Among the problems that face ITS designers, the problem of measuring the student knowledge state after concept learning in order to initially adapt a skill acquisition session according to a student's own necessities is a hard one. Typical approaches are the use of some sort of test to assess the student knowledge and choose an initial set of parameters for a session, or use, regardless the particular necessities of a student, a pre-defined set of initial parameters. We consider the fromer to be disrupting for learning and the latter too simple to deal with the broad possibilities that are faced. It is known that students show different behaviors during concept learning depending on the experience, background and actual understanding (the way a student is understanding a concept) during concept learning. Our approach here is to classify the different behaviors through fuzzy proposition and link them with a student model through fuzzy rules to use in an expert system, and with it, select the most suitable problem-solving strategy for each particular student in order to clear his misunderstandings and facilitate the learning of problem-solving skills. The use of probabilistic reasoning (i.e. Bayesian statistics) instead of fuzzy logic is not suitable for the present situation because of the rigidity and precision of the rules that do not allow a proper manipulation of the vagueness involved in the student behavior. We apply this idea to a circuit analysis ITS where the concept learning session is carried out on a Hypertext environment and the skill acquisition session on an interactive problem-solving environment. By tracing the student use of the Hypertext environment we can know the student behavior and use it as a premise in the fuzzy inference.

This study is intended to investigate a method to diagnose the student model in the domain of procedural problem solving. In this domain, the goal of an instruction should be to understand the processes of solving given problems, and to understand the reasons why problems can be solved by using sertain knowledge; the acquisition of problem solving skills might not be the intrinsic instructional goals. The tutoring systems in this domain must understand the effect of each problem solving operators, as well as when to implement these operators in order to effectively solve given problems. We have been studying and developing a system which deals with student modelling in the domain of procedural problem solving. We believe that the two types of knowledge should be clearly defined for the diagnosing tasks; effective knowledge (EK) and principle knowledge (PK). The former is the knowledge which is explicitly applied by students throughout problem solving processes, and the latter is the one which gives the justifications of the EK. We have developed a student model diagnosing system which infers students' knowledge structure pertaining to PK, based on the precedently manipulated student model about EK. This student model diagnosing method requires knowledge which argues the relationship between the PK and the EK. This knowledge plays the very important role in our system, and it's hard to describe such knowledge properly by hand. In this paper, we provide a student model diagnosing system which has the knowledge acquiring function to learn the relationship between EK and PK. The system acquires this knowledge through its own problem solving experience. Based on the student model and the acquired relational knowledge, the system can give students proper instructions about construction of EK with explanations in terms of PK. The system has been partly implemented with CESP language on a UNIX workstation.

This paper presents a hypermedia English learning environment, called HELEN (Hypermedia Environment for Learning ENglish), which integrates traditional methods of learning English, audio-visual facilities for both listening and watching and intelligent tutoring functions for suitable advice to each learner based on natural language understanding. HELEN consists of an authoring stage and a learning stage. In order to support multimodal learning, at the authoring stage HELEN gets voice and video scenes from a video disc and text sentences from an image scanner, then analyzes the sentences both syntactically and semantically by a natural language processing module so that necessary information for conversation, error identification and example sentence retrieval may be extracted. Thus at the learning stage, HELEN is able to aid learners to learn hearing, reading, writing, watching, consulting and noting. Besides these facilities HELEN also supports two facilities for tests in English: One is the test facilities of dictating sentences and the other is QA (questions and answers) facilities to make learner's comprehension state clear. According to the results of these tests, HELEN identifies learner's illegal usage of syntax or semantics, and piles them in a student model. The illegal usage in the model is used as resources for generating questions, treating errors, determining topics, etc. The main part of this paper concerns with the representation method for syntax and semantics of correct and incorrect sentences.

A simulation-based ITS (Intelligent tutoring system), SRIM, has been developed for the purpose of providing individualized learning to students of PID control. We first indicate that the following two steps will be a burden to the student during personal use of simulators: 1) Selection of operational goals and 2) Interpretation of the simulation results. In order to reduce the burden of students in learning with a simulator, SRIM guides the learning process by providing local goals for PID controller tuning and by giving messages. Two tutoring strategies: i.e. the exercise style strategy and the illustrating style strategy, are employed in SRIM. In the exercise style strategy, a local goal for tuning a PID controller is first given to the student. A local goal is defined as one which can be satisfied by a single operation step such as Decrease the off-set." The student selects his operation and executes the simulation. By observing the simulation, the student understands whether his operation was a success or a failure. The illustrating style strategy is invoked to repair the student's erroneous knowledge when a contradiction is detected in the student model or a wrong operation is selected repeatedly. The architecture of ITS is employed to perform the local goal selection and the tutoring strategy switching, in a natural, well timed manner. The performance of SRIM was evaluated for the purpose of demonstrating the effectiveness of the teaching strategy. The evaluation experiment was carried out in the following steps: 1) Pre-test, 2) Learning and 3) Post-test. The teaching effect of SRIM was compared with other learning methods such as simple use of simulators or a textbook from the results of the pre-test and the post-test. The results showed that SRIM is effective in providing individualized learning with simulators.

Student contradictions are the essentials of concepts and knowledge acquisition processes of a student, in the course of tutoring. This paper presents a new perspective to represent student contradictions and a student modeling architecture to capture them. The formulation of a student modeling mechanism enables flexible decision making by using information obtained from students. A nonmonotonic and inductive student model inference system HSMIS has been developed and formulated to cope with modeling contradictions, which basically embodies advanced representation power, sufficiently high adaptability and generality. The HSMIS is evaluated and compared with other representative systems in order to demonstrate its effectiveness.

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, PEOPLEPOWER, CLORIS and SMILE). The main issues revolve (as usual) around the questions of control and learning objectives.