Computing Before Computers A Look at the History

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

Yikes! 2

Can we make better use of educational technology by studying its past? 3

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 4

Educational Device Wish List n n n n 5 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

Johannes Gutenberg 1398 -1468 7 1456

Monitorial School 8 1839 Joseph Lancaster, 1778 -1838

St. Louis Museum 1905 9

Cleveland Museum 1909 10

Maria Montessori 1870 -1952 11

Montessori Method 1911 n n 12 Respect for the learner’s individuality Encouragement of the learner’s freedom

John Dewey 1859 -1952 13

Dewey Psychology 1896 n n n 14 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

Edward L. Thorndike 1874 -1949 15

Thorndike Laws 1913 n Law of Exercise n n Law of Effect n n A response is strengthened if followed by pleasure and weakened if followed by displeasure. Law of Readiness n 16 The more often a stimulus-induced response is repeated, the longer it will be retained. In any given situation, certain “units” are more predisposed to conduct than others due to the structure of the nervous system.

Thorndike Principles n n n 17 Self-activity Interest (motivation) Preparation and mental set Individualization Socialization 1913

Jean Piaget 1896 -1980 18

Piaget Conservation 1969 19

Piaget Conservation 1969 20

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 21

Sidney L. Pressey 22 1924 A self-scoring multiple-choice apparatus that gives tests and scores – and teaches

Sidney L. Pressey 23 1926

Sidney L. Pressey 1927 A multiple-choice device that omits items from further presentation once the student can consistently answer them correctly. 24

Pressey Variant by Skinner 1958 25

Rheem-Califone Variant 1959 26

Pressey Punchboard 1950 27

Pressey Punchboard 28 1950

B. F. Skinner 1904 -1990 29

Skinner Questions 1953 n n 30 What behavior is to be established? What reinforcers are available? What responses are available? How can reinforcements be most efficiently scheduled?

Programmed Instruction The whole process of becoming competent 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. 31

Skinner Machine 1954 32

Skinner Machine 1954 33 33

Skinner Disk 1958 Student at work in a self-instruction room. Material appears in the lefthand window. Student writes his response on a strip of paper exposed at the right. 34

Skinner Disk 1958 35

Skinner Disk 1958 36 36

Skinner Variant by Porter 37 1958

James G. Holland 38 1960

Holland Discrimination Task 39

“A Teaching Machine. . . for Lower Organisms” Holland, 1960 40

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 41

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. . 42 – John W. Blyth, 1960

Teaching Rats and Humans 43 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 Supervision Porter, 1958 44

Briggs Subj. -Matter Trainer 1958 45

Enter the Computer 1959 46

Crowder Self-Instructional 1960 “The program of materials presented can be arranged so that the presentation of new item depends upon the student’s own performance 47

Adaption to Failure 48

Adaption to Success 49

Intrinsic Programming 1 50 Norman A. Crowder, 1960

Intrinsic Programming 2 51 Norman A. Crowder, 1960

Intrinsic Programming 3 52 Norman A. Crowder, 1960

Intrinsic Programming 4 53 Norman A. Crowder, 1960

Intrinsic Programming 5 54 Norman A. Crowder, 1960

Types of Test Error 55

Type I: False Negative Error n n n 56 True master classified as a non-master Student takes unneeded instruction Less serious than a Type II error

Type II: False Positive Error n n n 57 True non-master classified as a master Student skips needed instruction More serious than a Type I error

Decision Model To minimize the probability of an error, we use a decision model that takes these error conditions into account. 58

Decision Model 1 Standard Criterion. Referenced Decision Model 59 100% Correct Mastery and Non-Mastery Criterion Score 0% Correct

Decision Model 2 Basic Sequential Testing Criterion. Referenced Decision Model 60 100% Correct Mastery Criterion Score Uncertainty Interval Non-Mastery Criterion Score 0% Correct

Decision Model 3 Sequential Testing Criterion. Referenced Decision Model With Decision Certainty Factor Based on Test Length 61 100% Correct } Mastery Criterion Score Variable Size Uncertainty Interval Non-Mastery Criterion Score 0% Correct

Decision Model 4 Sequential Testing Criterion. Referenced Decision Model For Tests 1 -2 Items Long 62 } } } All scores fall within the uncertainty interval

Decision Model 5 Sequential Testing Criterion. Referenced Decision Model For Tests 4 Items Long 25% } } } Uncertainty interval Non-Mastery Classification 63 0%

Decision Model 6 Sequential Testing Criterion. Referenced Decision Model For Tests 33% 6 Items Long 64 0% }} } Uncertainty interval Non-Mastery Classification

Decision Model 7 Sequential Testing Criterion. Referenced Decision Model 40% For Tests 9 Items Long 65 0% }} 100% Mastery Classification Uncertainty interval Non-Mastery Classification

Decision Model 8 Sequential Testing Criterion. Referenced Decision Model 45% For Tests 15 Items Long 66 0% 100% 90% } Mastery Classification Uncertainty interval Non-Mastery Classification

Influencing Factors n Fixed n n n Variable n 67 Mastery Criterion Non-Mastery Criterion Allowable Probability of a Type I Error Allowable Probability of a Type II Error Test Length

For Pretests We want to be very sure the student is a master before letting instruction be skipped. n n Mastery Criterion Non-Mastery Criterion Type I Error Probability Type II Error Probability 90% 65% 0. 025 0. 058 Note the low probability of a Type I (false positive) error. 68

For Posttests The student has already gone through the material at least once, so the criteria can be loosened. n n Mastery Criterion Non-Mastery Criterion Type I Error Probability Type II Error Probability 85% 60% 0. 050 0. 104 Note that the probability of a Type I error is twice as high as before. 69

Pretest Decision Rules Number of Items Answered Correctly 70 Number of Items Presented

Posttest Decision Rules Number of Items Answered Correctly 71 Number of Items Presented

Decision Rules Compared Pretest 72 Posttest

Technology’s Role 1 73

Technology’s Role 2 74

Technology’s Role 3 75

Technology’s Role 4 76

Technology’s Role 5 77

Technology’s Role 6 78

The Best Teacher The best teacher uses books and appliances as well as his own insight, sympathy, and magnetism. – Edward L. Thorndike Education , 1912 79

Jesse M. Heines, Ed. D. Dept. of Computer Science Univ. of Massachusetts Lowell 80 heines@cs. uml. edu or heines@mit. edu http: //www. cs. uml. edu/~heines

Primary Reference 81

Primary References n n n 82 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. Mc. Graw-Hill, Inc. , New York, NY. Saettler, Paul, 1990. The Evolution of American Educational Technology. Libraries Unlimited, Englewood, CO.

Some Secondary References n n 83 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.