiments to perform. These problems have all been observed in practice. For instance a known problem is that of conformation bias where learners perform only experiments that are able to confirm a hypothesis. In terms of the dual space search theory this can be described as an (unwanted) constraint on experiment space. Many problems with discovery learning can be described in terms of the search spaces. Sometimes learners have constraints on the search in either of the two search spaces that prevent successful discovery. In other cases, learners cannot find or construct constraints that would lead them in a successful direction. For instance, when doing experiments, a useful constraint is to vary not too much at a time. Learners missing this constraint will have trouble drawing sensible conclusions from their experiments. In hypothesis space, constraining oneself to a subset of the space will help focussed and more efficient discovery.

Cognitive tools

The term 'cognitive tools' was coined by the book edited by [START_REF] Lajoie | Computers as cognitive tools[END_REF]. In this book the view was expressed that computers could support learning by explicitly supporting or representing cognitive processes. In such a sense computers could serve as being a 'mind extension', augmenting the limited capacity of the brain. More general we can define cognitive tools as being instruments that are designed for supporting cognitive processes and thereby extending the limits of the human cognitive capacities. In principle anything can be a cognitive tool, for instance a sheet of paper and a pencil can be a cognitive tool to support the cognitive process of remembering items, extending the limited capacity of working memory.

When applied to learning, cognitive tools can be seen as supporting learning processes. Learning processes are the basic entities of describing the activities a learner needs to do in order to increase his or her understanding of a certain domain. This can be to remember something, to practice a procedure, to solve a problem, to set a hypothesis or some other process. Learning processes, when carried out properly, contribute to the construction of knowledge by the learner. However, learning processes may be quite difficult for a learner to perform, for instance because the process is very complex, not understood or because several learning processes need to be carried out at the same time. For instance, solving a complex physics process requires that the learner understands the physical properties of the problem, converts it to a mathematical representation, solves the resulting mathematical problem, understands the results in terms of the physics of the situation and monitors this whole process of creating representations and switching between them. This leaves the learning process as rather difficult, because multiple processes are carried out in parallel and not all of them may be very well understood by the learner. In teaching situations teachers usually assist the learner in learning how to perform processes by letting them concentrate on the essentials of the problem, for instance by offering them a divide and conquer strategy (first work out the physics characteristics, then solve the mathematical side of the problem).

In this example of physics problem solving, especially in physics domains where the mathematics is rather complex, such as electromagnetism, a mathematical symbolic equation solver (like Mathematica) can serve as a cognitive tool for these learners. The tool can take over part of the cognitive processes (solving the basic mathematics problems), in such a way freeing up working memory for concentrating on the real problems, those concerned with the physics. Note that the equation solver here acts as a cognitive tool. In other contexts, it may also serve different purposes.

Other examples of cognitive tools are tools for visualization of processes and domains in graphs, animations etc., as well as tools that can impose a structure on a reasoning process. For instance, visual instruments like Belvedere (Suthers, Weiner, Connely, & Paolucci, 1995) allow learners to see the structure of an argumentation, in such a way supporting the reasoning process by externalizing it.

It would be fairly easy to see almost all things that support learning as a cognitive tool. In order to limit the scope of the term, we define it within the context of this article as an instrument that is part of the learning environment that supports or performs an identifiable cognitive process that is part of the complete learning experience by the learner. In general, cognitive tools will not be the main instruments to present domain information, but they will be the instruments helping the learner to direct the process, to perform a part of it, or to externalize part of processes and memory.

In the next sections, the use and design of cognitive tools into simulation-based discovery learning environments will be discussed.

SUPPORTING DISCOVERY LEARNING WITH COGNITIVE TOOLS

In supporting discovery learning, cognitive tools may play an important role. As we have seen above, learning processes in discovery may be very complex and all kinds of problems for learners with discovery processes have been observed. In this section, we will investigate how cognitive tools can be used to support and direct the search processes in hypothesis and experiment space, by removing unwanted constraints and directing the search by learners in a fruitful direction. The challenge here is to make the tools unobtrusive, i.e. keeping the discovery character of the learning environment while at the same time trying to support and direct the learning processes going on. Two special examples will be discussed, one for supporting the generation of hypotheses, thus supporting search in hypothesis space, the second on monitoring experimentation, in the support for searching experiment space. The final part of this section will address hooking intelligent learner support to cognitive tools.

Hypothesis generation

Hypothesis generation is a very crucial process in discovery learning. It is the process where the learner actually generates new knowledge and ideas. Hypothesis generation has been found to be one of the most difficult learning processes in discovery learning. In the current section we deal with the support of two major problems, the structure of the hypothesis itself and unwanted constraints on hypothesis space search.

Learners very often do not know what the basic elements of a hypothesis are ( [START_REF] Joolingen | Supporting hypothesis generation by learners exploring an interactive computer simulation[END_REF]. Lacking this basic knowledge, the process of hypothesis generation will not even come to a start. This means that successful discovery is hindered by a relatively small flaw in the learner's knowledge. Hypotheses are statements that a certain relation holds between two or more variables. In formulating ideas, learners often divert from this form, expressing them in other ways. This still may be a valid representation of the idea for the learner, but a bad syntax will prevent proper testing of the hypothesis. Another problem can be that learners do not have enough prior knowledge about the extent of the hypothesis space. They may not know the variables to express ideas about or lack knowledge about the relations that exist and can be used to construct expressions.

Cognitive tools can help the process of hypothesis generation in different ways. This goes from performing the process for the learner, by providing a menu of ready-made hypotheses [START_REF] Michael | The pathophysiology tutor: a first step towards a smart tutor[END_REF], to tools that constrain or extend the search process in hypothesis space. One cognitive tool that we used in our own research is the hypothesis scratchpad (Van Joolingen & de Jong, 1997). The basic idea was to support the cognitive process of hypothesis formation by constraining the expressions that a learner could state as a hypothesis and by showing the contents of hypothesis space to the learner. The cognitive tool is a notebook-like screen that structures the statements that the learner can make to be valid hypothesis. This is made operational by offering a template for a hypothesis that the learner can fill in with variables and relations.

Figure 3 shows the hypothesis scratchpad in the version that was introduced in the SMISLE system [START_REF] Jong | The SMISLE environment: Learning with and design of integrated simulation learning environments[END_REF][START_REF] Joolingen | Design and implementation of simulation-based discovery environments: the SMISLE solution[END_REF]. The top shows the ingredients for a hypothesis, the middle part shows a hypothesis under construction and the bottom part shows the hypotheses that the learner has formulated before.

Relating this scratchpad to cognitive processes one sees that the template supports the basic hypothesis formation process. The ingredients for the hypothesis open up the dimensions of hypothesis space that the learner needs to form hypotheses. In such a way, the search process itself is stimulated, the learner can be given access to all or a part of hypothesis space. The bottom part of the hypothesis scratchpad serves as a memory of the trace through hypothesis space, in effect showing the effective learner search space. The list of hypotheses saves the hypotheses in the order they were created. It is impossible to delete them, but learner can annotate them and mark them as tested and assign a truth value. [START_REF] Jong | The SMISLE environment: Learning with and design of integrated simulation learning environments[END_REF][START_REF] Joolingen | Design and implementation of simulation-based discovery environments: the SMISLE solution[END_REF].

The hypothesis scratchpad was tested in experimental settings with different instructions to learners on its use. In [START_REF] Joolingen | Exploring a domain through a computer simulation: traversing variable and relation space with the help of a hypothesis scratchpad[END_REF] it was found that the hypothesis scratchpad was especially suitable to help learners with their initial exploration of hypothesis space and that such an initial exploration resulted in more, better and more explicitly tested hypotheses. In effect the scratchpad made learners more aware of the hypothesis generation process. Unfortunately the study failed to show any effects on posttests, but that may be attributed to the relatively short time learners worked with the system (a simulation of chemical error analysis problems).

Monitoring experiments

Another cognitive process associated with discovery learning is that of experimentation. In order to be successful in a discovery environment, learners must design experiments that serve as inspiration for hypotheses or that test hypotheses. In both cases, the experiments must be well designed. For instance, it is wise not to vary more than one variable at a time, to choose sensible variable variations (for instance multiply a variable by an integer number to test linear relationships), and to be aware of discontinuous changes in output variables (thresholds). In order to test a hypothesis usually a multitude of experiments has to be carried out. This puts a burden on working memory if the effects of all manipulations of variables have to be remembered by the learner. Therefore, a simple memory aid can already be quite helpful in storing experiments performed, and remembering them. Figure 4 shows an example of such a tool as described in Veermans & Van Joolingen (1998). The tool stands in close connection to the simulation (in this case of wastewater generation) and is able to read experiments from the simulation and store them on its internal list. The learner is in full control here, he or she can decide to add or delete an experiment. The tool offers means to manage the experiment like sorting experiments, replaying experiments and ordering variables. These relatively simple possibilities help learners to make sense out of the data they generate. As a cognitive tool the monitoring tool, as it is called, clearly supports the search process in experiment space, but in a different way than the hypothesis scratchpad supports hypothesis space search. Whereas the hypothesis scratchpad is mainly proactive, stimulating the learner to explore areas of hypothesis space by showing their presence on the scratchpad, the monitoring tool is reactive, in the sense that it comes into action only after experiments have been done by the learner. Relieving memory is clearly its main task, but offering means to get an overview of the part of the experiment space that has been searched is an important function of the monitoring tool as well.

Hooking intelligent support

The hypothesis scratchpad and the monitoring tool have in common that they both are relatively simple (it is admitted that the interface of the hypothesis scratchpad is a bit complex). They have a few functions, mainly for recording and editing. They interact with the discovery environment, a simulation, by retrieving and setting variable names and values. However, both tools have a great potential for much more intelligent support for the learner.

Intelligent learner support is a problem in open learning environments like discovery environments. Because of the open nature of the learning environment, it is hard to track what a learner does and thinks and therefore it is difficult to provide adaptive support to the learner. In the first instance the cognitive tools discussed here provide the learner with support on performing learning processes in a non-adaptive way, and their structure is constant. However, a strong point is that they let learners make their learning process explicit. Based on these explicit learning processes, a system (especially the cognitive tool itself) can provide the learner with feedback on the learning process or adapt the environment to optimise the learning processes.

This idea was elaborated in [START_REF] Veermans | Using Induction to Generate Feedback in Simulation Based Discovery Learning Environments[END_REF]. The monitoring tool that was designed to record experiments was used as a means to assess the experiment space search by the learner and provide feedback in order to stimulate the learner to increase the quality of experiments. The assessment method used assumes that the learner is testing a hypothesis and that that hypothesis is known to the system. This was controlled by providing learners with assignments that explicitly asked the learner to test a given hypothesis. In other contexts, of course, a link with a hypothesis scratchpad is feasible. Given this context and a set of experiments the monitoring tool can provide feedback on the experiments the learner did and on the conclusions the learner deduced from the experimental results. There are more ways to intelligently support the learner. For instance, the monitoring tool could advise the learner to perform a specific experiment, based on predictions generated from a hypothesis. Also, the simulation could be asked to constrain the experiment space, for instance by disabling manipulation of some variables. Likewise, the hypothesis scratchpad can do an analysis of hypothesis space search and provide feedback or constrain the learner, for instance by limiting the amount of variables and relations that are visible on the scratchpad.

DESIGN ISSUES

The two examples of cognitive tools given above both interact with the discovery environment in an intimate way. For instance, the monitoring tool must know which variables there are, which values they can assume and it must be able to read and set these values in the discovery environment, for instance the simulation. Similarly, the hypothesis scratchpad must have information about the domain in order to build its lists of variables and relations and for generating intelligent feedback it probably needs to know intimate details about the domain models.

This situation impedes flexible authoring of discovery learning environments with cognitive tools. The idea of a monitoring tool is on the one hand quite general, but on the other hand for actually working with this tool it needs intimate co-operation with the discovery environment. This tight level of integration is a problem because it seems to imply that for each new situation a new monitoring tool must be created. Especially for the many different kinds of simulation environments that exist, each time an interface must be designed to extract the variables and to read and control their value. In research projects this would be manageable, but when deploying discovery environment to schools and practice centers, it would be impossible to support adapting cognitive tools to a multitude of different environments.

SimQuest, component based design of learning environments

The way out of this situation is to design discovery environments as component-based systems, with standard interfaces between the various parts, or building blocks, which constitute the learning environment. This approach is taken in the SimQuest authoring system for simulationbased discovery environments ( [START_REF] Joolingen | The SimQuest authoring system for simulation-base discovery environments[END_REF]. SimQuest takes the approach that all applications can be built from building blocks, which then will be glued together to form a complete discovery environment. The basic assumption behind SimQuest is that it is possible to define interfaces between several kinds of cognitive tools and simulations.

Figure 6 displays the main authoring process in SimQuest. The author can pick building blocks from a library and move them to the discovery environment that is under construction. The library elements include parts for all components in the discovery environment, like simulation models, elements of the user interface and more or less complete cognitive tools, like the monitoring tool that was presented above. The building blocks are linked together and configured by the author in order to yield a complete learning environment.

The reason why this works within SimQuest is because the architecture below the mechanism allows this. Because simulations can take many different forms in SimQuest their properties are abstracted to a standardized form for the information that is actually needed by the other components in the learning environment. Every simulation environment uses variables that express its state and represent the inputs and the outputs. The number and nature of the variables and their possible values may change, but this basic way of representing the world is common to all simulations. Also there may be many ways of controlling a simulation, but most of these reduce to starting or stopping time, changing a variable value or changing a parameter, for instance one controlling the time frame. Within SimQuest this abstracted information is represented by a simulation context. The simulation context is the main means of communication between the simulation (or in fact any other kind of experimental environment) and all other components in the learning environment, such as a monitoring tool. In such a way the simulation context is the glue that keeps the various components together. All elements of the learning environment have a well defined interface to the simulation context, which passes on all relevant information to the components it may concern. For instance, changes in variable values are passed on to those components that have expressed interest in these values. Also, control over the simulation is co-ordinated through the simulation context. The basic architecture of any learning environment created with SimQuest looks like Figure 7. In this figure, instructional measures represent all possible components to support the learner, including cognitive tools.

Simulation Context

Simulation model

Instructional Measures

Learner Interface The SimQuest Architecture has been evaluated in various contexts. More than fifteen simulation based learning environments have been created with this system in which components like the monitoring tool, assignments, and explanations have been reused in each of the environments. The simulation models in these environments varied from simple static and dynamic systems to complex systems developed externally of SimQuest. These environments now find their way to practice and are used in experimental studies. For instance, see [START_REF] De Jong | Self-directed learning in simulation-based discovery environments[END_REF][START_REF] De Jong | The integration of computer simulation and learning support; an example from the physics domain of collisions[END_REF].

CONCLUSIONS

Cognitive tools as discussed in this article form a valuable addition to open learning environments like discovery learning environments. Cognitive tools were defined as instruments that support cognitive processes by relieving working memory or presenting a structure for performing the process. Also cognitive tools can perform a cognitive process automatically, allowing the learner to concentrate on another process first.

Using cognitive tools as hooks for adaptively and intelligently supporting learning processes is a promising means of solving the problem of adaptive support for learners in open learning environments. Because of their nature of supporting learning processes by making them explicit and visible, intelligent ways of generating support become possible. The kind of support that can be generated includes adaptive feedback, stimulative measures like providing the learner with new information on a search space and constraining measures, limiting the learner's search spaces. For the latter kind of support, the cognitive tools themselves can be an excellent means of implementation. For instance, the hypothesis space can be constrained by manipulating the variables and relations that are visible.

Finally, we presented an architecture for component-based design of discovery learning environments. The central element in this architectures is the simulation context which glues together the components present in the learning environment, the experimental environment, often a simulation, learner interfaces and learner support measures, including the cognitive tools that have been discussed in this article. The architecture enables generic design of cognitive tools that become pluggable into any discovery environment following this architecture.

In the future we may see more and more tools supporting all kinds of learning processes, including collaboration between learners, higher order skills, and advanced experimentation. The learning environment of tomorrow will allow learners to customize their way of working and receive the support they really need. Cognitive tools like these discussed here offer a wide range of possibilities in controlling the balance between guiding the learner and allowing for enough freedom for real exploration. In such a way the learner and the learning environment can find a position on the continuum between expository and discovery learning.

Figure 1 .

 1 Figure 1. Discovery described as a search in two related search spaces.

Figure 2 .

 2 Figure2. The decomposition of hypothesis space as described byVan Joolingen and De Jong (1997).

Figure 3 .

 3 Figure 3. The hypothesis scratchpad, in the version of the SMISLE system (De Jong & Van[START_REF] Jong | The SMISLE environment: Learning with and design of integrated simulation learning environments[END_REF][START_REF] Joolingen | Design and implementation of simulation-based discovery environments: the SMISLE solution[END_REF].

Figure 4 .

 4 Figure 4. Example of a monitoring tool for keeping track in experiment space.

Figure 5 .

 5 Figure 5. Two possible kinds of feedback that the learner can receive from the monitoring tool. The dialog on the left indicates that the quality of the experiments is such that no conclusion can be drawn. The dialog on the right indicates that the experiments done so far support the hypothesis.

Figure 5

 5 Figure 5 shows two examples of feedback generated by the monitoring tool. One refers directly at the quality of the experiments performed, the other concerns the relation of the experimental results with the current hypothesis. The method used for generating this feedback is based on a set of rules determining what proper experiments are and then applying deduction processes from the hypothesis to experiment space and seeing how the experiments fit with the predictions generated by the hypothesis.There are more ways to intelligently support the learner. For instance, the monitoring tool could advise the learner to perform a specific experiment, based on predictions generated from a hypothesis. Also, the simulation could be asked to constrain the experiment space, for instance by disabling manipulation of some variables. Likewise, the hypothesis scratchpad can do an analysis of hypothesis space search and provide feedback or constrain the learner, for instance by limiting the amount of variables and relations that are visible on the scratchpad.

Figure 6 .

 6 Figure 6. The main authoring process in SimQuest. On the left the library of available building blocks, on the right the discovery environment under construction.

Figure 7 .

 7 Figure 7. The basic architecture of a SimQuest learning environment

The work described in this article is a result of co-operation with many others. The author would especially like to thank the members of the SimQuest team, especially Ton de Jong and Koen Veermans, who contributed significantly to the ideas and software presented here.

The work presented here was carried out when the author was still at the Department of Educational Science and Technology of the University of Twente