Computing Before Computers
Transcript of Computing Before Computers
Computing
Before Computers
A Look at the History of Educational Technology
Jesse M. Heines, Ed.D.
Dept. of Computer Science
Univ. of Massachusetts Lowell
MIT CAES Tech Lunch, March 13, 2001
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Predicting the Future
Anyone who tries to draw the future in
hard lines and vivid hues is a fool. The
future will not sit for a portrait. It will
come around a corner we never noticed,
take us by surprise.
– George Leonard, 1968
Education and Ecstasy
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Educational Device Wish List
Easy-To-Use – no training needed
Easy-To-Read – typeset-quality text
Flexible – graphics and images
Small – carry in a pocket
Portable – doesn’t need to be plugged
in, doesn’t even need batteries
Reliable – never breaks down
Durable – different students can share it
and it can be reused year after year
Inexpensive – under $5/student
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Montessori Method 1911
Respect for the learner’s individuality
Encouragement of the learner’s freedom
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Dewey Psychology 1896
Stimulus and response are not fully
independent events, they are organically
related
Learning involves two-way interaction
between learners and their environment
Learners’ experiences within their
environments are the basis of the
meanings they deduce and the goals and
actions they pursue
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Thorndike Laws 1913
Law of Exercise
The more often a stimulus-induced response
is repeated, the longer it will be retained.
Law of Effect
A response is strengthened if followed by
pleasure and weakened if followed by
displeasure.
Law of Readiness
In any given situation, certain “units” are
more predisposed to conduct than others
due to the structure of the nervous system.
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Thorndike Principles 1913
Self-activity
Interest (motivation)
Preparation and mental set
Individualization
Socialization
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Thorndike on Technology
If, by a miracle of mechanical ingenuity,
a book could be so arranged that only to
him who had done what was directed on
page one would page two become
visible, and so on, much that now
requires personal instruction could be
managed by print.
– Education, 1912
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Sidney L. Pressey 1924
A self-scoring multiple-choice apparatus
that gives tests and scores – and teaches
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Sidney L. Pressey 1927
A multiple-choice device that omits items
from further presentation once the student
can consistently answer them correctly.
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Skinner Questions 1953
What behavior is to be established?
What reinforcers are available?
What responses are available?
How can reinforcements be most
efficiently scheduled?
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Programmed Instruction
The whole process of becoming compe-
tent in any field must be divided into a
very large number of very small steps, and
reinforcement must be contingent upon
the accomplishment of each step... By
making each successive step as small as
possible, the frequency of reinforcement
can be raised to a maximum, while the
possible aversive consequences of being
wrong are reduced to a minimum.
– Science and Human Behavior, 1953
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Skinner Disk 1958
Student at work in a
self-instruction room.
Material appears in
the left-hand
window. Student
writes his response
on a strip of paper
exposed at the right.
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Skinner: “We’re Done”
There is a simple job to be done. The
task can be stated in concrete terms.
The necessary techniques are known.
The equipment needed can easily be
provided. Nothing stands in the way
but cultural inertia.
– B.F. Skinner, 1954
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Teaching Rats and Humans
There is, to the best of my knowledge,
no science of maze running to be taught.
... The only reason a rat should turn to
the right rather than the left at a certain
point is that it is that turn which leads to
reinforcement. No better reason can be
learned because there is none.
Students sometimes pass courses in
logic and mathematics in the same way. ...
– John W. Blyth, 1960
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Teaching Rats and Humans
A student who gives a particular
response merely because that is the one
the teacher reinforces has not learned a
subject... The reason for giving some
response must be more than the fact that
it causes the teacher to say “correct.”
Our experience has convinced us that
the most effective method of presenting a
program of questions and answers is a
machine using microfilm and designed for
individual use.
– Heines paraphrase of Blyth, 1960
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Crowder Self-Instructional
“The program of materials presented can be
arranged so that the presentation of new items
depends upon the student’s own performance.”
1960
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Type I: False Negative Error
True master classified as a non-master
Student takes unneeded instruction
Less serious than a Type II error
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Type II: False Positive Error
True non-master classified as a master
Student skips needed instruction
More serious than a Type I error
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Decision Model
To minimize the
probability of an
error, we use a
decision model
that takes these
error conditions
into account.
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Decision Model 1
Standard
Criterion-
Referenced
Decision
Model
0% Correct
100% Correct
Mastery and
Non-Mastery
Criterion Score
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Decision Model 2
Basic
Sequential
Testing
Criterion-
Referenced
Decision
Model
0% Correct
100% Correct
Mastery Criterion Score
Non-Mastery Criterion
Score
Uncertainty Interval
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Decision Model 3
Sequential
Testing
Criterion-
Referenced
Decision
Model
With Decision
Certainty
Factor Based
on Test
Length0% Correct
100% Correct
Mastery Criterion Score
Non-Mastery Criterion
Score
Variable Size
Uncertainty Interval}
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Decision Model 4
Sequential
Testing
Criterion-
Referenced
Decision
Model
For Tests
1-2 Items
Long
All scores fall
within the
uncertainty
interval
}
}
}
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Decision Model 5
Sequential
Testing
Criterion-
Referenced
Decision
Model
For Tests
4 Items
Long
Uncertainty interval
}
}}
Non-Mastery Classification
25%
0%
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Decision Model 6
Sequential
Testing
Criterion-
Referenced
Decision
Model
For Tests
6 Items
Long
Uncertainty interval
}
}
Non-Mastery Classification
33%
}
0%
65
}}}
Decision Model 7
Sequential
Testing
Criterion-
Referenced
Decision
Model
For Tests
9 Items
Long
Uncertainty interval
Non-Mastery Classification
40%
100%
0%
Mastery
Classification
66
Decision Model 8
Sequential
Testing
Criterion-
Referenced
Decision
Model
For Tests
15 Items
Long
Uncertainty interval}
Non-Mastery Classification
45%
100%
0%
Mastery
Classification90%
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Influencing Factors
Fixed
Mastery Criterion
Non-Mastery Criterion
Allowable Probability of a Type I Error
Allowable Probability of a Type II Error
Variable
Test Length
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We want to be very sure the student is a
master before letting instruction be
skipped.
Note the low probability of a
Type I (false positive) error.
For Pretests
Mastery Criterion 90%
Non-Mastery Criterion 65%
Type I Error Probability 0.025
Type II Error Probability 0.058
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The student has already gone through the
material at least once, so the criteria can
be loosened.
Note that the probability of a
Type I error is twice as high as before.
For Posttests
Mastery Criterion 85%
Non-Mastery Criterion 60%
Type I Error Probability 0.050
Type II Error Probability 0.104
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The Best Teacher
The best teacher uses books and
appliances as well as his own insight,
sympathy, and magnetism.
– Edward L. Thorndike
Education , 1912
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Jesse M. Heines, Ed.D.
Dept. of Computer Science
Univ. of Massachusetts Lowell
[email protected] or [email protected]
http://www.cs.uml.edu/~heines
82
Primary References
Lumsdaine, A.A. and Glaser, Robert,
1960. Teaching Machines and
Programmed Learning: A Source Book.
National Education Association of the
United States, Washington, D.C.
Saettler, Paul, 1968. A History of
Instructional Technology. McGraw-Hill,
Inc., New York, NY.
Saettler, Paul, 1990. The Evolution of
American Educational Technology.
Libraries Unlimited, Englewood, CO.
83
Some Secondary References
Pressey, S.L., 1926. A simple apparatus
which gives tests and scores – and teaches.
School and Society 23:586, March 20,
1926.
Pressey, S.L., 1927. A machine for
automatic teaching of drill material. School
and Society 25:645, May 7, 1927.
Skinner, B.F., 1954. The science of learning
and the art of teaching. Harvard Educational
Review 24(2).
Skinner, B.F., 1958. Teaching machines.
Science 128, October 24, 1958.