After their beginnings in computer-aided instruction, automated tutors have re-emerged as intelligent tutoring systems. These intelligent tutors have obtained considerable success by using results from cognitive psychology and artificial intelligence to permit non-traditional instruction which is tailored to their individual students. The success of these automated tutors is due to their precise understanding and modeling of both the student and the domain being taught. A common measure of the robustness of an automated tutor is the size of the domain that it can understand. The schema-based Prolog tutor described in this dissertation is capable of recognizing a larger class of programs than existing Prolog tutors. By using powerful generalized transformations, our Prolog tutor can generate this class of programs from a very small set of normal form programs. Thus, our Prolog tutor recognizes a larger class of programs using fewer normal form programs than existing Prolog tutors. One o...

This paper presents a novel framework for looking at the problem of diagnosing a student's knowledge in an Intelligent Tutoring System. It is indicated that the input and the conceptualisation of the student model are significant for the choice of modelling technique. The framework regards student diagnosis as the process of bridging the gap between the student's input to the tutoring system, and the system's concept and representation of correct knowledge. The process of bridging the gap can be subdivided into three issues, data acquisition, transformation and evaluation, which are studied further. A number of published student modelling techniques are studied with respect to how they bridge the gap.

this paper and for allowing us to analyze the protocols he collected, to Ann Nguyen-Xuan for her comments, to Claire O'Malley for her invitation to this workshop and to all those who bought Pierre a beer during the workshop. This work was partly supported by the UK Science and Engineering Research Council grant GR/D/16079.

Intelligent Student Systems are a class of Intelligent Learning Environments that place the learner in the role of a tutor rather than a student. In an analogy with the educational practice of peer tutoring users learn by teaching the computer -- inverting the predominant `computer as tutor' metaphor. Intelligent Student Systems emphasize the learner's viewpoint in educational interactions in preference to the system's conception of the domain. These systems are considered to be less complex than Intelligent Tutoring Systems and to have the potential to generate novel human-computer educational interactions. Viewpoints also have an integral part in knowledge representation in Intelligent Learning Environments and they are utilised in the design and implementation of an Intelligent Student System in economics. Testing of the system produced insights into the future application of Intelligent Student Systems.

A Learning Companion System (LCS) is a variation of an Intelligent Tutoring System (ITS) where besides the tutor and the studentathird agent is added: a Learning Companion (LC). The exact nature of the role of the learning companion is one of the most important issues of these systems. This paper describes a LCS for Boolean Algebra (LECOBA) implemented to explore the role of the companion as a student of the human student (Learning byTeaching). To implement such a system, issues such as the motivation of the studenttointeract with the companion, and the LC's knowledge of the domain had to be dealt with. LECOBA provides companions with twotypes of expertise: weak and strong, and twotypes of motivation: `motivated' and `free'.

. In most Interactive Learning Environments (ILEs), the human learner interacts with an expert in the domain to be taught. We explored an different approach: the system does not know more than the learner, but learns by interacting with him. A human-computer collaborative learning (HCCL) system includes a micro-world, in which two learners jointly try to solve problems and learn, the human learner and a computerized colearner. This paper presents the foundations of this artificial co-learner. The collaboration between learners is modelled as 'socially distributed cognition' (SDC). The SDC model connects three ideas: i) a group is a cognitive system, ii) reflection is a dialogue with oneself, iii) social processes are internalised. The key has been to find a computational connection between those ideas. The domain chosen for illustration is the argumentation concerning how some changes to an electoral system affect the results of elections. This argumentation involves a sequence of argu...