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.
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