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CHAPTER IV

SYSTEM ANATOMY

 

We can see only a short distance ahead,

but we can see plenty there that needs to be done.

 

                                                               Alan Turing

 

System Overview

            The developed expert system in the study is named Taiwanese Intelligent Tutorial Expert System (TITES).  It offers a tool to enable teachers to generate teaching and testing material, to score tests, to record tutorial data, and to give instructions to students.  TITES currently runs on IBM PC compatible computers and was packaged on four floppy discs: knowledge base disc, utility disc, courseware disc, and PC Plus runtime disc.  As there are more than two hundred files in the package, it is recommended that the system be installed on a hard drive.

Operating Procedure

            After entering “TITES” at the DOS prompt, the screen message shows that the system is loading the knowledge base, then the TITES system title screen appears as shown in Figures 4-1 and 4-2.  To continue, press RETURN/ENTER key, and a user information form including a password request (see Figure 4-3) appears.  The ID, age, and password are invisible when the user enters these data.  Users must type each item and input an effective password before using the system.  Lack of any item or incorrect password will result in a logon failure and invoke the system restart process.

            When the user's password is accepted, the system will ask the user if introductions are needed; if so, a brief explanation of the system will appear on the screen.  If the user skips the introductions, the system will show the functions available to the user.  By using the arrow keys to place the color cursor over the function and depressing the RETURN/ENTER key, the user can choose instruction, examination, text file editor, or quit functions.  The instruction function includes tutor, digest, simulation, drill, and animation; the examination function consists of five tests.  Figure 4-4 shows the functional organization of TITES.

 

Figure 4-1.  First title page of TITES.

 

Figure 4-2.  Second title page of TITES.

Figure 4-3.  User information request form.

 

 

Figure 4-4.  Functional organization diagram of TITES.

            The tutor instruction is a book emulator which presents its contents on the screen as shown in Figure 4-5.  When the contents page is displayed, the user can move the cursor and select the appropriate chapter.  After pressing the RETURN/ENTER key, the title page of the chapter appears and shows the user how to use Home, End, and arrow keys to turn pages forward or backward.  The user can also press the Esc key to return to the contents page for accessing other chapters at any time or to quit the current chapter.  By selecting the tutor instruction, the user can read all the predesigned tutorial materials for that subject.  The digest instruction is similar to the tutor instruction; however, the materials were condensed so that the user can browse them by flicking pages.

 

Figure 4-5.  Contents screen in the tutor module.

            The drill function is a hierarchical knowledge presentation which enables users to review an article or paragraph.  The knowledge was compiled in a ladder type arrangement by levels of difficulty.  If the user needs detail or advanced information, s/he can choose terms or keywords from the screen.  The first time a user invokes the drill, a series of topics appears; then the user can use keys to navigate horizontally or vertically.

            The simulation instruction can be used in technique training.  For example, users can strengthen writing skills through the essay writing simulation.  When users choose a topic from the screen, several groups of sentences give step-by-step instructions to construct a paragraph.  The user can select one of the sentences in each group or write an original sentence.  After the user has constructed a version of the topic, s/he can read the paragraph on the screen and review the best version.  The user can also invoke an instructional module about how to construct a paragraph from these sentences.

            Animation is a set of motion pictures which can be used to present a scene, a fact, a phenomenon, or an idea.  Users can choose options to improve their knowledge, gain better understanding, and promote retention.

            The examination function provides five types of tests.  Each set of tests includes ten questions which allows a user to select the right one from four possible answers.  Test-A consists of text-based multiple-choice questions.  When a user moves the cursor to TEST-A and presses the RETURN/ENTER key, a series of test numbers appears on the screen.  The user can choose one of the tests or QUIT to return to the last screen to invoke other test types.  After the user has chosen one of the tests and pressed the RETURN/ENTER key, a yellow color testing guide appears to explain how to give answers and prompts the user, "IF ready, press RETURN/ENTER key."  Questions and possible answers appear on the screen one by one (see Figure 4-6).  The user can type A, B, C, or D or use the arrow keys to move the cursor to select the right answer.  After the user answers the ten questions, a score report including serial number, test number, score, quantity of right and wrong answer, and time used appears on the screen as shown in Figure 4-7.  The user can review the questions and answers, and correct answers on the next screen through the playback feature (see Figure 4-8).  Then the user can ask for tutorial instruction or select more tests to continue (see Figures 4-9 and 4-10).

 

 

Figure 4-6.  Multiple choice question of Test-A. 

 

Figure 4-7.  Score report of the test result.

            Test-B is a graphic-based multiple-choice test (see Figure 4-11).  Test-B and Test-C are designed for Chinese language, mathematics, geography, physics, and other scientific questions that need graphics for demonstration.  Each screen display shows one question.  Test-C is also a graphic-based test.  Each test offers ten blanks for users to complete (see Figures 4-12 and 4-13).  Students can use the RETURN/ENTER key to choose the appropriate position to type the answers from the keyboard.  All sentences used in Figures 4-12 and 4-13 were adapted from Robert James Dixson’s Essential Idioms in English (Regents, 1971).  Each sentence was coupled with relative Chinese translation.

 

Figure 4-8.  Screen showing a question, user's answer, and correct answer.

 

Figure 4-9.  Instruction menu of Test-A.

            Test-D is a test of sequential arrangements which can be used to test logical thinking, quantity analysis, and assembly techniques.  Test-E is a reading comprehension test.  Each test include two topics.  Users read an article on the screen and answer the 

questions about the meaning of the material.  The reading material may include a wide variety of topics.  Before learners answer the questions, they can press the F1 key and review the reading material from a window on the screen as shown in Figure 4-14.

 

Figure 4-10.  Instruction screen of Test-A.

 

Figure 4-11.  Graphic-based multiple choice questions of Test-B.

 

 

Figure 4-12.   Test-C: Ten blanks for users to complete. 

 

Figure 4-13.  Questions, user's answers, and correct answers for Test-C.

     All five types of tests share similar procedures so the user can manipulate the tests in the same way.  After users have completed the instructions and examinations, the system will produce a final score report (as shown in Figure 4-15) and update all the records in  dBASE III data files.  At this time, users can press the F2 key to exit or restart the system.


 

Figure 4-14.  Reading comprehension test of Test-E.

 

Figure 4-15.  Final score report of the examination.

Operating Flow Diagram

        TITES was developed with highly interactive and intelligent use of graphics for input and output.  All the data were manipulated by a set of routines with online help facilities.  Figure 4-16 shows a flow diagram for typical operation of TITES.

 

 

Figure 4-16.  Typical operating flow diagram of TITES (continued)

 

Figure 4-16.  Typical operating flow diagram of TITES (continued)

 

 

Figure 4-16.  Typical operating flow diagram of TITES.

 

General Architecture of the System

            In PC Plus, the knowledge base was constructed in hierarchical frame structures.

TITES can be divided into three major frame-tree structures.  The first frame, a set of the CAI family,  consists of several sub-frames such as drill, simulation, and tutor.  The tutorial materials could be textual, graphic, or animated.  The second frame, a set of the CMI family, is also a frame-tree structure.  The CMI frame is responsible for presenting problems or diagnostic information and recording the student's response to update the student database.  The third frame is the text file editor which allows the author to generate teaching material, test problems, and answers.  Figure 4-17 shows the overall frame-tree architecture of the system.  Each square is a frame in which goals, parameters, and rules are provided and, in some way, can be regarded as an independent knowledge base.

                                   

 

Figure 4-17.  Overall frame-tree architecture of TITES.

            The reasons to apply frame structure are:

            1.  To divide the knowledge base into several small subject-related hierarchical knowledge bases.  This kind of organizational skill simplifies the complicated structure and makes problems logical and clear.

            2.  A sub-frame can be instantiated repeatedly at any time.  This allows for variation in program control from a given point and switches to other parts or repetitions of particular items and fulfills the "branching" requirement of CAI.

        3.  By means of frame instantiated procedure and inference rules, an inference engine can distinguish a user's level of sophistication and make some adjustment to suit many needs of individual users, such as test-type, level of difficulty, progress of instruction, and frequency of review.  The benefit of learner control, therefore, can be reached in both flexibility and efficiency.


Programming Tactics

Frame Instantiation

            One of the advantages of frame structure is that hierarchical subframes permit instantiating repeatedly at anytime.  It is better, however, to initiate the root frame before subframes are constructed.  A series of instantiating control strategies should be considered before the system is generated.  Two important strategies to control frame instantiating are the dummy parameter method and the dummy goals method.  As an example of the dummy parameter method, consider Rule(146) in the CMI frame and Rule(117) in subframe TESTA frame.

        Rule146: If CMI.TYPE = TEST-A AND DUMMYA.CMI THEN CMI.GOAL

        (If the user selects Test-A, and the value of DUMMYA.CMI is “YES,” then the value of CMI.GOAL is “YES.”  Where CMI.GOAL is the goal of the CMI frame, CMI.TYPE and DUMMYA.CMI are parameters of the CMI frame.)

        Rule117: IF CMI.TYPE = TEST-A THEN DUMMYA.CMI

        (If the user selects Test-A, then the value of DUMMYA.CMI is “YES.”)

        In this case, DUMMYA.CMI is a check parameter or a dummy parameter.  The purpose of using a dummy parameter is to ensure that the inference engine checks the logic in the specified subframe.  At the beginning of normal backward chaining, the system searches the value of goal parameter CMI.GOAL by finding Rule(146).  Because the user selects TEST-A, the value of CMI.TYPE was set.  In order to find the value of DUMMYA.CMI, the system must try Rule(117) in TESTA frame.  By placing a dummy parameter in the ancestor frame (CMI) and ordering appropriate rules, the system will instantiate the specified subframe (TESTA).

        Another example is the dummy goal method used in ancestor frame CAI and subframe TUTOR.  Where DUMMYT.GOAL is a dummy goal in the CAI frame, FUNCTION is a parameter of the TUTOR frame.  To instantiate subframe TUTOR, a Rule(045) which sets the dummy goal’s value was organized in the TUTOR frame.

        Rule045:  IF FUNCTION = TUTOR THEN DUMMYT.GOAL

        (If the user selects Tutor instruction, then the value of DUMMYT.GOAL is “YES.”)

        In this case, DUMMY.GOAL is not a real goal of the CMI frame, the only purpose for using DUMMYT.GOAL is to force the system to instantiate the TUTOR frame.

        In the construction of TITES’s knowledge base, both the dummy parameter method and dummy goal method were used to control subframe instantiation.  For example, during the process of multi-goals inference, the use of these two control strategies cooperated with a complete tracing in backward chaining can force the system to trace the dummy goal and infer the value of the real goals one by one.  This inference method named “Hungry Tracing” given flexibility in causing frame instantiation, and it can be considered as an efficient design strategy of rule-based systems.  In TITES, more examples reveal that the hungry tracing method can get expected results even in the hybrid inference of forward chaining and backward chaining.

External Program Interface

            In TITES, teaching materials were separated from the tutorial system by means of several graphic routines and external program interfaces.  The system instantiates test frames to dump testing programs to a data buffer.  When the user decides to use the tutor or test module, each parameter reads individual programs and answers from the buffer.  Teaching and testing materials were designed in ASCII files or graphics with an identical serial number.  The courseware can be stored on the disk or CD-ROM.  The student's records and responses will be updated and kept in the database through external interfaces which can communicate with DOS, dBASE III, LOTUS 123, or other software.  The designs not only isolate courseware from the tutorial system but also allow the author to make changes for efficient and flexible lesson design.

Chinese Characters and Graphics Processing

        Because the Chinese language is based upon thousands of different characters, a computer that can apply Chinese characters must be equipped with a powerful CPU and flexibility for graphics generation and processing.  Several kinds of Chinese input systems have been developed in the past five years.  Though software compatibility and character input have hampered Chinese CAI, the development of hardware supports flexible, high resolution graphics which seem to offer a solution to the problem.

        There are two different methods of initiating graphic displays in IBM PC compatible computers.  One is bit-mapping in which images consist of pixels.  The other is object-oriented graphics in which images are created by lines or other geometric patterns.  For example, in EGA display, to draw a line from point A to point B, bit-mapped graphics light every pixel between point A and point B; object-oriented graphics apply an algorithm to link A and B.  Each method has advantages.  Bit-mapped images can display subtle and irregular shapes; object-oriented graphics can generate smooth patterns and require less disk space.  In the speed of display, however, bit-mapped images are faster than object-oriented graphics.  Based on the considerations of display speed and media space, it is obvious that bit-mapping is better for Chinese characters display.

        Most expert system shells take a large amount of memory in order to allow the full power of AI based reasoning techniques to be applied to them.  The Chinese input system also requires several hundred kilobytes of memory to be used successfully.  In addition to memory requirements, there are some interference problems between the expert system shell and the Chinese input systems.  In TITES, the design strategy is to transfer

Chinese characters to an image and simplify these problems.

        Numerous programming languages can be used to edit and display Chinese images together with English texts or graphics.  Sophisticated word processors can do similar work.  In order to grab Chinese characters, a set of programs were designed in which Chinese images can be transferred to Paint Brush PLUS, Dr. Halo III, AUTOCAD, or Lotus 123.  Such an approach is far less flexible than using the Chinese input system.  But it  increases the efficiency of the computer's processing and prevents interferences.  This is because the computer no longer has to reserve all Chinese characters and the Chinese input system is not stored in memory.  After finishing graphics transformation, the author can rotate, revise, stretch, enlarge, and position Chinese images within the graphic software environment.  The integration of images from various support tools successfully solves the problem of interferences between MS-DOS and the Chinese input system.

Text File Editor

            Many word processing programs have been marketed during the last decade.  Some of them are sophisticated, powerful, and expensive.  Although they can be used to create ASCII files, there is not an efficient tool to manage hundreds or thousands of ASCII files in current word processors.  The text file editor designed for courseware generating was devised from the concept of combining data management and word processing techniques.  Suppose each database is a book, then each record can be considered as a page in the book.  In the text file editor, an ASCII text file will be generated and managed as a record in a database.  The courseware author can construct a data structure and give a unique serial number to individual courseware.  A built-in text editor, a random-access index routine, and a dynamic data structure make up the program to perform operations of appending, browsing, coping, deleting, and updating records.  Other functions such as find, print, index generation, database information display, and data transfer can help the courseware author in editing work.  Figure 4-18 shows the main menu of the text file editor.  Using the file name as an index key, the courseware author can manipulate all the text files in a database identified by the serial number or specified title.

 

 

Figure 4-18.  Main menu of the Text File Editor.

            The text file editor is a conventional program written in Microsoft's Quick BASIC language.  With key file random access, the user can retrieve the desired file and directly read or write at any location on a database.  A data file, an index file, a data structure file, and an autoexec file make up the file structure of the text file editor.  The data file is a random file containing the text materials entered by the user.  The index file is a binary file containing pointers to the data file so that the user can access the record with a key value such as the ASCII text file name.  The data structure file is a sequential file containing the necessary data such as field number, field name, field length, and record capacity, which enable the program to construct a database.  The autoexec file provides the names of files that make up the database and other information which enables the courseware author to access the individual database whenever needed.

            As Quick BASIC supports dynamic arrays, the maximum capacity in a database can be expanded to 10,000 records.  The maximum number of fields in a record is 201; the maximum field length  is 142 characters.  Theoretically, there is no limitation to the number of databases except that imposed by the disk capacity.  The built-in word editor is a subprogram co-operated with the data management routines.  In order to fulfill the requirements of maximum field length and field number, the word processing routine was designed to scroll horizontally and vertically.  Some special features were designed in the word editor: a searching function that can be used to locate a word and a print function to print partial contents of an individual record, special records, or all records.  A portable ASCII file retrieval and backup feature in the edit function enables the courseware author to create final ASCII text file for TITES use.

            Index making is one of the useful functions in the text file editor.  There are two types of keyword indexing: page index and line index.  The program can accept keyword data either from the keyboard or from the prebuilt keyword data file.  The results of keyword indexing will be saved in an ASCII file which includes the name of the database, keyword source file, and word index list.  A password routine was attached to the editor which enables the authorized user access to the editor and prevents the courseware from unexpected modification.  The text file editor is also bilingual; it can be used to create English, Chinese, or Chinese-English hybrid text files.  Working with a resident graphic capture program, the text file editor can be used as a Chinese graphic generator for editing Chinese tutorial and test materials.

            In addition, the text file editor provides a temporary exit function which allows the courseware author to invoke MSDOS during modification.  Through the main menu of the text file editor, the tutorial system can be terminated by pressing the Esc key.  The

courseware author can run other programs at the DOS prompt or type "EXIT" to return to the text file editor to continue.


CHAPTER V

 

CONCLUSIONS AND RECOMMENDATIONS

 

                                                                                 The woods are lovely, dark, and deep,

                                                                                 But I have promises to keep,

                                                                                 And miles to go before I sleep,

                                                                                 And miles to go before I sleep.

 

                                                                                                                        Robert Frost

Conclusions

        One of the goals of the study was to create a programming-free administration and instruction environment to enable teachers to generate low-cost multimedia-based bilingual courseware.  Based on that perspective, the approach focused on pragmatic issues rather than on theoretical subjects.  With a basic understanding of computer programming, both expert systems techniques and conventional programming were used to construct the system.

        TITES, a prototype of an tutorial expert system, was developed earlier by the researcher.  The system can be used as a personal research tool; it also offers the researcher a pregnant experimental environment for programming.  Some concepts and ideas were implemented in the system, such as course-independent design, the use of graphics to present Chinese characters, combining database management and word processing techniques to create a text file editor, and the use of the hungry-tracing method in the hybrid inference of forward chaining and backward chaining. 

        During the development process, it was confirmed that developing an expert system without conventional programming was not efficient.  Although expert system development shells provide specific techniques for handling knowledge representations and inference mechanisms, its vast memory requirements and complicated inference processes

frequently results in garbage collection that slows down the system’s executing rate.  In

other words, the flexibility of the expert system is obtained at the cost of its efficiency.  On
the contrary, conventional programming techniques are more efficient but less flexible.

        The study also revealed that developing an expert system requires long-term efforts and a wide variety of background knowledge; hence, it is time-consuming and expensive. Figure 5-1 shows the development time of each implementation item.

 

 

Implementation Items

Time (hours)

 

Pilot Systems Design

380

 

Shell Study & Knowledge Base Design

1120

 

Text File Editor Design

960

 

Auxiliary Programs Design

160

 

Testing & Debugging

600

 

Total Time Used

3220

 

Figure 5-1.  Development Time in Hours: Where the Time Went.

            Tutorial expert systems can be regarded as a combination of art, education, and scientific technologies.  As more and more information and technologies become available with the use of computers, the process of learning will change.  As people become more knowledgeable, they want to know more and they want more indepth information.  Under the influence of the proliferation of microcomputers and the increasing costs of traditional education, using personal computers as auxiliary teaching and learning tools is not only a trend but a fact that educators must recognize.  Unless educators keep track of the efforts made by modern scientists and keep abreast of technological change, they will lose the battle as effective instructors.

            The design philosophy of TITES is to create an experimental environment for one of the knowledge navigators.  Turing (1950) said, "We can see only a short distance ahead, but we can see plenty there that needs to be done" (p. 35).  Research is like opening the door; there is always another door inside the open door.  Developing TITES not only verified the feasibility of applying expert system techniques in building educational software, it also holds the key to another door for future research.

 

Recommendations for Future Research

        The system described above is intended to be an instructional tool for teaching consultants.  It requires consideration of teaching material and student involvement in the form of an experimental tutor and the provision of information.  There are still several limitations in the current version of TITES.  Some students may prefer a more active learning style and feel excited if they can ask some questions and communicate with the computer in natural language.  As current linguistic and AI research have made some breakthrough in natural language understanding, it is believed that intelligent dialogue functions will become possible in the near future.

        Another problem stems from the limited feedback available.  Although feedback is one of the most important functions in the expert system, the assessment of the student's learning is still the weakness of the study.  Unlike a physics or chemical laboratory experiment, it is very difficult for the system to apply quality or quantity analysis of student performance.  Individual differences among students, such as learning curves, background knowledge, and personality, make feedback functions more difficult.

        Applying a student-learning model to install a more efficient feedback function will be feasible; however, it involves a relatively large student sample.  The tutorial system, equipped with a highly flexible teaching model which can survey students' learning curves, their potential and achievement, can provide the right teaching method for each student.  From this point of view, machine learning could be the way to a more flexible teaching model.

        Another way to make the tutorial system more intelligent is to develop intelligent-design courseware.  One of the important responsibilities of the teacher is to orchestrate student learning.  Designing courseware is not just to place lessons or tests in files or graphics.  The courseware author must set up the objectives for the lessons, write a behavioral objective for each concept to be taught, and decide how to measure whether the objectives have been met.  In other words, the author should make a logical analysis of the courseware.  This analysis includes applications of different domains: cognitive psychology, subject knowledge, instructional technology, Boolean analysis, syllogisms, and synthesis.  Once the courseware has been analyzed precisely and logically, effective feedback can be provided to direct the user toward the correct learning modes.

            Applying audio and video techniques to strengthen students' learning will be a valuable experiment for the tutorial system.  Sound is the sensation that is produced when auditory nerves are stimulated by vibrating air molecules.  It is an analog format signal.  To reproduce or simulate a sound effect on a computer, it is necessary to employ digitizing techniques.  Digitizing converts a sound from analog to digital format (see Figure 5-2).

 

Figure 5-2.  Digital sound format of a Taiwanese folk song presented in the MacRecorder.

            Image processing is another changing technique.  The 256 colors available with VGA cards on today's personal computers cannot present high quality graphics.  The 32-bit color or "true color" technique can provide 16.7 million colors to display perfect images.  The computer, however, requires much more data storage space and takes more time to display.  A more practical method of adding video effects to the tutorial system is to design an external video interface for the videodisc player.  A Constant Angular Velocity (CAV) videodisc can contain up to 54,000 frames of addressable video images.  Using a "search" command, users can move through the video frames sequentially or randomly.  By means of programming control, "Play" and multispeed commands display motion sequences at normal (30 frames per second), slow, or fast speed in forward or reverse.

            It can be expected that mass storage techniques and graphics coprocessors will be improved in the near future.  As a low-cost desktop video production system and the re-writable CD-ROM SCSI disk drive are marketed, it will become possible to integrate more and more multimedia technologies into tutorial expert systems.

        Expert systems provide a good experimental environment for intelligent tutorial modules.  It is possible to build an intelligent tutorial module based on meta-level knowledge in rule-based systems.  Any system that lacks feedback and automatic learning features cannot be a “true intelligent” system.  One of the next goals of TITES is to integrate CAI, database, expert system, and multimedia technologies to construct a knowledge base, with the ability to exhibit behavior classified as "an intelligent tutor."  The development of TITES is a definite effort in this direction.  The final goal of TITES is to be an artificial tutor expert that has the capability to teach, to communicate with the student, to know what to teach, and to be an assistant to the teacher.

 

 


REFERENCES

Alessi, S. M., & Shih, Y. -F.  (1989). The growth of computer-assisted instruction in       Taiwan schools.  Computers Education, 13(4), 337-341.

Ambron, S., & Hooper, K.  (1988).  Interactive multimedia: Visions of multimedia for       developers, educators and information provider.  Redwood, WA: Microsoft Press.

Barr, A., & Feigenbaum, A.  (1981).  The handbook of artificial intelligence (Vols. I-III).        Standford, CA: HeurisTech.

Bielawski, L., & Lewand, R.  (1988).  Expert systems development: Building PC-based       applications.  Wellesley, MA: QED Information Sciences.

Bielawski, L., & Lewand, R.  (1991).  Intelligent systems design: Integrating expert       systems, hypermedia, and database technologies.  New York: John Wiley & Sons. 

Bitzer, D. L.  (1986).  The PLATO project at the University of Illinois.  Engineering       Education, 77(3), 175-180.

Boardman, A.D., Cooper, B. W. J., Keeler, G. J., & Swage, J.  (1988).  Software       development for undergraduates in physics.  Computers Education, 12(1), 29-35.

Bush, V.  (1945, July).  As we may think.  Atlantic Monthly,  pp. 101-108.

Chang, Tung-Ying  (1985, September). Natural language study and automatic translation       system.  [Document in Chinese, title translated.]  Autotech Magazine,  pp. 73-82.

Chang, Tung-Ying  (1987a, July). The veil of intelligence.  [Document in Chinese, title       translated.]  Autotech Magazine,  pp. 97-102.

Chang, Tung-Ying  (1987b, December).  Semantic network and frame structure.        [Document in Chinese, title translated.]  Autotech Magazine,  pp. 211-221.

Chang, Tung-Ying  (1991, October).  Interactive multimedia.  [Document in Chinese, title       translated.]  Autotech Magazine,  pp. 111-116.

Chase, S.  (1954).  Power of words.  New York: Harcourt, Brace.

Chen, Ching-chih  (1989).  HyperSource on multimedia/hyperMedia technologies.        Chicago, IL: American Library Association.

Chomsky, N.  (1980).  Rules and representations.  New York: Columbia University       Press.

Ciser, S.  (1990).  Visual almanac: Interactive multimedia on the Mac.  Online, 14(2),       87-90.

Clancey, W. J.  (1987).  Knowledge based tutoring: The GUIDON program.  Cambridge,       MA: MIT Press.

Dever, S. Y., & Pennington, M. C.  (1989).  Computer capabilities underlying computer-      learner interaction.  In M. C. Pennington (Ed.), Teaching languages with computers:       The state of the art  (pp. 11-28).  La Jolla, CA: Athelstan.

D'Ignazio, F.  (1989).  Scholastic guide to classroom multimedia.  New York: Scholastic.

Duchastel, P.  (1989).  ICAI systems: Issues in computer tutoring.  Computers Education,      13(1), 95-100.

Duda, R. O.  (1979).  Model design in the PROSPECTOR consultant system for mineral       exploration.  In D. Michie (Ed.), Expert systems in the microelectronics age (pp. 153-      167).  Edinburgh, Scotland: Edinburgh University.

Elsom-Cook, M.T., & O'Malley, C. E.  (1990).  ECAL: Bridging the gap between CAL and intelligent tutoring systems.  Computers Education, 15(1-3), 69-81.

Feigenbaum, E., Buchanan, B., & Lederberg, J.  (1971).  On generality and problem       solving: A case study using the DENDRAL program.  In B. Meltzer & D. Michie       (Eds.), Machine Intelligence 6  (pp. 165-190).  New York: American Elsevier.

Fersko-Weiss, H.  (1985).  Expert systems: Decision-making power.  Personal       Computing, 9(11), 97-105.

Fletcher, J. D.  (1990).  Effectiveness and cost of interactive videodisc instruction in       defence training and education  (Report No. IDA Paper P-2372).  Alexandria,       Virginia: Institute for Defense Analyses.

Freedman, R. S., & Rosenking, J. P.  (1986).  Designing computer-based training    systems: OBIE-1:KNOBE.  IEEE Expert, 1, 31-38.

Gratch, B.  (1986).  Computer-assisted instruction: An unfulfilled promise.  Wilson       Library Bulletin, 61(4), 20-22.

Grigonis, R. W.  (1987, April).  MYCIN-like expert systems.  Dr. Dobb’s Journal, 

      pp. 42-82.

Hayes-Roth, F., Waterman, D.A., & Lenat, D.B.  (1983).  Building expert systems.        Reading, MA: Addison-Wesley.

Heid, G.  (1991).  A critical comparison of the Macintosh and IBM PC worlds.        MacWorld, 8(3), 120-129.

Johnson, B. L., Bergeron, R. D., & Malcolm, P.  (1990).  Modeling the teaching       consultant.  Computers Education, 14(2), 125-136.

Jones, C., & Fortescue, S.  (1987).  Using computers in the language classroom.  New       York: Longman.

Lawson, V. L.  (1988).  Using a computer-assisted instruction programs as an alternative       to the traditional library orientation / instruction tour: An evaluative study  (Doctoral       dissertation, The Florida State University, 1988).

Lemonick, M.  (1984).  Machines with living parts.  Science Digest, 92(2), 26.

Lu, S. C-Y.  (1989, January).  Artificial intelligence techniques for engineering       automation.  Unpublished lecture notes.  National Cheng Kung University.

Matta, K. F., & Kern, G. M.  (1989).  A framework for research in computer-aided

     instruction: Challenges and opportunities.  Computers Education, 13(1), 77-84.

McDermott, J.  (1982).  R1: A rule-based configurer of computer systems.  Artificial       Intelligence, 19 (1), 39-88.

Melle, W., Shortliffe, E. H., & Buchanan, B. G.  (1984).  EMYCIN: A knowledge       engineer's tool for constructing rule-based expert systems.  New York: Addison-      Wesley.

Miller, M. J.  (1989).  Multimedia technology is not just the buzzword of the year.        InfoWorld, 11(15), 56.

Minsky, M.  (1975).  A framework for representing knowledge.  In P. Winston (Ed.), The       Psychology of Computer Vision  (pp. 211-277).  New York: McGraw-Hill.

Newell, A., Shaw, J. C. & Simon, H. A.  (1957).  Empirical explorations of the logical       theory machine.  In E. A. Feigenbaum & J. Feldman (Eds.), Computers and thought        (pp. 109-133).  New York: McGraw-Hill.

Newell, A., & Simon, H. A.  (1972).  Human problem solving.  Englewood Cliffs, NJ:       Prentice Hall.

Newhard, R.  (1987).  Converting information into knowledge: The promise of CD-ROM.        Wilson Library Bulletin, 62(4), 38.

Nyns, R. R.  (1990).  An expert system in computer assisted language learning.        Computers Education , 15(1-3), 99-103.

Palmer, F. R.  (1981).  Semantics.  New York: Cambridge University Press.

Papert, S.  (1970).  Teaching children thinking.  In R. P. Taylor (Ed.), The computer in       the school: Tutor, tool, tutee  (pp. 161-176).  New York: Teachers College Press.

Piaget, J.  (1970).  Structuralism.  New York: Basic Books.

Rambally, G. K.  (1986).  The AI approach to CAI.  The Computing Teacher, 137, 39- 42.

Reasor, E.  (1985).  The expert system inference engine [Computer program manual].        Tampa, FL: Lightwave Consultants.

Schank, R. C.  (1972).  Conceptual dependency: A theory of natural language       understanding.  Cognitive Psychology, 3(4), 552-631. 

Schank, R. C.  (1984).  The cognitive computer.  Reading, MA: Addison-Wesley.

Shortliffe, E. H.  (1976).  Computer-based medical consultation: MYCIN.  New York:       American Elsevier.

Slobin, D. I.  (1979).  Psycholinguistics.  Glenview, IL: Scott, Foresman.

Texas Instruments.  (1987).  Personal consultant plus [Computer program manual].        Austin, TX: Texas Instruments, Data System Group.

Turing, A.  (1950).  Computing machinery and intelligence.  In E. A. Feigenbaum &      J. Feldman (Eds.), Computers and thought  (pp. 11-35).  New York: McGraw-Hill.

Walsten, D.  (1991).  NCSA video Macintosh: Producing scientific visualizations on a       desktop.  Data Link, 5(4), 22-26.

Wu, T. -H.  (1987).  CAI in Taiwan: State and problems.  Journal of Computer-Based       Instruction, 14(3), 104-106.

 

 



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