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Environment on Computer Networks. Gwo-Jen Hwang. Abstract—In developing a tutoring system, one of the most diffi- cult tasks is to collect tutoring knowledge ...
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On the Development of a Cooperative Tutoring Environment on Computer Networks Gwo-Jen Hwang

Abstract—In developing a tutoring system, one of the most difficult tasks is to collect tutoring knowledge from multiple educators, especially courses in which the contents change frequently, due to the advent of new technologies. In this paper, we propose a webbased intelligent tutoring strategy construction system, which is able to elicit, analyze, and integrate tutoring knowledge from multiple educators systematically. Experiments on two courses have been performed to evaluate the time, completeness, and accuracy improvement in constructing tutoring knowledge bases with our approach. According to the experimental results, it can be inferred that for most cases, our approach achieves desirable performances. Index Terms—Computer-assisted learning (CAL), computersupported cooperative work (CSCW), cooperative tutoring, knowledge engineering.

I. INTRODUCTION

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N recent years, the techniques of artificial intelligence (AI) have been applied to the development of computer-based tutoring systems. Earlier computer-based tutoring systems focused on the interactions between computer and individual student. With the fast development of computer networks, people can access information and communicate with others without the constraints of space and time; furthermore, they can discuss things with others to solve their problems. The experience of building hundreds of tutoring systems has shown the potential of such an approach and also reveals the difficulties of applying it. One major difficulty of building such systems is the elicitation of tutoring strategies and subject materials from educators. As each person has his (or her) own exclusive domain, if an educator’s exclusive domain does not completely cover the problem to be solved, incorrect decisions may be made [22]. Furthermore, for a domain expert to solve problems within his (or her) exclusive domain, over-confidence and ignorance may lead to mistakes. Therefore, it may be a good idea to construct a cooperative tutoring environment which can elicit tutoring knowledge and subject materials from several experienced educators in a systematic way. In this paper, we propose a network-based intelligent tutoring strategy construction system to elicit and analyze tutoring knowledge from a multiple of educators. Such a system not only guides educators, providing tutoring knowledge in a sysManuscript received December 2, 2000; revised July 19, 2002. This work was supported by the National Science Council of Taiwan, R.O.C., under Contract NSC-90-2520-S-260-001. This paper was recommended by Associate Editor R. Rada. The author is with the Department of Information Management, National Chi-Nan University, Nan-Tou, Taiwan 545, R.O.C. (e-mail: gjhwang@ ncnu.edu.tw). Digital Object Identifier 10.1109/TSMCC.2002.804451

tematical procedure, but also analyzes and integrates tutoring knowledge from multiple educators with differing opinions included. In Sections II–V, we shall present the theoretical basis and implementation techniques of our approach.

II. REVIEWS OF PERTINENT LITERATURE In the past decade, people have tried to refine subject materials and to develop new skills and tools for helping education to progress. In 1989, Johnson et al. proposed a software design and development research program called microcomputer intelligence for technical training (MITT). Specifically, Johnson et al. presented MITT Writer, an authoring environment for building intelligent tutoring systems for computer courses, which represented a practical application of AI in technical training [9]. In the same year, Vasandani et al. developed an intelligent tutoring system that helps to organize system knowledge and operational information to enhance operator performance [18], [19]. Meanwhile, Gonzalez and Ingraham designed an intelligent tutoring system, which is capable of automatically determining exercise progression and remediation during a training session according to past student performance [2]. Harp et al. employed the technique of neural networks to model the behavior of students in the context of an intelligent tutoring system, using self-organizing feature maps to capture the possible states of student knowledge from an already existing test database [3]. Furthermore, Rowe and Galvin employed planning methods, consistency enforcement, objects, and structured menu tools to construct intelligent simulation-based tutors for procedural skills [15]. Clearly, the development of intelligent tutoring systems and learning environments has become an important issue in both computer science and education [5], [11], [14], [20], [21]. Earlier computer-based tutoring systems focused on the interactions between computer and individual student. With accelerated growth of computer and communication technologies, researchers have attempted to adopt computer network technology for research on education. In 1998, Hwang proposed an intelligent tutoring environment which can detect the on-line behaviors of students [7]. One year later, Giraffa et al. demonstrated how a multiagent systems (MAS) approach can be used to build an interactive intelligent tutoring system [1]. In 2000, Ozdemir and Alpaslan presented an intelligent agent to guide students throughout course material on the Internet. The agent helped students to study and learn the concepts in the course, by generating navigational support according to their knowledge level [12].

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HWANG: DEVELOPMENT OF A COOPERATIVE TUTORING ENVIRONMENT

One of the application domains gaining increasing usage and visibility is computer-supported cooperative work (CSCW). The goal of CSCW is to provide systems that allow users to effectively collaborate on common goals using networked computer systems. Collaborators can exchange messages, share data, and collaborate even when they cannot physically meet. Many issues concerning the study and development of CSCW systems, including physical applications, data management, system architecture, group awareness mechanisms, etc., have attracted the attentions of researchers from various fields [4], [8], [10], [13]. In 1996, the University of Iowa started a multiphase project supported by the Hewlett Packard Company [16]. They utilized a 40-seat electronic classroom, in which lectures consist of presentation of concepts, immediately followed by examples, and practical exploratory problems. Four image-processing classes have been offered in the new collaborative learning environment during the 1996–1997 academic years. In Taiwan, the cooperative remotely accessible learning (CORAL) project was initiated by a research group at National Chaio Tung University, which consists of eight subtasks [17]: 1) the investigation of network-based tutoring systems, including pattern recording, remote data retrieval, and access control; 2) the investigation of network learning environments, including real-time monitoring and tutoring process control; 3) the investigation of wide area network (WAN) computerassisted learning (CAL), including feasibility, scalability, and architecture; 4) the testing and evaluation of network-based CAL, including motivation and cognition analysis; 5) the investigation of interface design for network-based CAL, including screen layout, icon/window design, and knowledge visualization; 6) the investigation of student modeling for network-based CAL, including the analysis of hypertext navigation and communication patterns; 7) the investigation of knowledge-based systems for tutoring process control, including knowledge representation for student characteristics and communication parameters, real-time analysis of student behavior, and dynamic arrangement of tutoring schedule; 8) the investigation of interaction pattern analysis, including the analysis of social context. The goal of the entire research group is to institute a collaborative learning environment on computer networks. One of the branches of CORAL is the intelligent tutoring and evaluation system (ITES) project, which focuses on applying the techniques of AI to enhance the tutoring process [7]. Based on the concepts of CSCW and knowledge engineering, an environment is established which helps educators provide superior integrated tutoring knowledge. In Sections III–V, we will present our approach in detail.

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III. ELICITING AND INTEGRATING TUTORING KNOWLEDGE In developing a tutoring system, one of the most difficult tasks is to collect tutoring knowledge and subject materials from multiple educators, especially in some engineering courses in which the contents change frequently due to the advent of new technologies. Taking integrated circuit design, for example, new technologies or tools are proposed approximately every three to six months; therefore, it is possible that an educator is only experienced with part of the technologies. Under such circumstances, the cooperation of multiple educators is required when providing tutoring knowledge and subject content. Furthermore, a knowledge integration technology is needed to efficiently produce integrated tutoring knowledge, one that precisely represents the experiences of every educator. A previous investigation [6] proposed a fuzzy table to cope with the problem of eliciting tutoring knowledge from an individual educator systematically. The procedure for constructing a fuzzy table consists of three steps. Step 1) Collect all of the elements (concepts to be learned or tutoring decisions to be made) from the educator. For example, in building a tutoring system for integrated circuit design course, some technologies, such as “fully customer design,” “standard cell design,” “gate array design,” and “FPGA,” may be proposed and placed across the top of the table. Step 2) Elicit attributes (fuzzy variables to describe the elements) from the educator. Each time the educator is asked for an attribute so as the three elements can be partially or fully distinguished. The fuzzy variables are listed in the left-hand side of the table. For each fuzzy variable, three fuzzy values are specified and listed in the right-hand side. Step 3) Fill all of the [element, attribute] entries of the table. Each entry consists of two parts: 1) a rating to indicate the most desirable fuzzy value and 2) the degree of certainty for allocating that rating. It is possible, however, that the educator selects the most desirable fuzzy value, but still lacks confidence about his (or her) choice. A 7-scale rating mechanism is employed in the fuzzy table. Each rating is an integer ranging from 3 to 3, which represent the possible combinations of linguistic hedges and fuzzy values. Consider the ratings of fuzzy variable “chip size” in the table: 3 means VERY LARGE, 2 means LARGE, 1 means MORE OR LESS LARGE, 0 means MIDDLE, 1 means MORE OR LESS SMALL, 2 means SMALL, and 3 means VERY SMALL. The degree of certainty for each rating is described by or , which represent VERY SURE and NOT VERY SURE, respectively. For example, consider the fuzzy table given in Table I; each column of the fuzzy table will be translated into a fuzzy rule. In [16], a formula is given to compute the truth-value for each output rule: of`` '' of`` '' of`` '' . For example, the first column of the fuzzy table is translated into the following rule:

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TABLE I ILLUSTRATIVE EXAMPLE OF A FUZZY TABLE FOR INTEGRATED CIRCUIT DESIGN COURSE

IF Chip size requirement is VERY SMALL, and Clock speed requirement is VERY FAST, and Design time limit is LONG, and Mask cost limit is VERY HIGH THEN The design technology can be “Fully Customer Design” In addition to the problem of interviewing educators, some problems may arise in integrating tutoring knowledge. 1) Since each educator works individually, it is possible that two educators express the same object or attribute (fuzzy term) using different vocabularies, which makes the knowledge integration process more complex and difficult. 2) Even though the vocabularies are unified, different ratings may be given by the educators to describe the relationship between an object and an attribute. Therefore, mechanisms are needed to integrate the differing opinions of the educators. 3) During the conflict-resolving process, it is possible that both the educators insist on their individual ratings category. In such a case, we need to carefully check whether the element or the construct is over-generalized. It can be seen that a network-based knowledge elicitation system may be capable of solving the problem of interviewing an individual educator. However, to have several educators work on the same goal, some knowledge integration mechanisms are needed. In Section IV, a network-based cooperative tutoring environment is proposed to cope with these problems. IV. WEB-BASED TUTORING ENVIRONMENT As the techniques of computer network advance, it is possible that several educators may work together from different locations via network communications. To produce high-quality integrated tutoring knowledge, the cooperative tutoring environment needs to provide the following functions: 1) eliciting tutoring knowledge from each educator systematically; 2) allowing the educators to share the elicited tutoring knowledge; 3) analyzing and comparing the opinions from several educators; 4) resolving differing opinions from the educators. Therefore, in this section, we present a network-based cooperative tutoring environment (also designated the intelligent tu-

Fig. 1.

Structure of intelligent tutoring strategy constructor.

Fig. 2.

Components of interactive knowledge elicitation unit.

toring strategy constructor in the sequel), which provides all of these functions. The intelligent tutoring strategy constructor is a JAVA program developed on Windows NT. It consists of an intelligent interviewing unit, a fuzzy reasoning interface, a knowledge analysis unit, a tutoring strategy negotiation unit, a knowledge base generator, and a JAVA-based network interface. Without loss of generality, we have selected one of the most popular expert system shells, C language integrated production system (CLIPS), as the target rule format. The structure of the intelligent tutoring strategy constructor is depicted in Fig. 1. In Sections IV-A–D, each unit of the intelligent tutoring strategy constructor will be introduced. A. Interactive Knowledge Elicitation Unit The knowledge elicitation unit consists of three components (see Fig. 2): 1) an interactive user interface; 2) a fuzzy table editor; and 3) a membership function builder. In the following, we depict each component of the interactive knowledge elicitation unit in detail. • The interactive user interface is used to construct the initial fuzzy table by interviewing educators via computer networks. It is a www interface coded with Java language. An illustrative example of interviewing an educator for a unit of a chemistry course is as follows. CONSTRUCTOR: Please give a set of concepts to be learned. EDUCATOR: Li, K, Fr, F, Cl, I. CONSTRUCTOR: Select a set of fuzzy values for fuzzy variable “boiling point”: 1. LOW/MIDDLE/HIGH 2. SHORT/MIDDLE/TALL 3. LIGHT/NORMAL/HEAVY 4. SMALL/MIDDLE/BIG 0. Other (user-defined) EDUCATOR: 1

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TABLE II ILLUSTRATIVE EXAMPLE OF A FUZZY CHEMISTRY COURSE

Fig. 4.

Illustrative example of membership function builder.

ship functions. It also contains an interactive display frame which depicts the membership function using a graphical representation. B. Fuzzy Reasoning Interface

Fig. 3.

Fuzzy table editor.

CONSTRUCTOR: Select a set of fuzzy values for fuzzy variable “atom radius”: 1. LOW/MIDDLE/HIGH 2. SHORT/MIDDLE/TALL 3. LIGHT/NORMAL/HEAVY 4. SMALL/MIDDLE/BIG 0. Other (user-defined) EDUCATOR: 0 CONSTRUCTOR: Indicate the lower bound of the fuzzy values. EDUCATOR: NARROW CONSTRUCTOR: Indicate the middle of the fuzzy values. EDUCATOR: NORMAL CONSTRUCTOR: Indicates the upper bound of the fuzzy values. EDUCATOR: WIDE CONSTRUCTOR: Select a set of fuzzy values for fuzzy variable “metalloid”: 1. LOW/MIDDLE/HIGH 2. SHORT/MIDDLE/TALL 3. LIGHT/NORMAL/HEAVY 4. SMALL/MIDDLE/BIG 0. Other (user-defined) EDUCATOR: 0

After the interviewing process, a fuzzy table is constructed, as shown in Table II. • Fuzzy table editor (see Fig. 3) allows the educators to modify the contents of the fuzzy table directly. This is an important feature, especially during the negotiation phase. • Membership function builder (Fig. 4.) enables the educators to define corresponding membership function. It consists of an interactive adjuster that an educator may decide to change the boundary values of various member-

The fuzzy reasoning interface is capable of accepting input values from users, performing fuzzification operations, invoking the inference process, and performing defuzzification operations on the outputs. Fuzzification operations are used to combine a real time input value (e.g., temperature and speed) with stored membership function information to produce fuzzy input values. Fuzzy inference attempts to correspond the fuzzified input facts to the premise patterns of fuzzy rules. Defuzzification combines all fuzzy outputs into a specific composite outcome. To accurately match the fuzzy terms with different types of linguistic hedges (e.g., very, more or less, not, etc.), some linguistic-conversion rules need to be defined to specify the relationships among those fuzzy terms. For example, If X is true with degree Dx , and Then X is very true with degree X is more or less true with degree and Not X is true with degree 1-Dx, and Not very X is true with degree , and Not more or less X is true with degree

Not X is more or less true with degree Not X is very true with degree C. Knowledge Analysis Unit Knowledge analysis unit checks if any conflict exists, and it also develops and integrates the tutoring strategies elicited from the educators. Without loss of generality, we represent the contents of a fuzzy table by two functions:

1) Fuzzy_value(Educator_ID, Object_name, Fuzzy_variable) and 2) Certainty_Degree (Educator_ID, Object_name, Fuzzy_variable) where Educator_ID(Expi or Expj shown below) denotes the identifier of an educator; Object_name(Gk shown below) denotes a possible solution, conclusion or action, Fuzzy_vari-

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able(Vs shown below) denotes an attribute with fuzzy terms to describe the characteristics of objects; Fuzzy_value denotes the fuzzy relationship between an object and a fuzzy variable; Certainty_Degree indicates the degree of certainty for the educator to give a fuzzy variable. For example, some entries of the fuzzy table given in Table II can be represented by the following facts:

Fuzzy_value (Educator01, Li, boiling point) Certainty_Degree (Educator01, Li, boiling point) Fuzzy_value (Educator01, Fr, atom radius) 1 Certainty_Degree (Educator01, Fr, atom radius) Fuzzy_value (Educator01, I, metalloid) Certainty_Degree (Educator01, I, . metalloid) Accordingly, we define several knowledge analysis rules as follows: Rule_analysis_01: IF Current_Phase is Knowledge_Analysis and Fuzzy_value(Expi, Gk, Vs) Fuzzy_value(Expj, Gk, Vs) and Certainty_Degree (Expi, Gk, Vs) is and Certainty_Degree(Expj, Gk, Vs) is THEN Return to Interactive_Knowledge_Elicitation_Phase. This rule is used to check if two educators assign conflict values to represent a fuzzy relationship. If this case does happen and both the educators are not confident about the value they gave, the system returns to the previous phase to get more confident information. Rule_analysis_02: IF Current_Phase is Knowledge_Analysis and Fuzzy_value(Expi, Gk, Vs) Fuzzy_value(Expj, Gk, Vs) and Certainty_Degree (Expi, Gk, Vs) is and Certainty_Degree(Expj, Gk, Vs) is THEN Set Suggested_Fuzzy_Value be Fuzzy_value(Expi, Gk, Vs) and and Set Suggested_Certainty_Degree be Set Current_Phase be Knowledge_Negotiation. This rule suggests that the value given by a more confident educator ( ) may be correct if a contradiction occurs. However,

as there is an educator who gives an opposite opinion, the resulting value is assigned with a lower certainty degree ( ). Rule_analysis_03: IF Current_Phase is Knowledge_Analysis and Fuzzy_value(Expi, Gk, Vs) Fuzzy_value(Expj, Gk, Vs) and Certainty_Degree (Expi, Gk, Vs) is and Certainty_Degree(Expj, Gk, Vs) is THEN Set Suggested_Fuzzy_Value be “ Conflict” and Set Current_Phase be Knowledge_Negotiation. This rule checks if conflict values given by two (or more) confident educators exist. In that case, the system is not able to make any suggestion; therefore, a negotiation phase is invoked to cope with this problem. Rule_analysis_04: IF Current_Phase is Knowledge_Analysis and Fuzzy_value(Expi, Gk, Vs) Fuzzy_value(Expj, Gk, Vs) and Certainty_Degree (Expi, Gk, Vs) is and Certainty_Degree(Expj, Gk, Vs) is and (Fuzzy_value(Expi, Gk, Vs) Fuzzy_value(Expj, Gk, Vs) or Fuzzy_value(Expi, Gk, Vs) Fuzzy_value(Expj, Gk, Vs) ) THEN Set Suggested_Fuzzy_Value be Fuzzy_value(Expi, Gk, Vs) and and Set Suggested_Certainty_Degree be Set Current_Phase be Knowledge_Negotiation. This rule combines the opinions from two educators who both give the values of the same pole with strong confidence. The more positive (larger) value or the more negative (smaller) value is suggested to be the integrated result with confidence . Rule_analysis_05: IF Current_Phase is Knowledge_Analysis and Fuzzy_value(Expi, Gk, Vs) Fuzzy_value(Expj, Gk, Vs) and Certainty_Degree (Expi, Gk, Vs) is and Certainty_Degree(Expj, Gk, Vs) is THEN Set Suggested_Fuzzy_Value be Fuzzy_value(Expi, Gk, Vs) and and Set Suggested_Certainty_Degree be Set Current_Phase be Knowledge_Negotiation. This rule combines the opinions from two educators who give the values of the same pole with different confidence. The value given by the educator who has more confidence is suggested to be the combination result.

HWANG: DEVELOPMENT OF A COOPERATIVE TUTORING ENVIRONMENT

Rule_analysis_06: IF Current_Phase is Knowledge_Analysis and Fuzzy_value(Expi, Gk, Vs) Fuzzy_value(Expj, Gk, Vs) and Certainty_Degree (Expi, Gk, Vs) is and Certainty_Degree(Expj, Gk, Vs) is and (Fuzzy_value(Expi, Gk, Vs) Fuzzy_value(Expj, Gk, Vs) or Fuzzy_value(Expi, Gk, Vs) Fuzzy_value(Expj, Gk, Vs) ) THEN Set Suggested_Fuzzy_Value be Fuzzy_value(Expj, Gk, Vs) and and Set Suggested_Certainty_Degree be Set Current_Phase be Knowledge_Negotiation.

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TABLE III EXPERIMENT RESULTS ON SIX TOPICS OF “INTRODUCTION TO COMPUTER SCIENCE”

TABLE IV EXPERIMENTAL RESULTS OF 250 TESTS CASES

This rule combines the opinions from two educators who both give the values of the same pole with less confidence. The more conservative value is taken as the combination result with less confidence . D. Tutoring Strategy Negotiation Unit In the tutoring strategy negotiation phase, the system presents suggestions made by the knowledge analysis unit as well as the original data to each educator. The educators are then asked to decide if the suggestion is acceptable. If the suggested value is “conflict,” the educators are asked to give suggestions, and then the knowledge analysis phase is invoked to check if any conflict still exists. The tutoring strategy negotiation unit presents helpful information to the educators to assist them in making the final decision. It also utilizes several analysis and statistic utilities to assist the educators when it is difficult to jump to a mutually acceptable conclusion. If the educators fail to make any conclusion for some object, it is possible that the object is over-general. In such case, an Object_Specialization procedure is invoked to help the educators divide the original objects into several more specialized ones. For example, if an educator insists that the color of a bear is white while another educator insists that the color should be gray, the Object_Specialization procedure will try to divide the object “bear” into “bear of North Pole” and “American gray bear,” etc., in order to resolve the conflict. V. EXPERIMENTS AND EVALUATION An experiment on six topics of the “Introduction to Computer Science” course has been made to evaluate the performance of our approach. Four educators, , , , and , are invited to join the experiment. For each topic, two educators were selected to be the experimental group (Group A) that employed our approach in providing tutoring knowledge, and the other educators in Group B used the normal Internet tools (i.e., e-mails and BBS) to achieve the same goal. Each group submitted the integrated tutoring data if the educators both agreed with the results. To have the experiment done within six weeks, the educators were asked to submit their results of each topic in a week and started the next topic even though the final agreement had

not yet been achieved. At the end of each week, four educators were asked to evaluate the completeness of the submitted tutoring data from each group. We rated the completeness with a value ranging from “not complete” (0) to “complete” (1). The average ratings from the four educators and the time spent by each group are given in Table III. It can be seen that for most cases, our approach achieved better performance for both time and completeness, especially in those topics whose contents change rapidly (e.g., input devices, output devices, and network communication tools). Another experiment on a medical course, differential diagnosis of common causes of inflamed eyes, was also done to evaluate the accuracy of the integrated data. A medical school student needs to learn to classify various types of inflamed eyes according to a patient’s physical feelings or reactions, e.g., pupillary light response, discharge, etc. In this experiment, ten attributes are used to classify five kinds of diseases. The results of consulting 250 test cases are given in Table IV. If the constructed rule set correctly classifies the disease in a test case, the inference process is deemed to be successful. That is, the number of successful inferences represents the times for the constructed rule set to correctly classify the diseases in the test cases. Consequently, the ratio of successful inferences can be calculated by dividing the number of successful inferences with the number of test cases (i.e., 250). From the experimental results, it can be observed that the integrated knowledge achieved better performance than the original rule sets. VI. CONCLUSIONS In this paper, we presented a network-based tutoring strategy construction system, which is capable of eliciting, analyzing, and integrating tutoring knowledge from multiple educators on computer networks. Two experiments on a computer science

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course and a medical course have been used, enabling us to evaluate the performance of our approach. Although the designed experiments may not stand for practical applications, they are helpful in evaluating the performance of our approach, which includes the efficiency, completeness and accuracy of eliciting knowledge with the cooperation of multiple experts. In some science and engineering courses, such as communication networks, operating systems, multimedia systems, etc., the subject contents proposed by different teachers may vary widely and may change frequently; consequently, the use of the cooperative tutoring environment become necessary. Furthermore, although the original purpose of this research is to propose an effective cooperative work environment for constructing tutoring knowledge bases, the proposed approach might be applied to other cooperative work applications, such as group decision-making, multiexpert knowledge elicitation, and multisource knowledge management for various application domains. Currently, a branch of our research team is attempting to apply the approach to the multisource knowledge management problems of enterprises. ACKNOWLEDGMENT The author wishes to thank Dr. C. C. Lai, Dr. J. C. Hwang, Dr. J. H. Wang, and Dr. H. J. Chen for their assistance in performing the experiments. REFERENCES [1] L. M. M. Giraffa, M. Mora, and R. M. Viccari, “Modeling an interactive ITS using a MAS approach: From design to pedagogical evaluation,” in Proc. 3rd Int. Conf. Comput. Intell. Multimedia Applicat., New Delhi, India, Feb. 28, 1999, pp. 153–158. [2] A. V. Gonzalez and L. R. Ingraham, “Automated exercise progression in simulation-based training,” IEEE Trans. Syst., Man, Cybern., vol. 24, pp. 863–874, June 1994. [3] S. A. Harp, T. Samad, and M. Villano, “Modeling student knowledge with self-organizing feature maps,” IEEE Trans. Syst., Man, Cybern., vol. 25, pp. 727–737, May 1995. [4] C. Haythornthwaite, “Collaborative work networks among distributed learners,” in Proc. 32nd Annu. Hawaii Int. Conf. Syst. Sci., 1999. [5] G. J. Hwang, “A knowledge-based system as an intelligent learning advisor on computer networks,” in Proc. IEEE Int. Conf. Syst., Man, Cybern., vol. 2, Tokyo, Japan, 1999, pp. 153–158. [6] , “Knowledge acquisition for fuzzy expert systems,” Int. J. Intell. Syst., vol. 10, pp. 541–560, 1995. [7] , “A tutoring-strategy supporting system for distance learning on computer networks,” IEEE Trans. Educ., vol. 41, pp. 343–349, Nov. 1998.

[8] J. Iivari and H. Linger, “Knowledge work as collaborative work: A situated activity theory view,” in Proc. 32nd Ann. Hawaii Int. Conf. Syst. Sci., 1999. [9] W. B. Johnson, L. O. Neste, and P. C. Duncan, “An authoring environment for intelligent tutoring systems,” in IEEE Int. Conf. Syst., Man, Cybern., vol. 2, 1989, pp. 761–765. [10] A. P. Kosoresow and G. E. Kaiser, “Using agents to enable collaborative work,” IEEE Internet Comput., vol. 2, pp. 85–87, July/Aug. 1998. [11] S. Labidi and J. S. Ferreira, “Technology-assisted instruction applied to cooperative learning: The SHIECC project,” in Proc. 28th Ann. Frontiers Educ. Conf., vol. 1, Tempe, AZ, Nov. 4–7, 1999, pp. 286–291. [12] B. Ozdemir and F. N. Alpaslan, “An intelligent tutoring system for student guidance in Web-based courses,” in Proc. 4th Int. Conf. Knowledge-Based Intell. Eng. Syst. Allied Technol., vol. 2, 2000, pp. 835–839. [13] A. Prakash, H. S. Shim, and J. H. Lee, “Data management issues and tradeoffs in CSCW systems,” IEEE Trans. Knowledge Data Eng., vol. 11, pp. 213–227, Jan./Feb. 1999. [14] J. B. Pugliesi and S. O. Rezende, “Intelligent hybrid system for a training and teaching environment,” in Proc. 3rd Int. Conf. Comput. Intell. Multimedia Applicat., 1999, pp. 148–152. [15] N. C. Rowe and T. P. Galvin, “An authoring system for intelligent procedural-skill tutors,” IEEE Intell. Syst., vol. 13, pp. 61–69, May/June 1998. [16] M. Sonka, E. L. Dove, and E. L. Collins, “Image systems engineering education in an electronic classroom,” IEEE Trans. Educ., vol. 41, pp. 263–272, Nov. 1998. [17] C. T. Sun and C. Chou, “Experiencing CORAL: Design and implementation of distance cooperative learning,” IEEE Trans. Educ., vol. 39, pp. 357–366, Aug. 1996. [18] V. Vasandani and T. Govindaraj, “Intelligent diagnostic problem solving tutor: An experimental evaluation,” in Proc. Conf. Syst. Man, Cybern. Dec. Aiding Complex Syst., vol. 3, 1991, pp. 1739–1744. , “Knowledge organization in intelligent tutoring systems for di[19] agnostic problem solving in complex dynamic domains,” IEEE Trans. Syst., Man, Cybern., vol. 25, pp. 1076–1096, July 1995. [20] L. H. Wong, C. Quek, and C. K. Looi, “TAP: A software architecture for an inquiry dialogue-based tutoring system,” IEEE Trans. Syst., Man, Cybern. A, vol. 28, pp. 315–325, May 1998. [21] A. Yoshikawa, M. Shintani, and Y. Ohba, “Intelligent tutoring system for electric circuit exercising,” IEEE Trans. Educ., vol. 35, pp. 222–225, Aug. 2000. [22] P. L. Yu, “Behavior bases and habitual domains of human decision/behavior—An integration of psychology, optimization theory and common wisdom,” Int. J. Syst., Meas., Dec., vol. 1, pp. 39–62, 1981.

Gwo-Jen Hwang was born on April 16, 1963, in Taiwan, R.O.C. He received the Ph.D. degree from the Department of Computer Science and Information Engineering, National Chiao Tung University, Hsinchu, Taiwan, in 1991. He is currently an Associate Professor at National Chi-Nan University, Nan-Tou, Taiwan. His research interests include computer-assisted learning, computer-assisted testing, expert systems, and mobile computing.