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.

This paper describes the current situations and future directions of intelligent CAI researches/development in Japan. Then necessity of intelligence in CAIs/Educational systems are thought over corresponding to the model of teaching and the cognitive model of human learning like the situated learning, knowledge construction and so on. Originally, the main aims of ITSs/ICAIs are to tealize the high level environment of individual teaching/learning. So it is the most important to incorporate the intellectual function of teaching into the system. Whatever kinds of teaching purposes ITSs have, they have the quite complex structure which consists of the domain knowledge base (Expert system), student model, the tutoring knowledge base, the powerful human interface, and sophisticated inference engine with plural functions by artificial intelligence technology. In this paper, the technological and educational points of view are discussed, surveyed and summarized based on intelligent teaching functions of ITSs/ICAIs. Moreover, the meaning of new paradigm from ITSs to ILE are mentioned under the new technology of networking and multi-media.

This paper describes an indexing framework for adaptive arrangement of mechanics problems in ITS (Intelligent Tutoring System). There have been some studies for adaptive arrangement of problems in ITS. However, they only choose a solution method in order to characterize a problem used in the practice. Because their target domains have been sufficiently formalized, this kind of characterization has sufficed to describe the relations between any two problems of such a class. In other words, here, it is enough to make students understand only the solution methods for the given class of problems. However, in other domains, it is also important to understand concepts used in the problems and not only to understand solution methods. In mechanics problems, concepts such as mechanical objects, their attributes, and phenomena composed of the objects and the attributes also need to be taught. Therefore, the difference between solution methods applied is not sufficient to describe the difference between two given problems. To use this type of problems properly in the practice, it is necessary to propose an advanced new characterization framework. In this paper, we describe a mechanics problem with three components: (1) surface structure, (2) phenomenon structure, (3) solution structure. Surface structure describes surface features of a problem with mechanical objects, their configuration, and each object's attributes given or required in the problem. Phenomenon structure is described by attributes and operational relations among them included in the phenomenon specific to the surface structure. Solution structure is described by a sequence of operational relations which compute required attributes from given attributes. We call this characterizing indexing because we use it as index of each problem. This paper also describes an application of the indexing to arrangement of problems. We propose two mechanisms of control: (a) reordering of a problem sequence, and (b) simplifying of a problem. By now, we have implemented basic functions to realize the mechanisms except for the part of interface.

This paper discusses the design of an ITS to realize a load-oriented tutoring to enhance the student's explanation understanding. In the explanation understanding, it is to be hoped that a student not only memorizes the new information from an explanation, but also relates the acquired information with his/her own knowledge to recognize what it means. This relating process can be viewed as the one in which the student structures his/her knowledge with the explanation. In our ITS, we regard the knowledge-structuring activities as the explanation understanding. In this paper, we propose an explanation, called a load-oriented explanation, with the intention of applying a load to the student's knowledge-structuring activities purposefully. If the proper load is applied, the explanation can induce the student to think by himself/herself. Therefore he/she will have a chance of gaining the deeper understanding. The important point toward the load-oriented explanation generation is to control the load heaviness appropriately, which a student will bear in understanding the explanation. This requires to estimate how an explanation promotes the understanding activities and how much the load is applied to the activities. In order to provide ITS with the estimation, we have built an Explanation Effect Model, EEM for short. Our ITS consists of an explanation planner and a self-explanation environment. The planner generates the load-oriented explanation based on EEM. The system also makes a student explain the explanation understanding process to himself/herself. Such self-explanation is useful to let the student be conscious of the necessity of structuring his/her knowledge with the explanation. The self-explanation environment supports the student's self-explanation. Furthermore, if the student reaches an impasse in self-explaining, the planner can generate the supporting explanation for the impasse.

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.

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.

The paper proposes and reports on pototyping a work bench system for novice Prolog programmers which consists of a visually-structured interactive tracer and a prototype-based programming support. The tracer actually is a simulated interpreter in Prolog. It is interpreted by a Prolog interpreter being embedded with facilities interfacing programs in Prolog and the objects programmed in C. It displays, by way of these objects, the past, current and future goals, highlights variable sharing and value substitution, and marks the current goals and backtrack choice points. It is at user's will to let the tracer show and hide subgoals as well as to let it backtrack when it failed, step back for redoing or terminate tracing. The programming support module first provides the programmer with structural prototype patterns and the roles of the constituent functions. We developed a support system for the 2 types of recursive definitions. After having selected the prototype, the user is requested to specify the data types and the names of variables to be put in the arguments, which propagate through the structure. The support module then offers a menu of primitive or user-registered constituent functions as may be useful in processing and/or obtaining user-specified types of data. Thirdly the system lets the user express his/her intention by sample input-output data instances in his/her task goals. It makes the values propagate through the structures thus motivating the user to design the constituent functions. At the goal recursion point, the user is allowed to creep into examining the definitions of the reduced versions of the instances, helping the user find the condition with which the recursion terminates. Finally the module assists the user to convert the structural descriptions into Prolog programs.

This paper describes the concepts and methodologies of the INTELLITUTOR system which is an integrated intelligent programming environment for learning programming. INTELLITUTOR attempts to work as a human programming tutor to guide a user, i.e., a student, in writing a computer program, to detect logical errors within it, and to make advices not only for fixing them but also for letting him notice his misunderstandings. The system consists of three major modules, i.e., GUIDE, ALPUS and TUTOR. GUIDE is a guided editor for easy coding, ALPUS is an algorithm-based program understander, and TUTOR is an embedded-intelligent tutoring system for programming education. The ALPUS system can infer user's intentions from buggy codes in addition to detecting logical errors by means of knowledge-based reasoning. ALPUS uses four kinds of programming knowledge: 1) knowledge on algorithms, 2) Knowledge on programming techniques, 3) Knowledge on a programming language, and 4) Knowledge on logical errors. These knowledge are organized in a hierarchical procedure graph (HPG) as a multi-use knowledge base. The knowledge on logical errors was obtained by means of cognitive experiments. The student model is built by means of the results of ALPUS and interactions between a student and the system. Teaching is done based on the student model. Because the ITS subsystem, i.e., TUTOR, is embedded within the intelligent programming environment interactions for creating the student model could be minimized. Although the current system deals with the PASCAL language, most of the knowledge is applicable to those of procedure-oriented programming languages. The INTELLITUTOR system was implemented in the frame-based knowledge engineering environment ZERO and working on a UNIX workstation for system evaluation.

Kanji Laboratory is a kanji learning ICAI system. In this paper, we describe the development of Kanji Laboratory, which is designed for foreigners who are learning Japanese kanji. We have developed Kanji Laboratory under the guidelines of environmental ICAI systems, based on a kanji learning method focusing on kanji radicals. Kanji Laboratory consists of a knowledge base, a learning environment and an advisor module. The knowledge base can well-handle the knowledge of Joyo Kanji (1,945 characters). Each one is related with its radicals via their inherited attributes. In addition, this knowledge base system can search kanji knowledge quickly. The learning environment has the following features: (1) Students can construct a kanji by combining radicals and disassemble the kanji into radicals and strokes. (2) Students can use electronic tools, such as a kanji dictionary, which support kanji learning. In this way, students can learn kanji and the relations with its radicals effectively. With regard to the advisor, although it occurs that students fall in plateaus of learning in environmental CAI, the advisor module is designed to give well-timed advice to students, avoiding those plateaus, based on the observation of their learning actions.

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.

This paper is concerned with an ITS designed for augmenting a student's capability in problem solving. Discussions are concentrated on helping students acquire strategic knowledge and assisting them to build it in their heads. In this paper, many kinds of strategies are treated from a unified point of view. Based on this consideration, a teaching paradigm of strategic knowledge is presented. The paradigm is realized in an ITS as a training environment for strategic knowledge. Assisting students to learn strategic knowledge, the system sets up an appropriate environment and gives them some appropriate advice in each environment. It is realized as a function of giving them appropriate problems and hints about it. In general, strategic knowledge is a kind of heuristics so that it is not easy to describe their application conditions deterministically and explicitly. For this reason, an ITS for strategic knowledge is required to be designed so as to cover not only the case where expertise is represented explicitly as an executable model but also the case where it is represented only implicitly. To realize this teaching paradigm, situation-dependent knowledge called reminding pattern is prepared in the system. It is represented by a triple of a strategy, a situation, and a key symbol in the situation. It denotes that the key usually reminds students of the strategy in the situation. The system gives students problems including positive/negative examples of applications of each strategy in its problem solving process and hints which remind them of an appropriate strategy and makes them resume the problem solving when they fall into an impasse. In this paper, the structure of the system realizing this teaching paradigm is explained in the domain of proving propositional formulas.

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.

This paper describes the development of an environmental ICAI system for English conversation learning, which is equipped with a simulation-based learning environment and an advisor function. Recently there have been various educational applications or tools for adult second language education, where the learning target is the acquisition of formal knowledge of a language. When considering the implementation of a practical CAI system, methods for developing communicative competence in learners are required. Although there are a number of ICAI systems for conversation learning, often the methodologies which they apply are not completely suitable for the acquisition of the required fundamental knowledge. Our system, based on the architecture of environmental CAI, enhances communication skill acquisition. The system has a learning environment with the following features: (1) A simulation of language activities, implemented in the role-playing game style, which helps to promote a learner's motivation. (2) Educational behavior of the system is varied through the modification of the learning environment and changes in the simulation progress and control commands. (3) An induction strategy, which can cause learners to fail to achieve a learning target, is executed by an advisor mechanism. The system is a prototype architecture for application in environmental ICAI systems for simulation based learning. We believe that the architecture of this system is an efficient framework for linguistic education.

This paper presents an experimental environment of an intelligent tutoring system called EXPITS. In this environment, users learn functions and the structure of the intelligent tutoring system and characteristics of knowledge processing. EXPITS provides facilities for investigating internal processes and internal states of the intelligent tutoring system. These facilities include visualization tools and controllers of internal processes. Because the internal states and behavior of ITS depend on student's understanding states, one cannot get total understanding of ITS without information about student's knowledge states. To solve this problem, we introduce a pseudo student which simulates a human student in order to visualize explicitly all information which affects ITS behavior. Target users of EXPITS are school teachers, who are users of intelligent tutoring systems, university students who are studying artificial intelligence and postgraduate students who are specially studying intelligent tutoring systems. We have designed EXPITS to achieve different learning objectives for these three kinds of users. The learning objective for school teachers is to understand the differnce between intelligent tutoring systems and traditional CAI systems. University students are expected to understand characteristics of knowledge processing and rule based systems. Lastly, EXPITS provides postgraduate students who are studying intelligent tutoring systems with a test bed for examining ability and efficiency of the system in different configurations by changing parameters and by replacing constituents of the system. To achieve these purposes, EXPITS has experimental facilities for the following four themes; relationship between the domain knowledge representation method and teaching activities, the selection method of teaching paradigms, relationship between problem solving processes and teaching activities, and student modeling.

The learning environment called IPE (Interactive Physical Environment) for Dynamics is developed on the computer. IPE can understand the simulated phenomena started from the arbitrary conditions set up by the learner. IPE has several scripts which describe the dynamical phenomena. The understanding of the simulated phenomena by IPE is obtained by matching these scripts with status list and phenomena list which are generated by the simulator using difference scheme. IPE gives the learner the explanation sentences which explain the features of simulated phenomena. This explanation strongly assist the learner in recognizing the details of result of the simulation.