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.

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 proposes a method for producing a soft scenario" that coordinates highly individualized and mixed-initiative tutoring with strategic dialogue composed of semantically consecutive topics. The soft scenario is generated in three steps. The first step generates an outline plot of the scenario at the beginning of learning. The outline is composed of parts of domain knowledge to be taught and their sequence. A knowledge representation model, called MHM, and a method to represent author's teaching strategy, called a strategy graph, are introduced and used for generating the outline plot. The second step generates detailed parts of the scenario at the time to punctuate learning. Detailed planning is composed of two portions. One portion is to modify the pre-generated outline plot if the results of learning do not agree with the forecast. The other portion decides a teaching paradigm and a topic. A method of identifying student error origins, a teaching paradigm called adaptive guiding and information about leaning situation are presented and used for detailed planning of soft scenario together with a resolution tree of a given topic and compatibility of a topic to paradigm. The third step, which is not included in this paper, creates a dialogue part of the scenario according to a dialoguing strategy by using the previous context, and accords natural language outputs with graphical outputs.

The proposed high-power-factor converter is constructed with a flyback converter, and locates the energy-storage capacitor on the secondary side of the transformer. A high power-factor can be obtained without needing to detect any current, and the ZVS operation can be achieved without auxiliary switches. To make the best use of these advantages in the converter, ZVS operations and power-factor characteristics in the converter were analyzed. From the analytical results, the effective control method for achieving ZVS was examined. Using a bread-board circuit controlled by this method, a power-factor of 0.99 and a conversion efficiency of 88% were measured.

The designed converter has a two-input-winding transformer powered by single-phase AC voltage and an energy storage capacitor. Small size and enhanced conversion efficiency are achieved, because more than half of the energy is supplied to the load via a single conversion stage, and fast output-voltage regulation is achieved by controlling the charging and discharging of the storage capacitor. The design and control methods for the converter take into account the reset conditions of the transformer and stability in the output voltage control. An almost unity power factor and a low output voltage ripple were achieved with this converter fabricated as a breadboard circuit using small capacitors.