An intelligent, multimedia-supported instructional system

ExpertSystemsWithApplications,Vol.7, No. 3, pp. 451--465,1994 Copyright© 1994ElsevierScienceLtd Printedin the USA.All rightsreserved 0957-4174/94 $6.00 + .00

Pergamon

An Intelligent, Multimedia-Supported Instructional System n I N N Y P. K O N G Division of Information Engineering,Schoolof Electrical and ElectronicEngineering,NanyangTechnologicalUniversity,Singapore2263

Abstract-- This article describes the design of intelligent features in the instructional system lntelh'gent Computer-Assisted Instruction for Computer Engineering (ICAI-C), which was developed for supporting computer instruction at the Nanyang Technological University, Singapore. The 1CAI-C system supports a set of coursewares for teaching and learning various computer engineering subjects. It generally provides multimedia-supported lessons, exercises, tests, feedback on students' performance and progress, as well as discourse moderation in an interactive and adaptive manner. The system architecture, instructional design, student modeling, and knowledge-base organization are described. The system architecture consists of an intelligent hypermedia interface, a CA1 (computer-assisted instruction) shell, and a hyperbase. The hyperbase was designed to integrate all the data, knowledge, and multimedia objects used in the instructional process.

1. I N T R O D U C T I O N

further refinement and development of a complete system. The goal is to improve per-instructor productivity with the aid o f computer-assisted instructional tools.

1.1. Project Motivation FACED WITH AN enormous demand for instructor time because of a rapidly increasing student population and class sizes, the School of Electrical and Electronic Engineering (EEE), Nanyang Technological University (NTU), Singapore, was forced to look for alternatives that would provide effective instruction. In recent years, enrollment has reached a level of over 1,600 undergraduate students in the School, with an average of more than 150 students per class for the computer engineering option. Inevitably, additional administrative and communication overhead is incurred whenever class size and more instructors are involved although the instructor-student ratio (of approximately 1:10) remains the same. (Additional persons increase the number of communication paths and the complexity of communication throughout a project; see Pressman, 1992.) Naturally, an engineering school with a computer engineering program will explore the possibility of employing computer-based solutions to support teaching. A phased software engineering approach has been followed in the project implementation: (1) study of technology, (2) building and evaluation of prototypes, (3) construction of a baseline system, (4) implementation of sample coursewares and system evaluation, and (5)

1.2. Intelligence

Various kinds of intelligence have been incorporated into an Intelligent Computer-Assisted Instruction for Computer Engineering (ICAI-C) system, which was developed for supporting computer instruction at the Nanyang Technological University, Singapore. ICAIC includes (1) application of artificial intelligence (AI) techniques and knowledge-based system approaches in the instructional process (such as discourse planning and control, analysis of students' performance, diagnosis of students' errors, etc.), as well as (2) using hypermedia to provide a more intelligent system-learner communication process (in subject material exploration and presentation, multimedia-supported illustrations, etc.) (Bielawski & Lewand, 1991). 2. R E V I E W O F I N T E L L I G E N T INSTRUCTIONAL SYSTEMS 2.1. Computer-Assisted Instructional Systems

In general, computer-assisted instruction (CAI) is a genetic term covering all kinds of instructional systems regardless of the subject domain or their design. They are also referred to as computer-based instruction (CBI) or computer-aided learning (CAL). In particular, CAI refers to earlier instructional systems that are less adaptive to the particular requirements of individual students.

Requests for reprints should be sent to Hinny P. Kong, Divisionof Information Engineering Schoolof Electrical and ElectronicEngineering, NanyangTechnologicalUniversity,NanyangAvenue, Singapore 2263. 451

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H.P. Kong

Examples of CAI systems include PLATO (Bitzer & Skaperdas, 1970) and TICCIT (Merrill, Schneider, & Fletcher, 1980). These early CAI systems were mostly designed by experienced teachers for the specific domains in which they were expert. The systems were student oriented and mostly linear CAI; that is all users (learners) were treated the same and were led through an identical learning path. Later, branching CAI systems evolved; that is, there are different learning paths for students to follow, depending on their requirements. In fact, many present simulation and learning games also qualify as CAl.

2.2. Intelligent Instructional Systems Intelligent instructional systems (IIS) emerged as a result of increasing emphasis on the system-learner interactivity as well as the application of AI techniques in the instructional process. IIS is generally addressed as Intelligent Computer-Assisted Instruction (ICAI) or Intelligent Tutoring Systems (ITS). Frequently, these terms are used interchangeably. In particular, it may be differentiated that ICAI systems are more complete in terms of overall instructional strategy and coverage (O'Neil, Slawson, & Baker, 1991). They cover course planning, teaching, and tutoting as well as assessment aspects of instruction; whereas ITS may be viewed as a subset or branch of ICAI, as ITSs often focus only on the tutoring aspect of instruction. The term 1CAIwill be used as the overall design issues of IIS are discussed in this article. Examples oflCAI development include Steamer (Stevens & Roberts, 1983; Stevens, Roberts, & Stead, 1983), PROUST (Johnson & Soloway, 1983, 1987), LISP-tutor (Anderson & Reiser, 1985), GUIDON (Clancey, 1987), training systems in the military (Fletcher, 1988), and the ICAI-C system described here. ICAI is an effort to develop more powerful, accurate, and adaptive instructional systems by applying AI techniques and cognitive science principles. Compared with that of CAI systems, a more formalized system structure of ICAI systems emerges as the various system components become more distinctive: an expertise module (for the subject knowledge to be taught), a tutoting module (for instructional strategy), a student

model (for assessing students' understanding of the subject), and an intelligent user interface. A typical ICAI architecture (Burns & Padett, 1991) is presented in Figure 1.

2.3. Multimedia and CAI There is an increasing number of multimedia applications in CAI, including the apprentice model of learning, using an interactive video disc (Collins, Brown, & Newman; 1987); technical training, using an interactive video disc (Clark, 1988); Electronic Book, using hypertext (Yankelovich, Meyrowitz, & van Dam, 1987); MediaText for composition, using multimedia (Guzdial & Soloway, 1991); multimedia and CD-ROM training, using multimedia (Ng, 1992; Sudbury, 1992); multimedia-based photoelectric sensor vision tutorial (Tan & Cham, 1993); and others as described by Park and Self (1990). The reason for an expanded interest in multimediasupported CAI is understandable. According to research findings (Bainbridge, 1991; Fletcher, 1990; Greiner, 1991), courses delivered by interactive multimedia instruction (IMI) systems do as well or better than courses given by traditional instruction methods. Research results indicate that (1) students like IMI better than traditional methods, (2) students learn more from IMI than from more traditional methods, (3) students probably achieve more desired behavior change (improvement in retention) than when using traditional methods, and (4) IMI courses take less time than conventional methods. A study by the U.S. Department of Defense (Fletcher, 1990) also showed that IMI was more effective than computer-based instruction without multimedia. Hypermedia (an associative multimedia) is also well suited to education and training applications primarily because of the flexibility the technology offers over conventional CAI (Bielawski & Lewand, 1991). This flexibility is reflected in a nonlinear access to information and a transfer of program control from the system (the simulated teacher) to the learner. Unlike traditional CAI programs, hypermedia can be more adaptive overall, offering a more effective program navigation that is geared to the needs and interests of

DOMAIN

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I SIMULATKO[~H_ ~ LINSTRUCTI ONAL EXPERT ~--~

I~FEKFACE ~ '" INTELLIGENT

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STUDENT MODEL FIGURE 1. Architecture of a genera| ICAI system.

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the learner. A second benefit of using hypermedia in an education or training environment is its ability to foster greater exploration of relevant, but perhaps tangential, information. 3. ARCHITECTURE OF ICAI-C SYSTEM The instructional environment provided by ICAI-C is centered on a CAI System Shell designed to support various coursewares. A Multimedia Subsystem (which could be optional) is attached to the System Shell to provide hypermedia/multimedia capabilities in instruction. A knowledge base, discussed in Section 4.3, contains various kinds of expertise. The architecture of the ICAI-C system is shown in Figure 2.

3.1. CAI System Shell The CAI System Shell consists of a teaching subsystem, a tutoring subsystem, an exercise subsystem, and a teacher's corner. The main tasks handled by each subsystem are described next.

3.1.1. Teaching Subsystem. This module handles student administration and instructional management. Student administration tasks include student registration, placement, records processing, etc. Instructional management takes care of courseware library materials (subjects, lessons, exercises), instructional presentation, a consultant (help) facility, and student response monitoring. 3.1.2. Tutor Subsystem. This module handles student modeling, assessment, and advising. There are two es-

sential components in this subsystem: the Intelligent Tutor and the Student Model. The Intelligent Tutor performs progress and performance assessment. The state of understanding or knowledge of a student is analyzed, based on the test results of the student on related topics. It also performs any other analytical tasks as related to assessing a student's performance in any aspect or part of the CAI course. It provides feedback to students on their performance of specific learning tasks as well as on their overall progress in particular subjects. Remedial actions to moderate the learning paths of students will be taken if deemed necessary by the Tutor. Student modeling consists of building the learning behavior models of individual students based on their learning paths, test performance, and input. (Refer to Section 4.1.2. for more details.)

3.1.3. Exercise Subsystem. This module supports students' assessment and is mainly responsible for two tasks: providing tests and quizzes online, and supporting computer-based exercises. The Exercise Module draws from the test banks. Some exercises are common for subjects of the same domain (different programming languages, different methodologies to solve the same type of problems), while others are domain specific. For design or programming exercises, development tools (design editors, compilers, debuggers, etc.) are available to develop solutions to a given problem. A logical analyzer diagnoses any logical errors detected in the submitted solution. 3.1.4. Teacher's Corner. This is a facility that is inaccessible to students. Only teachers logged in with an

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FIGURE 2. Architecture of ICAI-C.

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3.3. Integrationof AI, Hypermedia, and CAI

authorized teacher ID are allowed access to this Corner. It provides the teacher with a convenient yet secure place for courseware editing, knowledge updating, and student records maintenance. The following is a general procedure for courseware development or revision under ICAI-C: 1. Collect course materials. 2. Organize the material into an appropriate knowledge structure or lesson sequence (lessons, topics, . . . . exercise). 3. Enter or load the course materials into the courseware data base. 4. Enter the course structure into the courseware knowledge base. (Refer to Section 4. I. 1 for more details.) 5. Revise tutoring or other knowledge of the system if necessary. (Refer to Section 4.3 for more details on the knowledge base.)

In this article, AI application in CA! refers to the incorporation of the expertise module and knowledgebased approach in the CAI system. The result of applying AI and hypermedia in CAI is an intelligent, hypermedia-based CAI system. The structure of this system is as shown in Figure 3. The key system component to integration of these three technologies (AI, hypermedia, and CAI) is the hyperbase. It is a repository of multimedia objects, a conventional data base, and a knowledge base. The hyperbase provides storage of CAI lessons, student models, pedagogical strategies, etc., convenient access to them, and it supports the hypermedia interface. The ICAI-C hyperbase was designed to provide a coherent instructional and operating environment for the support of the CAI Shell and the Multimedia Subsystem. For incorporating courseware into the system, it is necessary to add subject knowledge, courseware structure, and multimedia objects into the hyperbase. (Refer to Section 3.1.4 for the courseware development procedure.)

3.2. Multimedia Subsystem

4. DESIGN OF ICAI-C

The Multimedia Subsystem, shown in Figure 2, consists of a window manager and a multimedia manager. The window manager handles presentation and coordination of multiple displays in different windows. The multimedia manager handles details of presentation in terms of resources (objects) and scripts (presentation sequences). Using the CAI System Shell as the operating foundation, various coursewares or training kits can be incorporated into the system to form a multimedia training environment. (Refer to Sections 4.4 and 5.2 for more details.)

Since the immediate concern for the project is to increase teaching productivity over a short time frame rather than to improve quality of instruction, a more pragmatic approach has been followed in designing the system. The project tried to adopt proven or easy-toimplement CAI techniques as much as possible rather than spend a great amount of effort searching for new techniques. Some of the main design objectives are generality of design within the scope of domains, applicability, and expandability. The intelligence of the

lntelliEent ~_ Hyl~rmedia Interface ( User ) CAI

Shell

~ d p b j e ~ Hyperbase

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(f knowledgeZ'~ ~ base / / J

FIGURE 3. A CAI architecture integrating AI and hypermedia.

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system lies in the student model, tutoring strategies, friendly hypermedia interface, automatic checking of student exercises, and knowledge-based instructional support.

TABLE 2 Progress Factors of a Student

Progress Factor 1. MASTERED-LESSON

4.1. Pedagogical Design

2. ATTEMPTED-LESSON

A rather thorough treatment of the instructional design process, from task specification to development and evaluation, was presented by Venezky and Osin (1991b). Various approaches of design (system approach, evolutionary approach, mathematical/statistical approach, and a three-phase plan) were also discussed by Steinberg ( 199 lb). There are four major areas of design for the ICAI-C system: (1) courseware structure, (2) learner strategy (student model), (3) assessment design, and (4) instructional strategy. Each area is described in the following sections.

3. COMPLETED-LESSON

4.1.1. Courseware Structure. The ICAI-C system supports a library ofcoursewares. Each courseware teaches one subject (for example, programming in C). The subject (domain) knowledge in the courseware, consisting of both subject material and exercises for concepts and skills reinforcement, is structured in Table 1. Each courseware normally comes with its own test and exercise banks. A test bank may contain both tests and quizzes (elaborated in Section 4.1.3). 4.1.2. Student Model. A student model reflects the student's assessed knowledge state and hypotheses about conceptions and learning behavior leading to the student's current knowledge state. The Intelligent Tutor differentiates between a student's current behavior (deduced from his or her response to a question) and student model. The model provides a profile of the following attributes about a student's learning progress and pattern: 1. Progressfactor: Level of mastery/understanding for a subject lesson 2. Knowledge state." Level of understanding/knowledge for a topic or issue

4. PASSED-LESSON

Interpretation Lesson understood before the lesson Lesson attempted (but not completed) Lesson attempted and compk3tad Lesson attempted and graded as "passed"

3. Other attributes: Learning patterns such as timing and the frequency of trying various exercises Possible values of a student's progress factor for a CAI lesson are shown in Table 2. A student's knowledge state for a topic or issue is defined and interpreted in Table 3. Diagnostic rules generate the student's current behavior, while the following information sources maintain the student model (Clancey, 1988): • Direct questions asked of the student • Student problem-solving behavior or learning progress observed by the system • Assumptions based on the student's learning experience and performance (Details of assessment, diagnostics, and remedial treatment are covered in subsequent sections.) 4.1.3. Assessment Design. The assessment design of ICAI-C is similar to the algorithms for student assessment in Venezky and Osin (1991a). The main design considerations are (1) planning the appropriate checkpoints (or testpoints) for student testing, and (2) designing test questions and style. The adopted assessment scheme consists of the following checkpoints: 1. Initial placement test: Determines the appropriate lesson sequence to be followed by a student or the fight level of subject knowledge to begin instruction. (For instance, students with prior knowledge of object-oriented programming may be allowed to skip the related topics in the C++ Programming CAI

course.) TABLE 1 Structure of Domain Knowledge

Level

Unit

Example

1

Subject

Programming

1.5

Module*

I/O programming

2 3 4

Lesson Topic Issue

Data structure Objects Object reference

Exercise/ Problem Programming a problem Programming a routine Test Quiz Examples/ questions

* Optional dependingon the subject domain.

TABLE 3 Definition of a Student's Knowledge State of an Issue

Knowledge Interpretation

State

MASTERED TRYING GUESSED PASSED LEARNING FAILED MIXED UP

= = =

Known before the lesson Has a vague idea before the lesson Did not know Learned

=

Has a vague idea after the lesson

=

=

Did not understand

=

Mistook an issue for another

456 2. Continuous evaluation: Assesses the understanding of main points or concepts by means of end-of-topic quizzes; or assesses the understanding of techniques or skills covered in a lesson by means of end-oflesson tests. (For example, a student may be given a test to code a segment defining a certain type of object.) Test or quiz questions are randomly generated from the test bank. Quiz questions are normally set in multiple-choice style. 3. Mastery checking." Assesses the mastery of the subject knowledge by asking students to attack some major problems (for example, programming C++ solutions for rather complex problems) after they have satisfied the continuous assessment. 4. Diagnostics: Detects any misconception of a student who is performing a learning task or assigned exercise. The resources required for performing diagnosis include correct answers of test and quiz questions, knowledge and solutions of exercise problems, and the student model (described in an earlier section). Both immediate diagnosis and developmental diagnosis (Venezky & Osin, 1991a) are supported. 4.1.4. Instructional Strategies. The system basically supports two instructional strategies: (1) tutor-directed expository approach, and (2) student-directed expository approach (Venezky & Osin, 1991c). For most subjects, the tutor-directed expository instructional approach is followed by the ICAI-C system, in which a student is required to understand lessons before practicing exercises for reinforcement of the concepts taught in the lessons. Individualized tutoring support is provided through adaptive tutoring and advising services based on the current student model. The student-directed expository learning approach is normally used only for casual or informal learning such as a refresher course for prior student users of ICAI-C, experimental subjects, or trial coursewares. The Intelligent Tutor module makes use of the diagnostic tutor model as well as the coach model (Kearsley, 1987) for tutoring. It uses the diagnostic tutor model for general teaching tasks, but the coach model is used to support problemsolving tasks of students. These tutoring strategies allow the system to respond to students' progress in the following ways: • Advise students of their progress status • Offer hints and suggestions for problem solving • Plans remedial actions • Modify lesson contents to suit a student's level of knowledge and understanding 4.2. User Interface Design ICAI-C provides a window-based environment that uses a mixed pop-up menu and hypermedia user interface system. An online, context-sensitive help facility

H.P. Kong is also provided when possible. Warning messages and sound-enlightened remarks (with selected melodies) are provided by the system when appropriate. For the C++ courseware, learning of music programming comes with a simulation of a musical instrument and graphics programming comes with an actual demonstration. A timer is used by the system to monitor time spent in learning activities. It will detect any inactivity or excessive time spent in tests or exercises. When the time limit is exceeded, the system will issue a beeper-cumwarning message and, eventually, will shut down the CAI session if the student ignores the message.

4.3. Knowledge-Based Design The core of the system is a knowledge base in which various types of human expertise are captured. The knowledge base is used for both instructional (discourse) and tutoring purposes. Essentially, it contains the following types of knowledge: 1. Domain knowledge: Rules for teaching the subject material (for instance, C language syntax) and the precedence relationship of knowledge units (subject issues) 2. Diagnostic knowledge: Rules for assessing a student's state of understanding of the subject material 3. Tutoring knowledge: Rules for formulating tutoring advice, remedial actions, and discourse plans 4. Knowledge on course structure: Domain attributes such as the lesson sequence and the exercise set in the discourse 5. Problem knowledge." Description of the problems included in the exercises Diagnostic and tutoring knowledge are also used to update the student model elaborated in Section 4.1.2. The knowledge states are defined in Table 3. Figure 4 lists samples of production rules.

4.4. Multimedia Instructional Design 4.4.1. Multimedia Courseware. The structure of multimedia courseware (MMCW) is organized as folders of card stacks, including help and navigational tools. A folder contains many different windows that can be opened at any time and in any combination. Each window is opened to a stack of cards. Each card holds a frame or full screen of information. A stack is analogous to a lesson or topic. A student going through a multimedia CA~,ession can view more detailed information or course material by browsing through the different windows of cards in any sequence desired. While the student browses through the cards, background music is played. For cards in which animation of objects is used, voice is incorporated to narrate the course material. Visual effects such as zoom in, wipe left, and wipe fight are used as special effects for transition between cards and between stacks. The "pointing

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Dbmnostic r u l t m for issue undemtandlno RI: if qulz-enore :,59 then set premed-Issue; R2: If q ~ ~39 then set Issming-issue; R3: If q u i r e <40 then set failed-Issue. Dleonontle rules for tonic undarstandlno (similar to RI-R3 ). Remedial rules for mls~one~otlen of an Issue RIO: If f a l ~ then present Issue-remedial-scssion ,cRIX>; R l l : If f a l ~ _ _ m - 1 then repeat issue-sssslon; R12: If falled~._am_,e-2then present Issue--,Itemate-asssion; R13: If falled-lmme-3 then dlspley consult-human-tsscher-m~l; R20: If lasmlng-lesue then present lasue.revlew; R30: If mixed-up-issue then present difference-lasuel-lssue2. Remedial rules for mlsconcenflon of a tonic R40: If failed-topic then present topic-remedial-asasion
FIGURE 4. Samples of production rules.

hand" cursor is changed to a "watch" cursor for transition between cards and stacks to indicate when data are being accessed. 4.4.2. Authoring Tools. Ideal multimedia authoring software supports a graphical/hypermedia user interface; integration of graphics, images, and video; multimedia data base access; and script programming. Claris's HyperCard (Murdock, 1991), Silicon Beach's SuperCard (Himes, 1990), and Oracle's Oracle Card (Trutna, 1991) were considered for the ICAI-C project. HyperCard was first considered because of its common availability and acceptance as a de facto standard. SuperCard was chosen because of its compatibility with HyperCard and its enhanced capability. Oracle Card is planned for integration of multimedia with data base management and query tools and compatibility with HyperCard. Other authoring software commercially available include Macromedia's MacroMind Director for the Macintosh platform; Asymetrix's ToolBook and IBM's LinkWay and Storyboard Live! for the MS-DOS platform; AimTech's IconAuthor and Asymetrix's Multimedia ToolBook for Windows; and Macromedia's

AuthorWare Professional, Spinnaker's PLUS, and Owl International's Guide for both the Macintosh and IBM PC platforms. Some of the authoring systems are specifically designed for instructional purposes; however, the concerns are that their pedagogical design features are not customizable or their multimedia handling capabilities are limited. These authoring software were discussed and compared by Beckmann ( 1991), Sudbury (1992), Semrau (1992), and Michel (1993). A checklist (Berdel, Locatis, Weisberg, & Cart, 1990) is available for evaluation of authoring systems for hypermediabased instruction. Important evaluation factors are ease of use, courseware design flexibility, cost of delivery, and portability of the end product (that is, being able to port over conveniently and run the developed courseware on other operating platforms). 4.4.3. Resources. Resources (objects) include icons, sounds, cursors, cluts, external commands, and functions. These are independent building blocks for multimedia programming. Some resources, such as background cards, are shareable. The collection of colors used in a picture form a clut (color look-up table). Pictures can be scanned in or borrowed from existing doc-

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hip FIGURE 5. Navigation tools.

ument files. Both pictures and cluts can be edited. Sound effects, voice, and music can be recorded and reproduced. A significant amount of memory space is required for sound and video resources. Storage space for resource files might be reduced significantly if appropriate data compression techniques are used. 4.4.4. Scripts. Script design includes navigation, visual effects, sound effects, and pop-up menus. The types of navigational tools used in ICAI-C are arrow keys, graphics, on-screen fields, and on-screen buttons. The five navigational tools are shown in Figure 5. Visual effects add dimension to a CAI session by helping the user gain some spatial perception of what action a button, field, or graphics can incite. The types of visual effect adopted are iris, zoom, dissolve, barn door, checkerboard, scroll, and wipe. A script language, SuperTalk of SuperCard, is used to write the scripts for various visual or sound effects. Examples of scripts for visual and sound effects are shown in Figure 6. 5. IMPLEMENTATION OF ICAI-C Implementation issues such as project evolution, development tools used, implementation constraints, etc., are discussed in the following sections. The CAI System Shell and the Multimedia Subsystem, with its Hyperbase implemented as a multimedia data base management system (DBMS) working in conjunction with hypermedia software, are also covered. 5.1. Implementation Philosophy

IBM PC compatibles and Apple Macintosh are two types of microcomputers currently available in most schools and organizations. To allow portability of CAI coursewares across operating platforms, standard or universal development tools should be used to develop future coursewares. That is, the authoring or development tool(s) chosen should be able to support delivery of developed coursewares on either platform with minimum effort. This implementation approach enables wider distribution of courseware.

on OpcnCard playSOUND3 showgraphicID 102 endOpenCard

In addition to software programming (CAI Shell) and hypermedia script writing (Multimedia Subsystem), major implementation tasks include setting up the test bank and lesson evaluation. The test and exercise banks take time to be designed, collected, modified, organized, and built to a sizable collection so that a reasonably good and random sample of questions can be generated when needed. Lesson evaluation and fine-tuning is also a lengthy process that requires planning, feedback, and analytical effort. The developed ICAI-C prototypes and coursewares, sdb-debugger (Lira & Lim, 1990), C++ Programming (Kong, Ng, & Lim, 1991), LAN (Tay & Tay, 1992), and Lisp (Ng, 1993) run on IBM PC and Apple Macintosh hardware platforms. 5.2. Implementation of System Shell

For convenience of discussion, let us refer to the current system as ICAI-C II and the earlier prototype as ICAIC I. Development work on ICAI-C I, which focused on development of the knowledge base, was implemented in GCLISP, a popular LISP dialect running on PCs (Lim & Lira, 1990). After thorough consideration of execution, performance, and portability, ICAIC II was created by porting the original prototype to the Zortech C++ language system (Chok & Seah, 1991; Yeo & Yong, 1992). This version of C + + comes with a comprehensive library of function routines as well as graphics and sound tool kits. Another favorable factor with C + + programs is their ease of integration with other software systems such as relational DBMSs, object-oriented DBMSs, knowledge-based management systems (KBMSs), and expert system shells (Hu, 1989). 5.3. Implementation of Multimedia Subsystem

Implementation oflCAI-C II is based on the Macintosh hardware platform, chosen because of its inherent hypermedia capability and its readily available authoring tools. 5.3.1. Multimedia data base. A multimedia data base (MMDB) is incorporated into ICAI-C II to support quick and efficient retrieval of objects and information with a full range of cross-referencing and storing capabilities. Multimedia resources like drawings and audio tracks are stored as fields within a data base record. The software used for implementing MMDB are Silicon Beach's SuperCard and Oracle's DBMS, ORACLE

onCloseCard playstop visualeffectdissolve end CloscCard

FIGURE 6. Examples of scripts.

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for Macintosh. (Oracle Card is planned for use in further development and integration work.) These two pieces of software are complementary because they provide the facilities for multimedia data base management. SuperCard provides the capability to manage multimedia resources, while ORACLE takes care of the conventional data base management functions. Hyper.SQL, which comes with the ORACLE for Macintosh package, connects the two software layers by providing a hypermedia/data base interface. It consists of a series of external commands (XCMDs) and external functions (XFCNs) that can be called from SuperCard to access ORACLE data bases.

enough to contain a large number of high-resolution images or video clips, external disc drives or an optical disc (Write Once Read Many [WORM] or Erasable Rewritable Optical Disk [EROD]) must be used. Audio data also take up very large amounts o f storage. For example, an 80-MB hard disc holds only 120 minutes of low-quality digital sound. For that reason, mass storage devices are necessary for projects requiring extensive use of sound. In this project, the sound was segmented into a maximum of approximately 300 KB before it could be stored. For the LAN training kit developed in this project, the total storage space occupied is only about 12 MB, inclusive of 4 MB of image and sound.

5.3.2. Window Management. A unified window manager, supporting a training environment of multimedia, is incorporated to provide the means for text or image display under different windows and for the user to interact with applications via buttons, menus, etc. The software used for window management are SuperCard and the Macintosh window system. (Oracle Card is planned for use in future versions oflCAI-C, as it provides a common user interface for both Macintosh and MS-Windows platforms.) Different-sized windows were made available to suit presentations of different purposes such as main window, background, navigation, etc. An optimal window size is chosen to suit the information being presented.

5.3.5. Implementation Issues. Several implementation issues exist and include the following considerations. First, navigation between windows of different sizes will cause a "window moving" effect because the location property of windows in SuperCard is the top left corner. This effect can be prevented if the differentsized, invoked windows are nonovedapping. For navigation between windows, the OPEN command of the SuperTalk's scripts is used instead ofthe GO command. This achieves a smooth, multiwindow effect. Second, every picture has a color look-up table or clut. When it is used in any stack, the clut must be part of the stack resources. Otherwise the SuperCard default clut (system clut) will be used, and this gives unnatural colors to the pictures because certain colors may not be in the system clut. To prevent this undesired effect, every picture is followed by a blank card. Third, for a large number of images to be displayed, the imported image occupies a large amount of memory. A better approach is to use an XCMD code to draw the image on the screen instead of importing the PICT files. Fourth, note that playing background sounds while other tasks are performed slows the system. There must also be sufficient memory space to store sound, or no sound can be heard.

5.3.3. Image andAudio Capture. An image scanner is required to digitize and capture required pictures or images. Many scanners are available, and a suitable one should be chosen based on such criteria as color, resolution, and the types of image format it will produce. The scanner used for the ICAI-C project is a Microtek Color Scanner, and the software program is ColorScan. The image file formats supported include GIF, TIFF, and Apple's PICT. Image processing programs can be used for retouch or to modify the scanned images. The audio capture is through a MacRecorder; the software are HyperSound and SoundEdit. (MacRecorder and SoundEdit are from Farallon Computing.) With some effort, SuperTalk can be used to compose a song. After editing, captured image and audio are imported as resources to a SuperCard folder. 5.3.4. Storage Media. Image data, especially color image data, take huge amounts of storage space. For example, an 8-bit, 512 × 512 image takes up 256 KB of storage without compression, and a 24-bit image of the same size would take 768 KB. In the project, a small image object like "computer professor" takes 6 KB while a larger image takes about 60 KB. If the data base contains a small number of images, an internal hard disc may suffice. However, if the data base grows

5.4. Illustrations Figure 7 provides an overview of the window-based system, and the C + + courseware (menus) shows the topics of teaching multimedia (music and flash graphics) programming with demonstration. Figure 8 shows a sample of the Tutor's feedback and advice to a student on different windows. Figure 9 demonstrates the system's simulation of a musical instrument. Figure 10 displays the tasks of the C + + Programming Exercise Module. Figure 11 shows a hypermedia-supported CAI lesson depicting the topic of LANs with various navigational features. LAN is a topic well suited for hypermediabased CAI because it lends itself to the illustration of

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networks and hardware devices by using photo images and drawings. Figure 12 displays a photo image of a network device invoked by a learner for illustration. Figure 13 depicts the knowledge structure of the LAN courseware.

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an intelligent hypermedia interface, a student model and reasoning module, and a hyperbase integrating data, knowledge, and multimedia resources. Subjects for trial included the sdb-debugger, C + + Programming, LAN, and Lisp. Additional coursewares are planned and will be implemented for subjects of various domains or knowledge types in order to collect more feedback and data for validating the generality of the instructional design, the system integratability, and the

compatibility of the knowledge organization with various domains. The ICAI-C project is still under development. The areas of outstanding work are summarized next. 6 . 1 . M u l t i m e d i a Enhancement

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multimedia interface (for example, not only offering graphics, sound, music, and still images, but also mixing natural language, interactive video, illustrated messages, and multiple presentations). In particular, the following enhancements and expansions are planned: 1. A common hypermedia user interface for both Macintosh and MS-Windows platforms, using Or-

acle Card or equivalent as the top layer of the user interface subsystem 2. Full integration of multimedia resources, including video clips and resources of various formats within the multimedia data base 3. A knowledge navigational tool in the form of an online course map invocable for reorientation by a learner when lost in a CAI session ,m

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4. A multimedia CAI server and LAN that will support large groups of CAI students and will provide highquality multimedia objects restored on the client stations as well as a high-speed multimedia communication link between the server and its clients

6.2. Knowledge Base The contents and structure of the student model knowledge base may be further improved. Histories of related CAI lessons and past behavior patterns of students might be added to the student model to support additional tutor intelligence such as inductive reasoning capability. Frame-based and case-based knowledge representation/reasoning methods (Riesbeck & Schank, 1991; Woolf, 199 l) for the student model are worth additional investigation for the improvement of tutoring functionality and performance.

6.3. Evaluation An instructional system project, such as ICAI-C, would not be complete without continuous evaluation. To assess the effectiveness of the system, both formative and summative evaluations (Steinberg, 1991a) are planned that will include lesson and learning evaluations. Alessi and Trollip's discussion of lesson evalu-

ation (1985), which includes pilot testing and a quality review checklist, serves as a good guideline for implementation planning of the evaluation. Legree and Gillis (1991 ) also proposed three effectiveness evaluation criteria: (1) Human tutors and traditional group instruction are required conditions of ITS, (2) only extensive ITS applications will be evaluated, and (3) large groups of students are required for these evaluations. For evaluation of multimedia systems for CAI, Waterworth (1992) presented two interesting case studies of usability assessment.

6A. Integration and Expandability To exploit the full potential of the latest information technology for instructional purposes, the system must be fully integrated, for coherent distributed processing, with various support systems. Included are various multimedia systems, data base management systems, knowledge-based management systems, and computer networks. The design goal is to ensure that all computer-based instructional resources are available to most students, on or offcampus, via networked workstations, remote terminals, and dial-in communication facilities. Concurrently, the human instructional administration and support tasks are carded out in a distributed manner from the convenience and privacy of

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t e a c h e r s ' offices o r h o m e s . T h i s is t h e u l t i m a t e g o a l o f intelligent, m u l t i m e d i a - s u p p o r t e d i n s t r u c t i o n a l systems s u c h as I C A I - C . -

Acknowledgements--The author wishes to acknowledge the helpful suggestions and comments of Dr. James Ragusa of the University of Central Florida, and the constant encouragement of Dr. Efraim Turban of California State University--Long Beach. The ICAI-C project also received support from NEC (Singapore), Asian Sources Media Group of Hong Kong, and BlackCurrant Technology at different stages of the project.

REFERENCES Alessi, S.M., & Trollip, S.R. (1985). Lesson evaluation and revision. Computer-based instruction: Methods and development (pp. 374400). Englewood Cliffs, N J: Prentice Hall (2nd ed., 1992). Anderson, J.R., & Reiser, B.J. (1985). The LISP tutor. Byte, 10(4), 159-175. Bainbridge, S. ( 1991 ). Classroom training versus interactive videodisc/ CBT training: The final word. SALT Proceedings: Interactive Instruction Delivery, pp. 34-40. Beckmann, L ( 1991). Media integration: Authoring software makes it happen. PC Today, June, pp. 15-21. Berdei, R.L, 1.xx~tis, C., Weisberg~ M., & Carr, V. (1990). Evaluation of authoring systems for hypermedia-based instruction. Journal of Interactive Instruction Development, spring, pp. 11-15. Bielawski, L., & Lewand, R. (1991). Intelligent systems defined. In

Intelligent systems design: Integrating expert systems, hypermedia, and database technologies (pp. 17-62). New York: John Wiley & Sons. Bitzer, D., & Skaperdas, D, (1970). The economics of a large-scale computer-based education system: PLATO IV. In W. Hoitzman (Ed.), Computer-assisted instruction, testing and guidance. New York: Harper & Row. Burns, H., & Parlett, J.W. (1991). The evolution of intelligent tutoring systems: Dimensions ofdesign. In H. Burns, J.W. Parlett, & C.L. Redfield (Eds.), Intelligent tutoring systems: Evolutions in design (pp. 1-12). Hillsdale, NJ: Erlbaum. Chok, C.H., & Seah, L.C. ( 1991 ). Intelligent computer aided mstruction system (Project Report NTI-EEE-FYP). Singapore: Nanyang Technological Institute. Clark, H.H., Jr. (1988). Air force likes training on a "silver platter." Instructional Delivery Systems, July/August, pp. 32-33. Clancey, W.J. (1987). Knowledge-based tutoring, the GUIDON program. Cambridge, MA: MIT Press. Clancey, W.J. (1988). The role of qualitative models in instruction. In J. Self (Ed.), Artificial intelligence and human learning." Intelligent computer aided instruction. New York: Chapman and Hall. Collins, A., Brown, J.S., & Newman, S.E. (1987). Cognitive apprenticeship: Teaching the craft of reading, writing and mathematics. In L.B. Resnick (Ed.), Cognition and instruction: Issues and agendas. Hillsdale, N J: Erlbaum. Fletcher, J.D. (1988). Intelligent training systems in the military. In S.J. Andriole & G.W. Hopple (Eds.), Defense applications of artificial intelligence: Progress and prospects (pp. 174-189). Lexington, MA: Lexington Books. Fletcher, J.D. (1990). Effectiveness and cost of interactive videodisc instruction in defense training and education. ERIC microfiche ED326194, July. Greiner, J.M. (1991). Interactive multimedia instruction: What do the number show? SAL T Proceedings: Interactive Instruction Dehvery, pp. 100-104. Guzdial, M., & Soloway, E. (1991). Design of an educational multimedia Composition environment. Proceedings: AAAI-91 Workshop on Intelligent Multimedia Interfaces, Anaheim, CA, pp. 169-172.

Himes, A. (1990). SuperCard version 1.5: the personal software toolkit. San Diego, CA: Silicon Beach Software, Inc. Hu, D. (1989). C/C+ + for expert system. Portland, OR: Management Information Source. Johnson, W.L., & Soloway, E. (1983). PROUST: Knowledge-based program understanding (Report No. 285). New Haven, CT: Yale University, Computer Science Department. Johnson, W.L., & Soloway, E. (1987). PROUST: An automatic debugger for PASCAL programs. In G.P. Kearsley (Ed.), Artificial intelligence and instruction: Applications and methodology. (pp. 49-67). Reading, MA: Addison-Wesley. Kearsley, G. (1987). Overview. In G. Kearsley (Ed.), Artificial intelligence and instruction: Applications and methods (pp. 3-10). Reading, MA: Addison-Wesley. Kong, H.P., Ng, C.H., & Lim, M.H. ( 1991). An intelligent CAI System for C/C++ program development. Proceedings: lASTED Inter-

national Conference on Artificial Intelligence Applications and Neural Network, Zurich, Switzerland, pp. 137-140. Legree, P.J., & Giilis, P.D. (1991). Product effectiveness evaluation criteria for intelligent tutoring systems. Journal of Computer-Based Instruction. 18(2), 57-62. Lira, C.T., & Lira, K.H. (1990). Intelligent computer aided instruction (Project Report NT1-EEE-FYP). Singapore: Nanyang Technological Institute. Merrill, M.D., Schneider, E.W., & Fletcher, K.A. (1980). TICCIT. Englewood Cliffs, N J: Educational Technology Publications. Michel, S. (1993). Navigating the authoring system maze. New Media (special issue: 1993 multimedia tool guide), pp. 27-30. Riverton, N J: Hypermedia Communications. Murdock, E. (1991). HyperCard the easy way. Dubuque, IA: W. C. Brown. Ng, T.H. (1992). Multimedia training system (Project Report NTUSAS-FYP-064). Singapore: Nanyang Technological University. Ng, C.M. (1993). Intelligent CAI system: Lisp (Project Report NTUEEE-FYP-270). Singapore: Nanyang Technological University. O'Neil, H.F., Slawson, D.A., & Baker, E.L (1991). Design of a domain-independent problem-solving instructional strategy for intelligent computer-assisted instruction. In H. Burns, J.W. Parlett, & Redfield, C.L. (Eds.), Intelligent tutoring systems, evolution in design (pp. 69-104). Hillsdale, NJ: Erlbaum. Park, A.P., & Self, J.A. (1990). Towards "interactive video": A videobased intelligent tutoring environment. In C. Frasson & G. Gauthier (Eds.), Intelligent tutoring system: At the crossroads of artificial intelligence and education (pp. 56-82). Norwood, N J: ABLEX Publishing. Pressman, R.S. (1992). People-work relationship. In Software engineering: A practitioner's approach (3rd ed., pp. 4-105). New York: McGraw-Hill. Riesbeck, C.K., & Schank, R.C. (1991). From training to teaching: Technologies for case-based ITS. In H. Burns, J.W. Parlett, & C.L Redfield (Eds.), Intelligent tutoring systems: Evolutions in design (pp. 177-193). Hillsdale, N J: Erlbaum. Semrau, P. (1992). A comparison of authoring tools for developing interactive video. Proceedings of 3rd Annual California State University Artificial Intelligence Symposium, Long Beach, CA. Shafer, D. (1990). Using ORACLE with HyperCard. Carmel, IN: Hayden Books. Steinberg, E.R. (199 la). Evaluation. Computer-assisted instruction: A synthesis of theory, practice, and technology (pp. 194-200). Hillsdale, N J: Erlbaum. Steinberg, E.R. (1991b). Procedures for designing CAl. Computer-

assisted instruction: A synthesis of theory, practice, and technology (pp. 51-79). Hillsdale, NJ: Erlbaum. Stevens, A.L., & Roberts, B. (1983). Quantitative and qualitative simulation in computer-based training. Journal of ComputerBased Instruction, 10(1), 16-19. Stevens, A.L., Roberts, B,, & Stead, L. (1983). The use of a sophisticated interface in computer-assisted instruction. IEEE Computer Graphtcs and Applications, 3(2), 25-3 I.

Intelligent, MM-Supported Instructional System Sudbury, S. (1992). Integrating multimedia technology into instruction. M.A. project, California State University, Long Beach. Tan, C.K., & Cham, P.L. (1993). A multimedia-based photoelectric sensor vision tutorial system (ProjectReport NTU-EEE-FYP-4 24). Singapore: Nanyang Technological University. Tay, B.H., & Tay, L.E. (1992). Multi-media system for training (ProjectReport NTU-EEE-FYP-244). Singapore: Nanyang Technological University. Trutna, R., & Mone, C. (1991). Oracle card for Macintosh. Oracle Corporation. Venezky, R., & Osin, L. ( 199 la). Algorithms for student assessment. The intelligent design of computer-assisted instruction (pp. 165188). New York: Longman Publishing. Venezky, R., & Osin, L. ( 1991b). Instructional design. The intelligent design of computer-assisted instruction (pp. 96-115). New York: Longman Publishing. Venezky, R., & Osin, L. ( 1991c). The mysteries of planning learning experience: Instructional strategy. The intelligent design of computer-assisted instruction (pp. 61-65). New York: Longman Publishing. Waterworth, J.A. (1992). Multimedia interaction with computers: Human factors issues. New York: Ellis Horwood. Woolf, B. (1991). Representing, acquiring, and reasoning about tutoring knowledge. In H. Burns, J.W. Parlett, & C.L. Redfield (Eds.), Intelligent tutoring systems: Evolutions in design (pp. 127149). Hillsdale, NJ: Eribaum. Yeo, K.Y., & Yong, T.L. (1992). Intelligent CAI system (Project Report NTU-EEE-FYP-243). Singapore: Nanyang Technological University. Yankelovich, N., Meyrowitz, N., & van Dam, A. (1987). Reading and writing the Electronic Book. IEEE Computer, 20(9), 15-30.

Additional R e f e r e n c e s Bunzel, M.J., & Morris, S.K. (1992). Multimedia applications development using D VI technology. New York: Intel/McGraw-Hill.

465 Fink, P.K. (1991). The role of domain knowledge in the design of an intelligent tutoring system. In H. Bums, J.W. Parlett, & C.L. Redfield (Eds.), Intelligent tutoring systems: Evolutions in design (pp. 195-224). Hiiisdale, NJ: Erlbaum. Johnson, W.L. (1986). Intention-based diagnosis of novice programming errors, research notes in artificial intelligence. Los Altos, CA: Morgan Kaufmann. Murray, W.R. (1987). Automatic program debugging for intelligent tutoring systems. Computational Intelligence, 3(l), l - 16. Parsaye, K., Chignell, M., Khoshafian, S., & Wong, H. (1989). Intelligent databases: Object-oriented, deductive hypermedia technologies. New York: John Wiley & Sons. Reiser, B.J., Kimberg D.Y., Lovett, M.C., & Ranney, M. (1992). Knowledge representation and explanation in GIL, an intelligent tutor for programming. In J.H. Larkin & R.W. Chabay (Eds.), Computer-assisted instruction and intelligent tutoring systems: Shared goals and complementary approaches (pp. 111-150). Hilisdale, NJ: Erlbaum. Sack, W., & Soloway, E. (1992). From PROUST to CHIRON: ITS design as interactive engineering; intermediate results are important! In J.H. Larkin & R.W. Chabay (Eds.), Computer-assisted instruction and intelligent systems: Shared goals and complementary approaches (pp. 239-274). Hillsdale, NJ: Erlbaum. baum Associates. Silverman, B.G. (1991). Expert critics: Operationalizing the judgement/decision-making literature as a theory of bugs and repair strategies. Knowledge Acquisition, 3, 175-214. Silverman, B.G. (1992). Human-computer collaboration. Human Computer Interaction, 7(2). Soloway, E., Rubin, E., Woolf, B., Bonar, J., & Johnson, W.L. (1983). MENO II: An Al-based programming tutor. Journal of ComputerBased Instruction, 10(! & 2), 20-34. Toy, D. (1992). 4th annual PC AI product guide: Training, consulting, and certification. PCAL 6(5), 55-56. Winston, P.H. (1984). Artificial intelligence (2nd ed.). Reading, MA: Addison-Wesley.