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

2

Yikes!

Can we

make

better

use of

educational

technology

by studying

its past?

3

4

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

5

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

The

Bible

of 42

Lines

printed

1456

6

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Johannes Gutenberg 1456

1398-1468

8

Monitorial School 1839

Joseph Lancaster, 1778-1838

St. Louis

Museum

1905

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Cleveland Museum 1909

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Maria Montessori

1870-1952

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Montessori Method 1911

Respect for the learner’s individuality

Encouragement of the learner’s freedom

13

John Dewey

1859-1952

14

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

15

Edward L. Thorndike

1874-1949

16

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.

17

Thorndike Principles 1913

Self-activity

Interest (motivation)

Preparation and mental set

Individualization

Socialization

18

Jean Piaget

1896-1980

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Piaget Conservation 1969

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Piaget Conservation 1969

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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

22

Sidney L. Pressey 1924

A self-scoring multiple-choice apparatus

that gives tests and scores – and teaches

23


24


A multiple-choice device that omits items

from further presentation once the student

can consistently answer them correctly.

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Pressey Variant by Skinner

1958

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Rheem-Califone Variant

1959

Pressey

Punch-

board

1950

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Pressey Punchboard 1950

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B.F. Skinner

1904-1990

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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?

31

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

32

Skinner Machine 1954

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Skinner Machine 1954

33

34

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.

35

Skinner Disk 1958

36

Skinner Disk 1958

36

37

Skinner Variant by Porter

1958

38

James G. Holland 1960

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Holland Discrimination Task

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“A Teaching Machine...

...for

Lower

Organisms”

Holland,

1960

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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

42

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

43

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

Teacher

Super-

vision

Porter,

1958

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Briggs Subj.-Matter Trainer

1958

46

Enter the Computer

1959

47

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

48

Adaption to Failure

48

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Adaption to Success

49

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Intrinsic Programming 1

Norman A. Crowder, 1960

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Types of Test Error

56

Type I: False Negative Error

True master classified as a non-master

Student takes unneeded instruction

Less serious than a Type II error

57

Type II: False Positive Error

True non-master classified as a master

Student skips needed instruction

More serious than a Type I error

58

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

60

Decision Model 2

Basic

Sequential

Testing

Criterion-

Referenced

Decision

Model

0% Correct

100% Correct

Mastery Criterion Score

Non-Mastery Criterion

Score

Uncertainty Interval

61

Decision Model 3

Sequential

Testing

Criterion-

Referenced

Decision

Model

With Decision

Certainty

Factor Based

on Test

Length0% Correct

100% Correct

Mastery Criterion Score


Score

Variable Size

Uncertainty Interval}

62

Decision Model 4

Sequential

Testing

Criterion-

Referenced

Decision

Model

For Tests

1-2 Items

Long

All scores fall

within the

uncertainty

interval

}

}

}

63

Decision Model 5

Sequential

Testing

Criterion-

Referenced

Decision

Model

For Tests

4 Items

Long

Uncertainty interval

}

}}

Non-Mastery Classification

25%

0%

64

Decision Model 6

Sequential

Testing

Criterion-

Referenced

Decision

Model

For Tests

6 Items

Long


}

}


33%

}

0%

65

}}}

Decision Model 7

Sequential

Testing

Criterion-

Referenced

Decision

Model

For Tests

9 Items

Long



40%

100%

0%

Mastery

Classification

66

Decision Model 8

Sequential

Testing

Criterion-

Referenced

Decision

Model

For Tests

15 Items

Long

Uncertainty interval}


45%

100%

0%

Mastery

Classification90%

67

Influencing Factors

Fixed

Mastery Criterion


Allowable Probability of a Type I Error

Allowable Probability of a Type II Error

Variable

Test Length

68

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

69

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

70

Pretest Decision Rules

Numberof Items

AnsweredCorrectly

Number of Items Presented

71

Posttest Decision Rules

Numberof Items

AnsweredCorrectly

Number of Items Presented

72

Decision Rules Compared

Pretest Posttest

73

Technology’s Role 1

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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

80

Jesse M. Heines, Ed.D.

Dept. of Computer Science

Univ. of Massachusetts Lowell

[email protected] or [email protected]

http://www.cs.uml.edu/~heines

Primary

Reference

81

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.

Computing Before Computers

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