Las propuestas de los Planes de Ordenamiento Territorial

Naturally there are differences between instructional design for people and instructional design for artificial neural networks. These differences arise in several ways.

The human student is a relatively fixed entity, the underlying mechanisms governing learning cannot readily be changed by the teacher. When using ANNs the learner or pupil is not fiX�d. In fact the majority of work on training ANNs has been focused on improving "the pupil" with scant reference to

instructional design [Ooyen & Nienhuis

1 992,

M�ller

1 993,

Riedmiller & Braun

1 993,

Tollenaere

1 990,

Fahlman & Lebierer

1990] .

It is not suggested that this is not worth doing but only that it is but one aspect of the problem and work on other aspects is also worthwhile.

Secondly, in training people a teacher has the option of stepping outside the actual training exercise to help the learner who is having difficulty. Tills ability to use verbal strategies to rapidly change tack can play a significant role in teaching. For example a teacher might say;

"What I am trying to get across is that the sort of movement required here lS ...

and perhaps if you tried doing X it might help you learn this task more quickly"

In ID for ANNs this can only be done in much more limited ways. Suggestions of different ways the pupil might behave have to be translated into changes in the actual learning algorithm. Any appeal to the learner to approach learning this particular task in a special way has to be translated into changes in examples, presentation order, alternative error calculations, structural modifications to the network or some other explicit feature of the neural network or its training environment.

B efore discussing ID further it is perhaps appropriate to comment on the present trend in educational research to focus on assisting the pupil to learn rather than teaching [Entwistle & Ramsden

1983] .

This shift in focus was probably necessary after a long history of educational research on teaching, which with

Chapter 6 Instructional Design and Training Artificial Neural Networks 6.7

time led an over emphasis on the role of the teacher as the active participant. The learner is the one in which it is hoped the change will take place and hence it should be the learner who is the major focus during the learning process. In the study of artificial neural networks no such over emphasis on the teacher has

occurred. In training ANNs the main focus has been on modifying the pupil to

make it better at learning, an option not normally available in human teaching ! For this reason in this thesis an emphasis is placed on the training environment for ANN s, that is, on teaching.

The following definition of teaching has been used;

Teaching is the process of arranging the form of presentation, variety of material, order of examples and any other relevant aspects of the learning environment in such a way as to make it as easy as possible for the learner to learn.

This differs from the standard dictionary definition, to enable or cause to do, b y stressing the importance of enabling in contrast to causing. It also defines and limits the type of methods to be considered in this work.

Another major difference between human and machine learning, is that, in most training situations involving people, the instructor knows how the problem should be solved. It is merely a matter of transferring the ability from the teacher

to the student. For example,

if

the task is to solve simultaneous equations, the

instructor knows how this can be done. The desired outcome is that at the end

of the training the student will be able to solve simultaneous equations using

the same method. In other tasks the solution method may be unknown or only

approximately known, for example teaching someone to play a particular shot in table tennis. In these cases the teacher is trying to assist the student to discover a solution method and hence gain the desired skill. The required net result is known but not the method of obtaining it. This second situation is more akin to training artificial neural networks. Where an explicit solution method or algorithm is known, other non neural network approaches are generally preferable. This is not always the case; a known algorithmic method may be too slow or a NN solution may be used to provide an inherently parallel implementation.

Another difference to keep in mind is that ANNs can be returned to an initial or earlier state. In fact the standard approach in training ANNs is to revert to a new set of random weights at the beginning of each new task. This is not possible in

Chapter 6 Instructional Design and Training Artificial Neural Networks 6.8

human training, we can never go back. For this reason it is important, with people, to ensure that incorrect responses are never practiced. They will have to be unlearned later. Tiris may not be so important in training ANNs as a set of weights can be saved and later restored, however

if

the training of ANNs is to be made more like human training this may involve the loss of the capability to reload weights. _If this should turn out to be the case then it will become important to ensure that ANNs do not practice incorrect responses during training.

Lastly, the ANNs available at the present time are relatively limited in their capabilities . Tills will sometimes restrict our interest to a subset of the components of a particular ID theory. For example in the forthcoming discussion of Component Display Theory only a subset of the performance­ content matrix proposed in that theory is relevant to training present ANNs. Therefore the parts of the theory relevant to that subset will be discussed in more detail than the rest of the theory.

Chapter 6 Instructional Design and Training Artificial Neural Networks

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