The Role of Student Models in Learning Environments (Special Issue on Intelligent CAI and Hypermedia)

The student model component of intelligent tutoring systems (ITSs) used to be considered central: it was the means by which the ITS could individually adapt the learning experience to suit the learner’s perceived needs. However, the practical difficulty of building reliable student models, the evolution away from the knowledge communication style of ITSs towards a more constructivist philosophy, and the development of new media to support learning interactions have all combined to question the role (if any) for student models in current interactive learning environments (ILEs). In this paper we will explore the new role of student models by considering the lessons learned from five Lancaster projects (SAFE, EPIC, PEOPLE-POWER, CLORIS and SMILE). The main issues revolve (as usual) around the questions of control and learning objectives. 1 . I n t r o d u c t i o n The current AI debate about situated automata (e.g. Brooks, 1991) has both practical and philosophical aspects. It is about the balance between on-line and 'compiled' decision-making, the former being considered infeasible in many cases, and it is about the nature of knowledge, about whether reasoning consists of manipulating symbols or is intrinsically tied to the environment. This debate is mirrored in the 'AI in Education' community in the arguments over tutoring systems and learning environments. The ITS vision of having systems with explicit knowledge of the subject, the student, and how to teach, by means of which it would dynamically determine optimum individualised interactions has been challenged as impractical (we just cannot specify the required knowledge in this form) and philosophically unsound (it does not reflect the nature of knowledge as used by teachers and acquired by learners). Instead, it is proposed that we build ILEs in which on-line decision-making is reduced by compiling into systems the anticipated interactions or by transferring this responsibility to the student. As in AI, this debate is couched in revolutionary terms, by, for example, denying the existence of 'symbolic knowledge' (previously held to be the foundation of ITSs) and by presenting it is a radically new educational philosophy, based on student autonomy and authentic learning. However, in some respects this new revolution has come full circle. The implications of the 'new philosophy' are only beginning to be explored in AI in Education, though they have been debated by pedagogicians for centuries for it is, of course, not specific to the ITS/ILE debate (Clancey, 1993; Hoppe, 1993). Moreover, and rather ironically, the now-criticised ITSs are not, if considered objectively, actually 'tutoring' systems at all: they do not seek to impart new knowledge but rather to provide a 'practice environment' for knowledge previously taught. The aim of this paper is not to review the general implications of the trend towards ILEs but to focus specifically on the student modelling component. The student model was at the heart of an ITS in that it was the component which enabled individualised decision-making. We will first briefly review the many student modelling techniques which have been developed. The role of student models in ILEs is not so straightforward. We will consider a number of projects concerned with various kinds of ILEs to try to extract implications for student models in these contexts. 2 . Student models in intelligent tutoring systems Recently we have carried out three studies to try to describe and define the various student modelling techniques developed in ITSs. The first (Dillenbourg and Self, 1992a) maps the techniques onto a 3x3 framework. In the 'vertical dimension' we distinguish between 'behaviour', 'behavioural knowledge' and 'conceptual knowledge' (this is not intended to be a deep philosophical statement but just a synthesis of existing ITS work). In the 'horizontal dimension' we have the system, the system's representation of the learner, and the learner. All the different techniques can be related to specific components of the framework and to mappings between them. In this way it is possible to compare techniques, to see when they address different aspects of the problem, and to identify gaps in the set of techniques. The second review (Self, 1992a) considered student modelling as a problem of 'cognitive diagnosis' (Ohlsson, 1986) and thus as an instance of general diagnosis as studied in AI (Reiter, 1987; Struss, 1992). Most of the standard student modelling techniques, previously presented discursively without any reference to formal AI methods (e.g. Wenger, 1987), could be expressed rather more rigorously in the default reasoning terms of AI. However, many of the deeper problems of student modelling that student modelling is inherently an interactive process (the student ideally being an active participant and not a passive 'device' to be diagnosed); that student modelling serves more subtle goals than just the repair (or remediation) of faulty components (of knowledge); that there are things other than mere 'bits of knowledge' (e.g. metacognitive skills, attitudes, etc.) which need to be diagnosed all problems likely to become more important as we move towards ILEs, have yet to be addressed by AI diagnosticians, either because they are less relevant to device diagnosis or because they are fundamentally difficult. The third analysis (Self, 1992b) represented student modelling in terms of changes in the belief sets of the learner and the system (in particular those beliefs about the student's beliefs). In this way, student modelling can be related to many central concerns of formal AI, such as non-monotonic reasoning and belief revision as indeed it must in a long-term theoretical sense, since student modelling is unavoidably concerned with making default inferences about students (since a system can never obtain complete, reliable information about the student) and with maintaining a changing representation (since students are, by intent, expected to change their beliefs through interacting with an ITS). The outcome is that the body of ITS student modelling techniques, many of which have been shown to be practically useful in certain situations, can be considered to be reasonably well-understood. The more problematic areas may not disappear within ILEs but may become more central. Before considering some case studies, let us briefly summarise the main characteristics of ILEs, as compared to ITSs. 3 . Characterist ics of interact ive learning environments The evolution towards ILEs tends to emphasise the following characteristics, none of which is intrinsically antithetical to ITS design, unless the term is narrowly defined to exclude them: • Student autonomy: ILEs tend to provide the student with more freedom to follow their own learning agenda than is typically prescribed by curriculumbased ITSs. • Alternative conceptions: Since the target knowledge may not be explicitly defined, ILEs tend to recognise that students can have and indeed should have alternative (not necessarily incorrect) conceptions of domain knowledge. • Non-domain knowledge: In the absence of target domain knowledge, ILEs tend to emphasise the processes of acquiring such knowledge and hence the importance of more or less domain-independent metacognitive skills. • Varied teaching styles: With objectives transcending the learning of specific domain knowledge, ILEs tend to adopt styles other than the knowledge communication style of ITSs, emphasising instead the importance of dialogue and collaboration with the learner. • Use of multimedia: These teaching styles typically demand more than textbased interactions, requiring for example complex visual demonstrations. • Authentic learning: The richer setting of ILEs encourages 'authentic learning', that is, learning in situations as close as possible to the contexts in which learned skills will actually be used. Of course, not all ILE designers will emphasise all characteristics and indeed for certain extreme positions, e.g. that ILEs such as pure simulations should only react to student inputs, the implications for students models are clear they are irrelevant. However for most interesting cases, student use of a purely reactive ILE may not be satisfactory (however that may be defined) and it is necessary for the ILE to make some kind of instructional intervention on the basis of contents of a student model. We will illustrate some of the issues with the following five case studies. 4 . Five case studies