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Some Factors In The Secondary School Curriculum Which Affect Student Learning Efficiency

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Some Factors In The Secondary School Curriculum Which Affect Student Learning Efficiency

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Content This dissertation has been microfilmed exactly as received 69-19,406 STEAD, John Henry, 1931- SOME FACTORS IN THE SECONDARY SCHOOL CURRICULUM WHICH AFFECT STUDENT LEARNING EFFICIENCY. University of Southern California, EdJD., 1969 Education, theory and practice University Microfilms, Inc., Ann Arbor, Michigan Copyright by JOHN HENRY STEAD 1969 SOME FACTORS IN THE SECONDARY SCHOOL CURRICULUM WHICH AFFECT STUDENT LEARNING EFFICIENCY A Dissertation Presented to the Faculty of the School of Education The University of Southern California In Partial Fulfillment of the Requirements for the Degree DOCTOR OF EDUCATION by John Henry Stead June 1969 This dissertation, written under the direction of the Chairman of the candidate’s Guidance Committee and approved by all members of the Committee, has been presented to and accepted by the Faculty of the School of Education in partial fulfillment of the requirements for the degree of D octor of Education. Date. Guidance Commute irman fi/'J .!.'.* ,.:.. TABLE OF CONTENTS Page LIST OF T A B L E S .................................... iv Chapter I. THE PROBLEM................................ 1 Introduction Importance of the Study The Problem Scope of the Study Definition of Terms Chapter Summary II. REVIEW OF THE RELATED LITERATURE....... 19 Considerations for a Comprehensive Learning Theory Learning Efficiency Chapter Summary III. PROCEDURES EMPLOYED IN THE IDENTIFICATION OF FACTORS WHICH AFFECT LEARNING EFFICIENCY . 51 Pilot Study The 'Learning Efficiency Model Processing of the Data Chapter Summary IV. FINDINGS...................................... 79 Hypothesis Verification Computer Output Summary Tables The Relationships of Learning Efficiency Ratings to Test Results and Grade Point Average Data ii Chapter Page Validity of the Negative Relationships among LE and Test and Grade Point Average Data The Relationships of Courses to Learning Efficiency Ratings Sex Differences Time of Day Effect Differences between Students with High and Low Mean LE Ratings Elective Course Effect Replication Modification Results Chapter Summary V. SUMMARY, CONCLUSIONS AND RECOMMENDATIONS . . Summary of the Problem and Research Procedures Summary of the Findings Conclusions Implications of the Study Recommendations Chapter Summary 175 BIBLIOGRAPHY 196 LIST OF TABLES Table Page 1. Differences between "Academically Talented" and "College Prep" Student Scores on the California Test of Mental Maturity and Grade Point Averages............................. 55 2. Chi-square Contingency Tables Test of Independence of A.T. and C.P. Groupings Using Numbers of Years of Course Enrollments as a Basis.................................. 56 3. Chi-square Contingency Tables Test of Independence of Boys and Girls Using the Number of Years of Course Enrollments as a B a s i s ...................................... 57 4. Correlation Coefficient Matrix of Test Data and G.P.A. for "Academically Talented" and "College Prep" Student Scores.............. 59 5. Data U s e..................................... 68 6. Distribution of Classification of Courses Based on Mean LE, Class of 1966 ....... 82 7. Distribution of Classification of Courses Based on Mean LE, Class of 1967............ 83 8. Course Rank Order Listing by Mean LE, Class of 1966 ........................................ 84 9. Course Rank Order Listing by Mean LE, Class of 1967 ........................................ 89 10. LE Range Restriction......................... 97 iv Table Page 11. All Students, Student File Summary, Class of 1966 ................................... 100 12. All Students, Student File Summary, Class of 1967 .......................... 102 13. Boys Student File Summary, Class of 1966 . . . 103 14. Girls Student File Summary, Class of 1966 . . 104 15. Boys Student File Summary, Class of 1967 . . . 105 16. Girls Student File Summary, Class of 1967 . . 106 17. Student Groupings in Rank Order by Mean LE, GPA and CTMM, Class of 1966 108 18. Student Groupings in Rank Order by Mean LE, GPA and CTMM, Class of 1967 109 19. Top Quartile Division Summary, Student Files Based on LE, Class of 1966 ................. 110 20. Third Quartile Division Summary, Student Files Based on LE, Class of 1966 ................. Ill 21. Second Quartile Division Summary, Student Files Based on LE, Class of 1966 ............ 112 22. Bottom Quartile Division Summary, Student Files Based on LE, Class of 1966 ........... 113 23. Top Quartile Division Summary, Student Files Based on LE, Class of 1967 ................. 114 24. Third Quartile Division Summary, Student Files Based on LE, Class of 1967 ................. 115 25. Second Quartile Division Summary, Student Files Based on LE, Class of 1967 ............ 116 26. Bottom Quartile Division Summary, Student Files Based on LE, Class of 1967 ............ 117 v Table Page 27. Curricular Groupings in Rank Order by Mean LEf GPA and CTMM, Class of 1966 119 28. Curricular Groupings in Rank Order by Mean LE, GPA and CTMM, Class of 196 7 ............... 120 29. Mentally Gifted Student File Summary, Class of 1966 ......................................... 122 30. General Student File Summary, Class of 1966 . 123 31. Basic Student File Summary, Class of 1966 . . 124 32. Mentally Gifted Student File Summary, Class of 1967 .......................................... 126 33. General Student File Summary, Class of 1967 . 127 34. Basic Student File Summary, Class of 1967 . . 128 35. Honors English Student File Summary, Class of 1966 ..................................... 130 36. Honors English Student File Summary, Class of 1967 .......................................... 131 37. College Prep English Student File Summary, Class of 1966 132 38. College Prep English Student File Summary, Class of 1967 134 39. General English Student File Summary, Class of 1966 .......................................... 135 40. General English Student File Summary, Class of 1967 .............................. 136 41. Foreign Language Student File Summary, Class of 1966 .......................................... 137 42. Crafts Student File Summary, Class of 1966 . . 138 r vi Table Page 43. Metals Student File Summary, Class of 1966 . . 139 44. Woodshop Student File Summary, Class of 1966 . 141 45. Industrial Arts Student File Summary, Class of 1966 .......................................... 142 46. Industrial Arts Student File Summary, Class of 1967 ........................................ 143 47. Honors Mathematics Student File Summary, Class of 1966 144 48. Correlations between LE and G P A ............. 146 49. Curricular Areas, Correlations between LE and G P A .......................................... 148 50. All Students, Correlations between DAT Scores and L E ........................................ 149 51. Curricular Groupings— Correlations between LE and DAT, Class of 1966 ...................... 15 3 52. Curricular Groupings— Correlations between LE and DAT, Class of 1967 ...................... 154 53. Artificial Parameters for LE Rank Order Listings..................................... 15 9 54. Sex Differences— Critical Ratio Data Summary, Class of 1966 164 55. Sex Differences— Critical Ratio Data Summary, Class of 1967 165 56. Mean LE and GPA by Periods of D a y ........... 167 57. Differences between Student Files from the Upper and Lower Halves of the Interquartile Range Critical Ratio Data Summary, Class of 1966 .......................................... 169 vii Table Page 58. Differences between Student Files from the Upper and Lower Halves of the Interquartile Range Critical Ratio Data Summary, Class of 1967 ................... 170 viii CHAPTER I THE PROBLEM Introduction Every teacher faces the responsibility of deciding which materials should be presented for the student to learn and how these materials should be presented. This task is irrespective of both vertical and horizontal delineations within the framework of education. That is, educators at all levels from pre-school through graduate school and encompassing all disciplines, both formal and informal, evaluate the materials, techniques, procedures and systems of organization which they may use to facilitate student learning. On what are decisions regarding learning experiences being made? Teachers may make their decisions on an empir ical basis or they may seek the assistance of a supervisor or curriculum consultant. This merely moves the decision to another level of responsibility. The consultant may make recommendations which are founded on his own study and experience without regard to any theoretical rationale. This does not help the teacher to solve his own problems. It probably only increases his anxiety because he does not understand the basis on which the consultant made his decision. The adherence to empirical decision-making remains because at present there does not appear to be a functional theoretical basis from which to operate. This void has also resulted in an inability to evaluate non-experimental learning situations. The major difficulty stems from the inadequacy and infrequency of learning theories which have comprehensive implications. Learning theories which serve to explain the phenomenon of certain forms of learning are found to be inadequate when used as criteria for the development of satisfactory learn ing experiences. In this study the theory was posed that as students become more involved in a learning situation the efficiency with which they learn increases. Therefore, learning efficiency might vary in relation to differences in the curriculum. The problem was one of ascertaining the relative efficiency with which students learn in various subject areas in order to validate the above theory and also discover how learning efficiency might be improved. Importance of the Study Increasing the efficiency with which students learn is a concern of everyone in education today. Allen (1964) says that, "The greatest challenge of the teaching profes sion is to get students to learn as much as they can with a minimum of effort" (p. 2). There are financial as well as altruistic reasons for concern. Harley (1967) believes that cost per student figures should be based not on the number of students attending, but instead on a cost per student who has learned basis. He suggests that education costs can be reduced by increasing the output of functional students. Increasing the output of functional students requires improvement of the curriculum. Taba (1962) feels that sound curriculum development must be based on a sound psychology of learning. She points out that, "There is no coherent theory which encompasses consistently all aspects of learning" (pp. 76-78). Similarly, Krathwohl et al. (1956) stated that a theory which structured the components of learning into a single continuum could not be found. The lack of a comprehensive learning theory and the paucity of studies which have to do with conditions and management of learning may have caused Cronbach (1966) to state that . . . Today's proudest educational ideas, as practiced in the shiniest of suburban schools, seem to be little more effective than the stereotyped programs that the majority of American schools have been running off in their sleep for a generation or more. (Cronbach, 1966, p. 540) A deep understanding of both learning and motivation are necessary to instructional effectiveness. Similarly, Ammons (1964) quotes one administrator as saying, "We just went off into the blue," and adds that this "... may characterize the approach of many school systems" (p. 454). Travers (196 4) feels that a framework of theory is needed for the emerging concept of curriculum. This is similar to Goodlad's (1965) feeling that today's major problem is located in the domain of goals and processes of schooling. Bixler (1966) agrees. Bruner (1963) also points to the problem. Michael (1965) similarly makes a plea for a comprehensive theory of the teaching and learn ing process. Several writers have commented about the shortcom ings of present learning theory. Becker (1967) believes that the research dealing with learning theories and the behavior of the learner is quite primitive. Melton (1964) feels the present situation is a result of theorists work ing within the narrow confines of a learning category which is tied to a restricted range of variables. Allport (1962) explains that, "The trouble with our current theories of learning is not so much that they are wrong, but that they are partial. They fit best the learning of animals and young children" (p. 380). Bush (1963) cites an example of mathematical models of learning which are much more successful in analyzing data from rat experiments than human ones. Nelson (196 3) reports research suggesting that conclusions about human behavior which are based on the study of domesticated rats and mice have led to distorted concepts about human motiva tion and learning. The relationships of the role of the rat's pellet of food or sip of water to human learning are also questioned by Underwood (1964). Somewhat later, Krech (1968) states that "... the learning theories derived from the study of maze-running rats or target-pecking pigeons have failed to provide insights into the education of children" (p. 48). It is often said that reward systems facilitate learning. Goodlad (1966) suggests that the system of rewards and punishments is, ". . / extraneous— and probably deleterious— to learning, and is based on society's materi alistic conception of education" (p. 36). Bruner (1963) also feels that reward and punishment systems do not facilitate learning. Weitzman (1963) analyzes the current status of learning theory and concludes that continued treatment of learning in such a mechanical fashion can only be expected to require robot behavior of adult humans. Tiedman and Field (1962) make a similar observation in that overt con ditioning is incompatible with our expressed educational goals. The Problem The problem is essentially to discover how the over-all efficiency with which students learn can be increased. The study is based on the premise that differ ences in the curriculum are associated with variations in the efficiency with which students learn in various subject areas. As originally conceived, the problem was to evalu ate learning situations on the basis of student learning efficiency. What causes learning efficiency in the class room to increase or decrease? The learning theorists have not offered an operational theory on which to build an evaluative instrument; nevertheless, one was needed. Underwood (1964) fosters the thought that a compre hensive learning theory is^ possible. He suggests that a general system of human learning is unavoidable, as there is some continuity in the laws of behavior across all phila in the animal kingdom including Homo sapiens. Asimov (1967) similarly believes that the gulf between man and animals is a result of his having far more of what the lower animals have rather than something they do not have. Scientific evidence of chemical and structural changes in the brains of rats as a result of their living in an educationally enriched environment is reported by Krech (1968). Superior problem-solving animals were created. He concluded that; A lack of adequate educational fare for the young animal— no matter how large the food supply or how good the family— and a lack of adequate psychological enrichment results in palpable, measurable, deteriora tive changes in the brain's chemistry and anatomy. (Krech, 1968, p. 50) 8 It appears that Minor's (1964) idea of education being to help children, "... become more fully involved with exploring their world" (pp. 50-74), is of vital importance. Ideas such as these helped in developing a learning theory for use in this investigation. The theory is that learning accrues with the con cordant involvement of the individual. That is, as a student becomes more involved in a learning experience, the efficiency with which he learns increases. Verification of this theory requires an evaluation of learning experiences which make up the secondary curriculum and an understanding of these experiences to ascertain factors which have con tributed to the results. Traditionally, the evaluation of learning situa tions has included the notion that learning is inferred from measured performance after learning and the experi mental conditions which precede this performance. In most instances, the process of learning itself receives very little attention. Emphasis is placed on the conditions which are believed to be responsible for the learning with small consideration being given to the abilities and experiences which the learner brings to the situation. Performance and the situation which is deemed responsible for the learning generally receive the premium of atten tion. An evaluation of the learning process which is based on the above and the equipment which the learner brings to the learning situation in the form of past learnings (educational skill) and ability to learn, is not as a rule encountered. This oversight contributed to the obtaining of con fusing results in many studies related to learning. For instance, different men cannot be expected to achieve equal performance ratings when exposed to replicated learning experiences. Yet, generalizations about learning continue to be based on concepts gained through experiences (and experiments) which fail to consider learning as a process involving an organism equipped with his own unique equip ment (Bloom, 1963) for learning. This lack of consideration for the learning process as it occurs enhances the earlier cited lack of congruence. Variations in experimental results are not entirely the fault of the learning process. They may in part be due to differences in the learning equipment which the subject brings to the learning situation. Ratings of performance which are based on evidence gathered solely as a result of learning experiences may not 10 necessarily reflect a true indication of the effectiveness of a learning situation. In order to properly evaluate the effectiveness of a learning situation, both the results of the experience and what the subject brings to this situa tion, must be given consideration. This combination of results and the equipment which the subject brings to the learning situation may more properly be utilized to infer the effectiveness of the learning situation. The results of the learning situation (performance evidence) and evidence about the individual's equipment for learning (ability and educational skill) make up the con struct learning efficiency rating. The notion that some curricular areas will produce higher learning efficiency ratings than others is based on the earlier stated involve ment theory of learning. Purpose of the Study There are three interrelated purposes to this study: 1. Validate the proposed involvement theory of learning. 2. Identify the areas of the secondary school curricu lum which enhance learning in varying amounts. 11 3. Ascertain the relationships between student learn ing efficiency and the secondary school curriculum. Postulates 1. Student learning efficiency ratings can be devel oped utilizing the evidence of ability (CTMM scores), educational skill (STEP scores), and per formance as evidenced by semester grades. 2. Students who operate with optimum learning effi ciency can be identified as well as those who are learning in a relatively inefficient manner. 3. An analysis of learning efficiency ratings can assist in the identification of some areas of the curriculum which enhance learning more than others. Hypothesis Student learning efficiency ratings will vary from one course-offering to another in relation to differences in the extent to which students are involved in the curriculum. In other words, those learning situations which include the greater amounts of materials and tech niques for involving students will yield the higher learn ing efficiency ratings. Emphasis will be on individualized instruction. Conversely, those segments of the curriculum 12 which place relatively lower emphasis on the use of instructional materials and employ techniques which do not attend to the unique needs of each student will have rela tively lower learning efficiency ratings. Questions to Be Answered 1. What is the order of courses when they are ranked on a mean learning efficiency rating basis? 2. Does the rank order listing of courses support the hypothesis that learning efficiency would vary in relation to differences in the curriculum? 3. What is the correlation of student learning effi ciency ratings with the following? a. California Test of Mental Maturity. b. Sequential Tests of Educational Progress. c. Grade Point Average. d. Differential Aptitude Test. 4. In what ways are the courses which have high learn ing efficiency ratings different from those which have low learning efficiency ratings? 5. Is there any evidence of sex bias in test data, learning efficiency ratings or GPA's? 13 6. Does "time of day" have a relationship to learning efficiency ratings or GPA's? 7. In what ways are the data summaries of students with high learning efficiency ratings different from the data summaries of students with low learn ing efficiency ratings? 8. Does the fact that a course is an elective have a relationship to learning efficiency ratings or GPA1s? Basic Assumptions 1. The semester grade is a reliable and valid evalua tion of student' performance in the class for which the grade is given. 2. The California Test of Mental Maturity is a reli able and valid indicator of the student's learning ability. 3. The Sequential Tests of Educational Progress are reliable and valid indications of a student's educational skill and past educational experiences. 4. The parameters, age and exposure to past educa tional experiences, are established by including only twelfth grade students in the study. 14 Scope of the Study Delimitations This study surveyed all of the students in the senior classes of 1966 and 1967 at two comprehensive high schools within the same southern California school dis trict. Those students within this group whose records lacked the data necessary for computing learning efficiency ratings were deleted. The pilot study, which was used to demonstrate that there are significant relationships among student course patterns, sex, ability and grade point average, utilized all of the "college prep" students in the senior class of 1965 at the same two high schools. Students with incom plete records were not included. The grades and test data used in this study encom passed information from the time the students entered the ninth grade until they completed the twelfth grade or left the school district. Raw scores for test data were not available. CTMM scores were reported as IQ and all others as percentiles. Because summer school courses and driver training are organized on a different time basis, these grades and 15 courses were not included in the study. For the same reason, the educable mentally retarded students' classes were deleted. Limitations This investigation is limited to the students from two senior high schools within the same unified school district. Therefore, results may reflect the bias of that particular district. A second limitation is that this study can be only as valid and reliable as the grades and test data on which it is based. Definition of Terms Educational skill. A measure of the learner's level of competence over material and subject matter taught in school. It is a summary of previous educational achievement which provides evidence of past educational experiences. Involvement, level of. The degree to which the assets of the organism are structured for the learning purpose at hand; the correctness with which the intellectual, emotional, and physiological components are task oriented. 16 The posture is similar to concentration in that attention is given to the learning situation. Involvement may receive assistance through the participation of motivation— encouragement of the intention to learn. Learning. In its broadest sense, the process by which man is able to maintain contact and communicate with his total environment. It is the process by which he acquires knowledge and skills, modifies his behavior in light of experience, proposes and solves problems. It is manifested by performance. Learning efficiency. A measure of the effective ness of the experience during the process of learning as implied by performance after learning and the equipment which the learner brings to the situation in the form of ability and educational skill. For purposes of evaluation, measures of learning efficiency can be assigned to both the learner and the situation which was responsible for the learning. Secondary curriculum. All of the classes which students attended during grades 9 through 12. 17 Chapter Summary The need for a comprehensive learning theory is receiving increasing emphasis. The theory is needed to provide a basis for curriculum planning, research, and evaluation as well as to aid the classroom teacher in making judgments about teaching techniques and materials. Because of these needs, the following learning theory was formu lated: the more a student becomes involved in a learning experience, the more efficient will be his learning. This theory served as an empirical basis for the research problem. Specifically, the problem is to validate the pro posed involvement theory of learning, identify the areas of the curriculum which promote learning efficiency to the greatest extent, and ascertain the relationships between learning efficiency and the curriculum. The sine qua non of this study is an evaluation of the effectiveness of the various course offerings of the curriculum. Generally, when learning situations are evaluated, the evaluation has been based on student performance (as an indication of learning success) without giving 18 consideration to the individual's unique equipment for learning. More properly, consideration must be given to the individual's performance in light of his ability and past educational experiences. In order to accomplish this, a learning efficiency rating was developed. The learning efficiency rating served as a normal izing device for evaluating in-school learning situations and student performance reactions so that these could be investigated according to standardized criteria and the involvement theory could be related to these findings. CHAPTER II REVIEW OF THE RELATED LITERATURE This chapter contains a review of the literature which bears directly on the problem. Initial efforts were placed on the search for a learning theory with comprehen sive implications and/or clues which might lead to the development of a theory. As a result of this search, a theory was developed, the concept was formalized, and a formulation of the variables to be studied was crystallized. Attention was also given to the examination of pro cedures which might be used for attacking a problem of this nature— an evaluation of the normal in-school learning experience of students. The procedures were required to be objective in nature and also provide for generaliza- bility. Considerations for a Comprehensive Learning Theory There appears to be a general feeling that man has an innate need to learn. Bagley (1907) developed the idea 19 20 that man's instincts are satisfactory explanations for the phenomena attention; desire for changes and variety; play; curiosity; liking for bright colors, sharp contrasts, intense stimuli of all kinds; constructions; incentives; and the social instincts praise, commendation and adulation. He explained how to use these instincts to promote learning. In another instance (Bagley, 1911) he said that nature implanted in man the need to know, to seek out, to classify and arrange. Sears (1918) summarized the thinking of Rousseau. "Rousseau saw that learning was essential to living, and suggested . . . that the very same laws which underlie the processes of ordinary life activities are the fundamental laws of learning in school" (p. 4). On this basis, the concepts that there is a necessity for personal adjustment and that ideas and actions are essentially related were developed by Sears (1918). Contemporary thinking”~tends to support the notion that man innately needs to learn. Phenix (1964) states that the, "... fundamental human motivation is the search for meaning ..." (p. 344) . According to Hall (1959) ". . . man's tremendous brain has endowed him with a drive and a capacity for learning which appears to be as strong 21 as the drive for food or sex" (p. 39). Bruner (1966) believes that children possess, "intrinsic motives for learning" (pp. 114, 127). Gregory, in his book. Eye and Brain, The Psychology of Seeing, (1966) inadvertently demonstrated the above position with his explanation of how visual perception operates within the mind. He pointed out that perception is not determined by just what is seen: . . . rather it is a dynamic searching for the best interpretation of the available data. The data is sensory information, and also knowledge of the other characteristics of objects. . . . The visual system entertains alternative hypotheses and never settles for one solution. . . . Perceiving and thinking are not independent: "I see what you mean" is not a puerile pun, but indicates a connection which is very real. (Gregory, 1966, pp. 11-12) Gregory demonstrates his research summaries with several illusions as illustrations for the reader. The visual system as a part of its function requires that man think— that he learn. The hypothesis is offered that use of all of man's input devices (hearing, smell, touch, et cetera) may require judgment which is based on learning. Maw and Maw (1963) reported their findings that children with "high-curiosity" amassed a larger store of general information than a child of similar ability who is 22 less curious. Curiosity was one of the items identified as an instinct by Bagley (1907). Maw and Maw also concluded that a curious child by definition exhibits a desire to know more. Johnson (1966) explained that, "What motivates learning is not the.commonplace but the unexpected and unexplained, which pique the curiosity and imagination" (p. 123). In a similar vein Phenix (1964) commented that, "... the pursuit of the apparently impractical funda mental studies using ostensibly impractical imaginative materials proves in the long run to yield the richest harvest of practical fruits" (p. 350). Webber (1965) summarized six items about learning theory on which educational psychologists agree: 1. Motivation is essential. 2. Transfer of training is likely to occur if the experiences are meaningful in terms of goals of the learner. 3. Repetition or exercise or drill is not necessarily conducive to learning unless the learner sees that these activities are related to their goals. 4. Learning is usually related to goals or purposes of the learners. 23 5. Plans of action which seem to propel the learners toward their goals are more likely to be learned. 6. Learning is, in part, a process of,, discriminating one situation or plan of action from another in meaningful patterns which are related to the learners' goals. A special feature on learning contained in the March issue of the NEA Journal (1963) presented articles by G. Watson, B. Bettelheim, Jerome Bruner, Carl Rogers, and J. Richard Suchman. The common thread which appeared to run through their topics was that learning takes place when things happen, when students do things; i.e., experience things, discover, face problems, use the heuristic process. The above cited summaries appeared to aim at learn ing as a process in which the learners are involved. Melton, the editor of Categories of Human Learning (1964) concluded that an improved understanding of human learning is dependent upon increasing the study of learning proc esses. In the preface he states: . . . the largely artificial boundaries between the "kinds" . . . [and] "types" of learning . . . need to be forcibly bridged in the interests of promoting more comprehensive understanding of the interrelations of forms of human learning. (Melton, 1964, p. ix) 24 The Learning Process The fact that so many of our children dislike school or finish their schooling uneducated suggests that we still have much to learn about learning as a process. (Hall, 1959, p. 54) According to Thompson (1962), "Learning . . . does not occur unless the organism is active in a behavioral sense" (p. 101). Similarly, Piaget proposes that: Knowledge is not a copy of reality. To know an object, to know an event is not simply to look at it and make a mental copy or image of it. To know an object is to act on it. . . . Intelligence is born of action. (Jennings, 1967, pp. 81, 83) An illustration of this concept was reported in the October 22, 1965 issue of Time magazine. Music and dance were utilized to increase success in helping students learn to read. The school principal explained that, "There is a physical accompaniment to a mental process" (p. 64). Like wise, Reissman (1963) believes that the slow child may not understand an idea unless he does something with what he is trying to understand. Jackson (1965) feels that it is necessary for involvement to be maintained in the classroom; that is, for the students to remain engrossed in the learning activity. The literature suggested a variety of means for maintaining this involvement. 25 Games involve the students actively and focus attention according to Coleman (1967). He maintains that, . . persons do not learn by being taught; they learn by experiencing the consequences of their actions" (p. 70). Torrance (1965) presented the results of several investiga tions which led him to conclude that competition can be used to stimulate involvement. Anxiety seemed to facilitate learning intellectual skills according to the findings reported by Feldhusen et al. (1965) and Right and Sassenrath (1966). The utilization of students to tutor other students also appears to create learning environments in which students become involved. A report in Time for October 21, 1966 said that below average students who had been hired to teach younger students advanced an average of three and one half years in reading ability in a short time. Wagner (1966) discussed the characteristics of a challenging classroom environment. Some of his suggestions were: 1. The physical features of the classroom should be colorful, exciting, mobile and varied from time to time. 26 2. Innovation is a hallmark of the learning environ ment . 3. Through differentiation of instruction (especially through the use of independent-type activities) the experiences of all pupils are made more stimulating and fruitful. A change of environment in the form of both things and people profitably stimulates children (Wittlin, 1963). A supportive article appeared in Soviet Education for March, 1966. Leont've and Gal'perin suggested that the process of learning is based on action, "... external practical or internal intellectual. Along with this, the action should be strictly adequate to the knowledge being assimilated" (pp. 9-10). The importance of this posture for learning is stressed by McLuhan (1965). 1. The results of the famous Hawthorne experiment . . . came about because the workers were permitted to join their energies to a process of learning and discovery. 2. . . . the school drop-out situation will get very much worse because of the frustration of the stu dent need for participation in the learning process. 3. Environments are not passive wrappings but active processes. (McLuhan, 1965, p. vi) If learning is to meet with success, the process must involve the learner. This concept may help to explain 27 findings reported by Krathwohl (1964): One of the writers, (Bloom), has been attempting to do research on what might be called peak learning experi ences . . . the evidence so far suggests that a single hour of classroom activity under certain conditions may bring about a major reorganization in cognitive as well as affective behaviors. . . . It may also help us to recognize that not all hours of student-teacher- material interaction are of equal value. (Krathwohl, 1964, p. 88) Another insight was suggested by Maslow (1959). He hypothesized that a single powerful experience may have much more impact on the individual than many less powerful experiences. Lees (1963) lends credibility to Maslow's point with his comment that the knowledge which a mature language user brings to bear on the problem of expressing himself or understanding another person does not involve in a significant sense the frequency with which he has pre viously encountered any particular sounds, words or sentences. Similarly, Burton and Arnold (1963) concluded that frequent practice is not in itself a means of improv ing writing. It was pointed out earlier that educational psychologists are in agreement that repetition, exercise or drill is not necessarily conducive to learning. The func tion of repetition in learning was explored by Rock (1958). His research findings supported the contention that 28 repetition has nothing to do with the forming of associa tions, as these are formed instantly. Pressy's (196 3) report that reinforcement is not a significant process verifies Rock's findings. The findings from experiments involving the effects of repetition on programmed material are reported by Silberman (1962). Repetition failed to result in signifi cant differences in performance, tending to verify the suggestions of Bloom, Maslow, Lees, and Rock. He also reported that branching programs encouraged a deeper involvement in learning. Silberman discussed another experiment which indi cated no significant -difference between the results of a high anxiety provoking program and a program which was designed to be anxiety reducing. This is contrary to the findings of Kight and Sassenrath (1966) which were cited earlier. This apparent contradiction of facts illustrates a need identified by Melton (1964) : . . . An improved understanding of the entire range of human learning required a much greater emphasis on the determination of the effects of similar processes— and the variation in the characteristics of such similar processes— in otherwise different contexts of other variables. (Melton, 1964, p. 339) 29 Gange and Brown (1961-62) reported on different methods of teaching which they called "guidance-discovery," "discovery," and "rule-and-example." They found that the "guidance-discovery" method was most effective followed by the "discovery" and the "rule-and-example" least effective (pp. 313-21). A statement by the Educational Policies Commission (1964) helps to amplify the above findings. "There is general agreement that personal interchange between the student and a qualified instructor is an essential part of the learning process" (p. 13). However, the statement does not explain why the "guidance-discovery" method was most successful. Perhaps student involvement was greatest in the "guidance-discovery" method. The Involvement Theory Krathwohl (1956) comes rather close to explaining the involvement theory of learning in his use of the term internalization. He defines it as the process by which a phenomenon or value becomes a part of the individual; that is, the process by which inner growth takes place. At the lowest levels of the internalization continuum there is little emotion in the behavior. At this end the individual is mainly just perceiving the phenomenon. At middle levels, emotional response is a recognized and critical part of the behavior as the individual 30 actively responds. As the behavior becomes completely internalized and routine, this emotion decreases and is not a regular part of most responses. (Krathwohl, 1956, pp. 28-30) De Rose (1965) suggests that the contemporary science teacher ". . . must engineer activities in which students (and teachers) are intellectually and physically involved in learning" (p. 9). Buffie (1964) states that, "The importance of personal involvement in instructional activity can not be overestimated" (p. 63). A third source, Wills (1964) agrees. He feels that a lack of stu dent involvement is the reason educational television instruction has not met with greater success. The need for real involvement in order to promote change in attitudes (a result of learning) is evidenced by Williams (1965). As a result of his research, he concluded that experiences abroad do contribute to a teacher1s inter est in international understanding; whereas, taking pertinent academic courses seemed to have no particular influence on the teacher's interest in this area. Stated as a formalized theory: Learning accrues with the concordant involvement of the individual. Closely allied with this theory is the concept that student involvement is unlikely to occur without effort. 31 An awareness of this principle is necessary to understand ing the involvement mechanism. Thompson (1962) suggested that organisms follow a principle of "least effort" in most of their goal strivings, as evidenced by their preference for behavior patterns which require the least expenditure of energy. Perhaps this prompted Doll's (1964) comment on the selection of learning experiences. He recommended the choosing of experiences which prove satisfying to the learner. Wallach and Kagan (1965) concluded similarly that children should be encouraged to approach school assign ments in the spirit of associative play. The selection of satisfying experiences can be facilitated by helping students to select their own learn ing experiences. Dr. Ebert (1966), for example, encouraged his medical students to work out their own choice of lab demonstrations and lectures. Ubell (1963) reported that graduate students at Stanford have a place where without prior write up or explanations they " . . can go and perform any hare-brained psychological research project that strikes their fancy" (p. 20). Rogers (1967) tells about an experimental class for 35 eleven year olds in which the non-directive approach to teaching was used. The children studied what interested them. The class became a 32 permanent adventure in learning. These attitudes are in keeping with the findings reported by McConnell (1934) about children learning in their own natural ways outside of school as well as Torrance's (1965) suggestion that students need periods in which to select their own behavior patterns. Chase came very close to summarizing the involve ment theory when he stated that: Research has shown that learning is an individual matter. Consequently, if the child is an active learner, much of his work must be highly individual ized. . . . The instructional program therefore, should be organized and operated to encourage each pupil to make effective use of all of his senses and to utilize his total environment for his education. He will, thus, have to do more than the simple reading, writing, and arithmetic. Field trips, work activities such as constructing, dramatizing, analyzing, projecting, and the use of many media— plastics, metals, woodwork, etc.— all enable the individual to meet or more nearly meet his needs. (Chase, 1964, p. 48) It may be that Tyler (1966), in identifying a new task facing industrial arts educators, had insights to the problem at hand. The new task was ". . .to build a more adequate bridge between the world of sense experience and the effective use of thought and thus to understand and extend the direct experiences we have of the world through our senses" (p. 7). Minor (1964) stated that, "The teacher must provide opportunities for and encouragement of fuller 33 utilization of senses whether in kindergarten or the twelfth grade. Failure to do so deprives youngsters of indispensable data" (pp. 57-58). Hall, in an interview reported by Fenner explains it this way: We have built our educational system around people who get information through their ears and pay little attention to those who are primarily eye-minded or the ones who understand everything in terms of physical relationships, the kinesthetically oriented. . . . We are wasting the talents of men and women who are not ear-minded . . . (Fenner, 1967, p. 88) The curator of anthropology at the Brooklyn Children's Museum is reported as saying, "If they can't touch the things, . . . it might as well be a movie" (Lagemann, 1964, p. 128). If a movie is to be of maximum value however, past experiences must include learning with the aid of all of the senses. As Gregory pointed out: The seeing of objects involves many sources of informa tion beyond those meeting the eye when we look at an object. It generally involves knowledge of the object derived from previous experience, and this experience is not limited to vision but may include the other senses; touch, taste, smell, hearing and perhaps also temperature or pain. Objects are far more than patterns of stimulation: objects have pasts and futures; when we know its past or can guess its future, an object transcends experience and becomes an embodi ment of knowledge and expectation without which life of even the simplest kind is impossible. (Gregory, 1966, p. 8) 34 Involvement in learning requires making advanta geous use of all of the senses since no one is able to do precisely what the other does. Thus, to encourage involve ment and also achieve the assimilation of data in the most complete manner, as many input devices as practical must be utilized. In fact, McLuhan (196 5, pp. 42-47) described in detail the numbing effect which the stimulus of a single sense has on the central nervous system. Similarly, Tolhurst (1966) reported that, "When man is exposed to a steady-state sound he becomes less and less aware of his acoustic surroundings" (pp. 170-71). He also cautions that the absence of stimuli is poorly tolerated by man. Student Involvement The need for student involvement as a vital part of the learning situation is illustrated by the following examples. Bristow (1964) in reporting curriculum problems of students who leave school early, said, "Early leaving, like divorce, is the culmination of something which has its roots in conditions and actions experienced long before. The best hope for helping those drop-outs . . . lies in a good curriculum" (p. 145). A good curriculum probably is one with which the students become involved— one which 35 attracts them and at the same time appears to require a minimum of unnecessary effort, for as Krathwohl (1964) pointed out, "... only as one is willing to attend to a phenomenon will he learn about it" (p. 50). Ohlsen found that: The delinquent has rarely, if ever, become deeply involved in a meaningful experience at school. At least he has not become ego-involved and achieved success in those school activities which either the school or the society honors and rewards. (Ohlsen, 1 9 6 4 , p . 407) Johnson (1960) in his study of high school drop-outs found that two-thirds of the drop-outs which he interviewed had been school failures. Lichter (1962) reported that adequate success experience in school was a key factor in a student's continuation in school. The drop-out problem has only begun to develop according to McLuhan (1965). He says that, "The student can find no possible means of involvement for himself, nor can he discover how the educational scene relates to the 'mythic' world of electronically processed data and experi ences that he takes for granted" (p. vii). Learning experiences need to be designed to encourage the kind of involvement which promotes success, if students are to remain in school. 36 By utilizing the theory that learning accrues with the concordant involvement of the individual, it follows that efficiency in learning can be increased by increasing the student's level of involvement. Learning Efficiency In developing the problem around the learning efficiency concept, the guidance of several researchers was used. Cronbach (1966, p. 542) proposed that studies which conceptualize will provide "greater ultimate payoff" than studies which test new devices. Travers (1964) advised that a simple theory dealing with a few major variables can be much more productive than one dealing with a larger number of variables. The tendency in educational research according to Carter (19 61) has been to be concerned with one limited aspect of the total educational process at a time rather than the over-all system. McLean (1966) five years later observed that experimental design has unfortunately tended to focus on a single response in spite of the case that "... every experiment in education is a multivariate one . . . " (p. 494). 37 Underwood commented that a fresh approach is needed: . . . our standard situations from which we measure only a limited form of behavior, may be in part responsible for our inability to relate adequately the learning behavior in one situation to that in another. . . . If in the science of learning we have anything approaching "breakthroughs" it may be because these occur when the investigator is able to throw off the shackels imposed by standardized procedures. (Under wood, 1964, p. 76) Carpenter and Hadden (1964) suggested exploring the problem as conceived in terms of efficiency as this allows the methods of educational research to take on the charac teristics of engineering. Utilizing the observations of these researchers in conjunction with the involvement theory of learning, it was conceived that it should be possible to identify: 1. The areas of the curriculum in which learning occurs with high and low efficiency. 2. Students who learn with high efficiency. 3. Students who learn with low efficiency. Many discrepancies in research findings may be resolvable if the concept of learning efficiency is applied to the results. For example: Carter (1949) investigated the significance of psychological factors in success and failure of high school students. He found that there was 38 not a significant difference between passing and failing * groups. Walter (1962) on the other hand reported in his findings that success or lack of success is not a problem which necessarily has its origin within the present educa tional framework. He concluded that school success is related to the basic personality characteristics of the individual. The question of whether or not the student was actively involved in the learning situation was not a part of the research. Lumsdaine (196 3) reviewed research find ings which provided troublesome problems of interpretation. He pointed out that sometimes overt response procedures were more effective than covert responses and other times covert responding was more effective. When learning time was controlled, various results were encountered. Ambi guity of results was also reported in the use of feedback and reinforcement. Sex differences in achievement have often been investigated. Roberts (1960) concluded that girls secure higher grade point averages and that there are more high achieving girls than boys. This may be the result of a greater degree of involvement in learning on the part of the girls, rather than the fact that they are girls. Schmadel (1960) found that sex membership did not 39 contribute to success in achievement except in reading comprehension. In another study Frymier and Thompson (1965) found that girls consistently seemed to be more highly motivated to learn in school than boys during the junior high school years. Perhaps being more highly motivated to learn really means that the learning environ ment caters to girls more than it does to boys. Waetjen (1965) stated that, "The sex of the learner is a determinant of his learning" (p. 22). He suggests that this is because of the marked superiority which girls have over boys in the language area and since school is essentially a verbal, symbolic, linguistic experience girls do better. Torrance (1965) said, "It seems rather clear from a number of indications that teachers can improve the educa tion of boys by providing more creative ways of learning . . . " (pp. 256-57). As the result of a study about students1 motiva tion, Frymier (1964) stated that, "... the most effective teacher will be that one who is most able to 'fit' his instructional techniques to each child's unique needs" (p. 242). Involvement may be dependent on a recognizing of the unique needs of the individual. 40 The most significant concept in modern education, according to McKenney (1967), is how the teaching environ ment makes it possible for the child to learn at his own rate and as an individual. Learning Efficiency and the Curriculum The literature provided several clues to the development of a procedure for identifying varying amounts of learning efficiency. Carrol (1961) suggested a direct attack on the crucial variables of learning rates and attitudes. At another time (1963) he discussed programmed instruction and student efficiency. "... The evidence thus far accumu lated suggests that individual differences in performance during learning remain large" (p. 7). He suggested study ing differences in relation to predictors such as IQ, aptitude test scores, and school achievement. Closely allied is this comment by Bloom (1963). He asked: In what ways do the patterns of abilities that students bring to the learning situation affect the nature of the teaching process? Do some learning experiences make more effective use of these abilities than others do? (Bloom, 1963, p. 385) It was suggested earlier that perhaps school is geared to certain abilities more than others. The design 41 of the curriculum may be more advantageous to some. Lumsdaine (1963) suggested viewing the instructional factors as being "... instrumental in the control of student response, whether explicit or implicit" (p. 628). These factors may control the response, but the nature of student performance is also influenced by the background of the learner, as pointed out by Carrol and Bloom. Melton (1964) added that, ". . . it is unlikely that one can make an appropriate prediction or assessment of human perform ance in any task situation without considering the historical antecedents of that performance capability . . . " (p. 327). A similar approach was suggested by Silberman (1961), "Perhaps what is needed most to improve research on program variables is a standard method of expressing learn ing efficiency in terms of both test performance and cost factors [learning time]" (p. 7). The literature supported the notion that learning efficiency can be used as a basis for evaluating the curriculum. Efficiency has been defined as a function of learner time by Wallen and Travers (1963), and Myers and Travers (1966). This is not entirely correct as a learning efficiency rating is influenced by the instructional 42 factors (of which time is only one) and the performance capability of the student, which is composed of ability and the past learnings that a student brings to the learning situation. Learning Efficiency— the Dependent Variables Cook and Hovet (195 6) suggested that dependent variables be classified as student behavior. Wallen and Travers (1963) proposed that the dependent variable be the response variable. Learning Efficiency is implied by -Uj performance and the equipment which the learner brings with him to the learning situation in the form of ability and educational skill. Weitzman (1963) suggested that the dependent variable must be a statistic (a mean, a variance, a corre lation or a proportion). Learning efficiency is based on the results of student behavior on achievement tests (evidence of past experience), intelligence tests (evidence of ability) and grades (an evaluation of student response to the learning situation). It is expressed as a statistic. Secondary Curriculum— the Independent Variables These are the variables, related to the learning process, that the teacher may be able to manipulate. By doing so, the teacher may be able to exercise some control over the efficiency with which the learning process takes place. (Wallen and Travers, 196 3, p. 487) Thus, those parts of the secondary curriculum over which the teacher has control are identified as the inde pendent variables. This organization is in complete agree ment with the suggestion of Cook and Hovet (1956) that, "The primary task of curriculum research is to define independent variables (conditions) and the dependent variables (behavior) and to relate these functionally to each other" (p. 233) . It was hypothesized by Silberman (1961) that a "high payoff" would be obtained if as much effort were devoted to the dependent variables as is spent on the independent variables. His explanation is that, "The nature of the criterion variable determines the outcome of the experiment no less than the treatment conditions themselves" (pp. 7-8). The entanglements of the variables were pointed out by MacDonald and Raths: 44 . . . Educators rarely have definitive evidence to suggest which variables of concern are really inde pendent and which are dependent. Certainly, specific variables may play either role in various research designs in terms of the labels assigned by researchers. However, the act of calling variables independent certainly does not assure that they are truly inde pendent. (MacDonald and Raths, 1963, p. 326) Achievement Quotient The achievement quotient, sometimes referred to as the accomplishment quotient (AQ), was used early in this century to evaluate achievement in comparison with ability. The measure was defined as the ratio between the actual level of scholastic performance (evidenced by achievement test results) and the expected performance level (IQ score) (Pintner and Marshall, 1921; Ruch, 1923; Greene, 1954). Coy (1930) reviewed the use of the AQ in measuring teaching efficiency. He concluded that because of the large number of factors which influence the AQ, this was not practical. One major problem with the AQ was its negative correlation with IQ. Another was that AQs of more than 100 were achieved although in theory an AQ of more than 100 is impossible. This was due to the unreliability of the measures and spurious correlations according to Chapman (1923) , Popenoe (1927), Nyggard (1928), and Wilson (1928). 45 Horn (1937) believed that the AQ was abandoned because of the existence of difficulties which were not understood as well as the above cited lack of reliability. She developed the "Law of the Uneven Distribution of the Effects of Errors in Measurement." . . . In a distribution of test scores, a larger pro portion. of positive chance errors in testing will be found in the upper brackets of scores, and a larger portion of negative chance errors in testing in the lower brackets. This is equivalent to stating that individuals who have received a relatively high score on a test more often than not (but not always) have been those who have had their ability overestimated by the results. Similarly, low scores would represent more cases of underestimation. (Horn, 1937, p. 24) She suggested that this was the main reason for dull pupils being found to have achieved relatively more than expected (1937) . Since the AQ tended to be unreliable, the easiest solution was to not use it. Instead, educators chose to use phrases such as underachievement, overachievement, accelerated or retarded to describe the relationships of students' ability and achievement test data. This tech nique avoided the difficulties which are encountered in the comparison of achievement and ability. 46 Learning Efficiency Rating The learning efficiency rating is used in this study as an indication of the effectiveness of learning experiences. It is calculated from performance, ability and achievement test data. The resulting rating yields ordered results which are extremely desirable for research according to Page and Marcotte (1966) . The California Test of Mental Maturity results were used to gauge learning rate or ability. According to Bloom (1963), intelligence tests can be used for this pur pose. Conversely, Sorenson (1963) utilized the amount of time needed for learning programmed materials as a measure of intelligence. Further support for the use of intelli gence scores as an indication of student ability is con tained in Millman and Glock's (1965) conclusion that the educational experience of the young child plays a key role in the development of his intelligence— intelligence reflects past experience. Achievement test data supplies an indication of past educational experience and is highly correlated with intelligence test results. According to Bloom (1963) this correlation is the result of a general factor under lying performance on both types of tests. Garrett (1958) 47 suggests that this is a spurious correlation— a result of the correlation between chronological age and mental age or achievement. He also states however that, "Both reading and arithmetic enter with heavy, but unknown, weight into most general intelligence tests . . ." (p. 422). Hostrop (1966) feels that, "... achievement tests can only have valid meanings . . . if the results are used in conjunction with intelligence test results" (p. 557). The availability of multiple intelligence and achievement test data presented another area of concern. Ruth Dugan (1962) provides evidence which suggests that the emotional condition of the child rather than general intelligence or environmental factors ". . .is the signif icant factor in determining the performance of a child on an achievement test" (p. 551). Similarly, Brown (1964, p. 14) found that "restless squirming behavior" may be related to high humidity and low barometric pressure. He suggests that climatic variables be considered when gather ing behavioral data. Thus, an individual's low test scores may not be a true indication of his capability. Pullias (1965) expresses the thought that, "... the potential of man is judged most meaningfully by what he #has thought and done at his best. . . . These high-water 48 marks . . . give the true estimate of the nature of man; they suggest what he can be" (p. 14). It was decided to utilize the highest test scores which were available between grades 9 and 12. According to the California Test Bureau (1957), the IQ is reasonably stable and can be used to predict potential at any time up to and including adulthood. Bloom (1963) reports that group intelligence test results become relatively constant (correlations rarely less than .80) by about the second or third year of school. He further reports that achievement scores rarely have correlations of less than .80 after grades 5 or 6. Chapter Summary The review of the literature was divided into two categories: (1) consideration for a comprehensive learning theory, and (2) implications for the study of learning efficiency. There appears to be an innate need for man to learn. Man's existence is predicated upon this need. The educa tional problem is one of implementation, i.e., capitalizing on this need to learn. There are several items about learning theory on which educational psychologists agree 49 including the notion that a comprehensive learning theory is needed. One approach is to study learning as a process. What then is the nature of this process? For the most part, it appears to be one of action which occurs when the individual becomes involved. The learner may refuse to become involved in a learning experience or his level of involvement may become so high that he loses contact with all else. The spectrum is so broad that the efficiency with which learning occurs can vary a great deal. Those situations which enhance the involvement of the learner, tend to be associated with high learning efficiency. The literature supported this theory that learning accrues with the concordant involvement of the individual. In order to validate the theory that learning effi ciency is related to student involvement, a fresh approach is needed— one which considers more nearly the total educational process and yet deals with few major variables. The literature suggested that the variables to be studied would include learning situations, individual differences in performance, ability and achievement. The dependent variables would be statistical evidence of 50 student behavior and the independent variables— learning situations. Early in this century, the achievement quotient was used to evaluate learning. It utilized ability and achievement scores combined into a ratio. This practice fell into disfavor because of reputed unreliability. The learning efficiency rating developed for this study combines the suggestions of several authors. It serves as an indicator of the effectiveness of a learning experience. It is a statistical measure of student per formance expressed in light of ability data and past educational experiences as evidenced by achievement scores. This chapter reviewed literature bearing on the problem outlined in Chapter I. The next chapter explains the procedures which were employed in solving this problem. CHAPTER III PROCEDURES EMPLOYED IN THE IDENTIFICATION OF FACTORS WHICH AFFECT LEARNING EFFICIENCY The problem as outlined in Chapter I has three parts: (1) to develop student learning efficiency ratings, (2) to ascertain the relationships between these ratings and the secondary school curriculum, and (3) to validate the involvement theory of learning. A learning theory was formulated to serve as a basis for development of postulates and the hypothesis to be tested in this study. In essence, the problem was based upon a student's ability as measured by the California Test of Mental Maturity, his past educational experiences (educational skill) as measured by the Sequential Tests of Educational Progress, and his performance as indicated by semester grades. This chapter describes in detail the research design and procedures utilized in the study. Relevant data was collected and analyzed over a three year period in 51 0 52 order to carefully crystallize and refine the research design. Initial concern centered around the use of the above mentioned data to formulate learning efficiency ratings. Was the nature of the data such that it might yield a learning efficiency rating? Would the intercorre- lations of the data be such that the learning efficiency rating might be inferred from a segment of the data or would each of the scores make a unique contribution? These questions were in the main answered by the pilot study. Pilot Study In the spring of 1965, a study was completed using data supplied about all 354 students who were enrolled in the twelfth grade college preparatory and "Honors" English classes at two comprehensive high schools within the same school district. The college preparatory English selection criterion was utilized in order to introduce a measure of homogeneity and restricted grouping which might sharpen the results. Only twelfth grade students were used in the study so that age and educational experience could be held relatively constant. 53 The study was undertaken in order to ascertain whether there were any consistently significant relation ships among student course patterns, grades, sex, ability, and educational skill (achievement) test data. Procedure The following types of data were key-punched into cards for analysis: 1. Senior high school attended. 2. Sex. 3. Test data. a. Highest recorded California Test of Mental Maturity (IQ) score. b. Sequential Tests of Educational Progress achievement scores in reading, writing, and mathematics. 4. Grade point average at the end of grade eleven. 5. Number of years of courses taken in the following subject areas: a. English. b. Mathematics. c. Science (all). d. Spanish. e. French. 54 f. Business. g- Industrial Arts and Home Economics. h. Art. i. Life Science. j- Physical Science. k. Lab Science. The students were divided into several groups for analysis. The first grouping was accomplished by separat ing the "Academically Talented"(AT) group, defined by Conant (1959) as those students who score in the top 15 per cent of the population on intelligence and achievement tests, from the others (hereafter referred to as the CP group). A second grouping was based on the schools attended and a third grouping was based on sex. The t-test was used to ascertain that the AT and CP groups were different on the basis of IQ scores and GPA as indicated in Table 1. Chi-square contingency tables were set up to ascertain the independence of the groupings based on the number of years of courses taken. See Tables 2 and 3. A correlation coefficient matrix was developed to identify the relationships of test scores with each other TABLE 1 DIFFERENCES BETWEEN "ACADEMICALLY TALENTED" AND "COLLEGE PREP" STUDENT SCORES ON THE CALIFORNIA TEST OF MENTAL MATURITY AND GRADE POINT AVERAGES Academically Talented College Prep. t-test Results Group N CTMM GPA N CTMM GPA CTMM GPA M S.D. M S;.d . M S.D. M S.D. 1. All 54 127.61 8.07 3.25 .52 300 114.82 8.50 2.81 .47 10.255 6.229 2. All at School 2 22 131.27 8.36 3.38 .47 154 115.31 8.23 2.82 .47 8.732 5.228 3. All at School 3 32 124.78 6.49 3.17 .54 146 114.30 8.75 2.80 .46 6.395 3.990 4. Boys at both schools 21 129.95 8.07 3.19 .49 147 115.28 8.56 2.67 .43 7.396 5.093 5. Girls at both schools 33 126.12 7.70 3.30 .54 153 114.37 8.42 2.95 .46 7.376 3.840 Note: Data from the Class of 1965 at Senior High Schools 2 and 3. Total Class Size = 942. Number enrolled in College Preparation English = 384. Number on which there was complete data = 354. t is significant in all cases at the .01 level. in in 56 TABLE 2 CHI-SQUARE CONTINGENCY TABLES TEST OF INDEPENDENCE OF A.T. AND C.P. GROUPINGS USING NUMBERS OF YEARS OF COURSE ENROLLMENTS AS A BASIS Subject Area Chi Square Row Number of Years of Subject Area Completed between Grades 9-12 {Cell Entry Indicates Number of Students) 0 1 2 3 4 5 6 7 1 28 12 8 6 English 3.380 2 140 92 40 17 1 18 13 23 Math* 14.019 2 153 86 58 1 10 22 15 6 Science 3.023 2 87 119 62 29 1 12 3 14 12 13 Spanish* 22.851 2 85 51 110 29 25 1 38 5 8 French 3.102 2 241 17 27 1 10 29 8 Business .415 2 46 149 51 Ind. Arts & 1 30 16 8 Home Econ. 5.303 2 114 68 78 1 31 16 7 Art .568 2 159 90 27 Life 1 16 25 13 Science 1.965 2 68 186 62 Physical 1 9 32 12 Science 6.384 2 89 172 36 Lab 1 20 31 3 Science 8.749 2 163 119 5 Note: Data is from the Class of 1965— same students as Table 1. Row 1 = Number of A.T. students. Row 2 = Number of C.P. Students. *P < .01. 57 TABLE 3 CHI-SQUARE CONTINGENCY TABLES TEST OF INDEPENDENCE OF BOYS AND GIRLS USING THE NUMBER OF YEARS OF COURSE ENROLLMENTS AS A BASIS Subject Area Chi- Square Row Number of Years of Subject Area Completed between Grades 9-12 (Cell Entry Indicates Number of Students) 0 1 2 3 4 5 6 7 1 108 48 19 7 English 13.759 2 78 67 30 16 1 6 50 67 58 Math 44.402 2 13 113 46 24 1 25 72 51 33 Science 43.037 2 75 82 32 9 1 66 30 72 12 5 Spanish 36.689 2 44 28 60 32 35 1 45 113 22 5 Business 33.524 2 19 82 39 26 Ind. Arts & 1 50 38 27 31 Home Econ. 30.962 2 111 53 24 9 Note: Data is from the Class of 1965. Row 1 = Boys. Row 2 = Girls, p <.01 in all cases. 58 and GPA for the total group and each of the subgroups. These results are indicated on Table 4. Results 1. A significant difference was noted between the CP and AT groups on the basis of IQ, GPA, and the numbers of years of mathematics and Spanish courses which were taken. 2. There was a lack of significant correlation between grade point average and IQ scores for all groups. 3. Grade point avenge correlated with mathematics achievement for boys in the AT group at the .05 level and for the CP boys' group at the .01 level. 4. GPA correlated with writing achievement scores for all CP students at the .01 level, but for AT students this relationship held only for boys. 5. The reading achievement scores generally corre lated at the .01 level with the IQ scores of the CP groups. 6. Mathematics achievement scores correlated with the IQ scores of the AT groups at the .05 level for all subgroups except boys where there was a lack of significant correlation. The mathematics achievement scores of the CP groups correlated with their IQ scores at the .01 level for TABLE 4 CORRELATION COEFFICIENT MATRIX OF TEST DATA AND G.P.A. FOR "ACADEMICALLY TALENTED" AND "COLLEGE PREP" STUDENT SCORES Grouping Grade Point Average Math S.T.E.P. Writing Reading A.T. C.P. A.T. C.P. A.T. C.P. A.T. C.P. 1. All .036 .053 .347 *.283 .007 *.227 .067 *.226 2. All at school 3 -.180 .155 .373 *.307 -.349 *.270 -.110 .214 ii 3. All at school 2 .107 -.049 .333 *.260 .325 *.222 -.022 *.255 u 4. Boys at both schools .164 .061 .063 *.348 -.016 *.271 -.024 *.232 5. Girls at both schools -.003 .082 .431 .215 -.005 *.353 .167 *.270 1. All .090 *.257 -.154 *.203 .228 *.516 •H 2. All at school 3 .167 *.280 -.076 .193 .176 *.413 'C 3. All at school 2 -.321 *.247 -.409 *.212 .234 *.568 nJ (U 4. Boys at both schools .379 .201 .084 *.380 *.567 *.491 PS 5. Girls at both schools -.063 .184 -.218 *.262 .102 *.412 tp 1. All .343 *.440 -.072 .105 a ■H 2. All at school 3 .369 *.416 -.199 .163 •P 3. All at school 2 .224 *.475 .078 .062 • r l u 4. Boys at both schools *.648 *.332 .235 *.416 IS 5. Girls at both schools -.285 *.250 .216 *.404 1. All .154 .063 rs 2, All at school 3 .137 .157 - p 3. All at school 2 .157 -.030 U J s 4. Boys at both schools .524 *.252 5. Girls at both schools .035 .113 Note: *Signifies .01 significance of correlation data from the Class of 1965 at Senior High Schools 2 and 3. See Table 1 for the number, mean, and standard deviation of each group. 60 all subgroups. Mathematics generally correlated with read ing at the .01 level for the CP group with a lack of significant correlation for the AT group. 7. A significant difference was noted (P smaller than .01) in the numbers of years of English, mathematics, science, Spanish, business, and industrial arts and home economics elected by boys and girls. Conclusions After manipulating the data, it appeared that unidentified factors were affecting the relationships of the variables. No single measure could be relied upon to serve as an accurate indicator for all groups. This is in keeping with the conclusion reported by Chase (1964) that statistical controls on mental age alone cannot be relied upon to hold constant the ability to learn and the sugges tion of Cramer and Bock (196 6) that often there is no objective basis for giving priority to certain variables. It also demonstrated that there are difficulties in the comparison of ability and achievement as pointed out in reviews of the achievement quotient. It was concluded that a unique bit of data is supplied by each of the measures and that a composite 61 indicator such as a learning efficiency rating might be developed along the lines outlined in the first two chapters. The Learning Efficiency Model The theory on which this study is based states that as a student becomes more involved in a learning experi ence, the efficiency with which he learns increases. Per formance during the learning experience is dependent not only upon the student's involvement, but also his ability and skills gained as the result of past educational experi ences which he brings with him to the learning situation. Therefore, a learning efficiency model was postulated to demonstrate these relationships. Use of the learning efficiency model provided a normalizing influence on the data so that the learning experiences themselves could be evaluated. Rationale Student A has average ability and educational skill as evidenced by his test data. His performance as demon strated by his participation in a particular learning experience was evaluated as being average. As a result, 62 his learning efficiency rating would be average. Had student A received an above-average performance rating, it is theorized that his involvement increased and as a result, he learned with greater efficiency (assuming that the time allotted in which to learn was held constant). Conversely, if he received a below-average learning efficiency rating, this would be reflected in a below-average learning effi ciency rating. Student B with above-average ability and/or educa tional skill would need to receive an above-average performance rating if his learning efficiency rating were to be average. Student C with below-average ability and/or educa tional skill and an average performance rating would have an above-average learning efficiency rating. The model illustrates these relationships. Model Learning Efficiency = Performance2 Ability x Educational Skill For purposes of this investigation, the model was modified in the following manner to accommodate the data. 63 LE = Learning Efficiency P = Performance (Grade) LE A = Ability R + W + M 3 • J R = Reading Skill W = Writing Skill M = Mathematical Skill Standardizing of Data For ease of interpretation, it was decided that the average efficiency rating should be equal to 1.000. To accomplish this objective and also enable the model to function as designed, the measures were transmuted into standard scores. The T-score with a mean of 50, a standard deviation of 10, and a range of from 0 to 100 was selected because of its convenient relative scaling and the fact that the data would also be normalized. This served to discourage the possibility of skewed distributions which might otherwise be encountered. Grade each class of this particular school district between the ninth and twelfth grade levels was utilized as the perform ance rating. The data was converted from letter grades to The semester grade which the student achieved in 64 the numerical equivalents A=4, B=3, C~2, D=1, F=0 and then transmuted into T-scores for use in the formula. Grades of "incomplete" and "no grade given" were not given consideration. Those grades for courses which were completed outside of the school district were also excluded from the study. Ability The student ability data was the highest California Test of Mental Maturity (CTMM) total IQ score which the student achieved between grades seven and twelve. The scores were converted into per cents of N and then T-scores were assigned. Educational Skill This data supplied evidence of past educational experience. It was obtained from the Reading, Writing, and Mathematics sections of the Sequential Tests of Educational Progress (STEP). Data was selected from the results of tests which were given between grades nine and twelve. The highest Reading, Writing, and Mathematics percentile scores were selected and then arbitrarily averaged (after being converted to T-scores) in order to obtain a single score which would share equally its influence with the CTMM ability score. Processing of the Data Organization of the Data The following data was placed on tape for process ing on the Honeywell 400. The data was collated into individual student files for all members of the graduating classes of 1966 and 1967 (except for those students identi fied as educable mentally retarded as they are enrolled in special classes). 1. Student identification number. 2. Classification of students. a. Mentally Gifted. b. General. c. Basic. 3. Sex. 4. Junior high school attended. 5. All CTMM scores. a. Language. b. Non-language. c. Total. 6. All STEP Achievement scores. a. Reading. b. Writing. c. Mathematics. 7. Differential Aptitude Test scores. a. Verbal Reasoning. b. Numerical Ability. c. Abstract Reasoning. d. Space Relations. e. Mechanical Reasoning. f. Clerical Speed and Accuracy. g. Spelling. h. Sentences. 8. Grade and class data (For each course in which the student was enrolled from grades nine through twelve). a. Class identification number. b. Semester grade. 9. Senior high school attended. The following information was obtained through processing of the above data. It was then added to each student file. 1. Grade point average. 2. Grade point average of elective courses. 3. Learning Efficiency Rating for each course. 4. Total mean Learning Efficiency Rating. 5. Class rank based on mean Learning Efficiency Rating. 6. Mean Learning Efficiency Rating for elective courses. Treatment of the Data All of the student files were inspected for com pleteness. Table 5 is a distribution of data use. Class Data All of the learning efficiency ratings were listed for each course. The sample size, mean learning efficiency rating, standard deviation, range and rank based on the mean learning efficiency rating were reported for each course. Organization of the Data Summaries 1. Sample size. 2. Mean, standard deviation, maximum and minimum scores and range for the following: 68 TABLE 5 DATA USE Files Class (N= of 1966 1,042) Class (N= of 1967 1,073) N % N % LE used for Class and Curriculum groupings 849 81.5 525 48.9 LE, STEP and DAT Summaries Correlation Tables 602 57.8 411 38.3 LE and STEP Summaries and Correlation Tables3 247 23.7 114 10.6 Unusable*3 193 18.5 548 51.1 aLE and STEP correlation tables and data summaries were used to validate (by inspection) the LE and STEP results of the LE, STEP, and DAT summaries and correlation tables. All files in the Class of 1966 were used except those which contained incomplete test data. For the Class of 1967, the school district was in the process of assign ing new student alpha numbers. As a result, much of the test data could not be retrieved. a. Language CTMM score. b. Non-language CTMM score. c. Total CTMM score. d. STEP Reading score. e. STEP Writing score. f. STEP Mathematics score. g. GPA Total. h. GPA Elective Courses. i. Mean Learning Efficiency Rating. j. Mean Learning Efficiency Rating for Elective Courses. k. DAT Scores. 3. Correlation coefficient matrix utilizing items a through k above. Grouping Criteria for Data Summaries (Computer Output) 1. All students (students for which complete data is available). a. All. b. Boys. c. Girls. Ability (same as above but grouped on ability). a. Mentally Gifted Minors (Classified as such by California Education Code standards). (1) All. (2) Boys. (3) Girls. b. General Students (those who do not fit into the preceding or following categories). (1) All. (2) Boys. (3) Girls. c. Basic Students (enrolled in one or more courses for "slower" students). (1) All. (2) Boys. (3) Girls. School Attended. a. Students from junior high school A. (1) All. (2) Boys. (3) Girls. b. Students from junior high school B. (2) Boys. (3) Girls. c. Students from junior high school C. (1) All. (2) Boys. (3) Girls. d. Students graduating from senior high school 2. (1) All. (2) Boys. (3) Girls. e. Students graduating from senior high school 3. (1) All. (2) Boys. (3) Girls. Curricular Groupings (in most instances student files were required to contain a minimum of four semester grades in the particular curricular area to qualify for inclusion in that group. In the Business category six semester grades were required because of the great numbers of students who enroll in typing for two or more semesters and are not pursuing a Business course. In Industrial Arts, two or more semesters of Drafting are often 72 completed by students who will not ordinarily enroll in other industrial arts courses. Therefore, to purify the grouping, a six semester prerequisite was required.) a. Agriculture (four or more semester grades required for qualification of a student file for this group). b. Art (four or more semester grades required). c. Athletics (four or more semester grades). d. Automotive (four or more semester grades were required). e. Business (six or more semester grades were required). (1) All students. (2) Boys. (3) Girls. f. Crafts (Pine Arts Department) (four or more semester grades were required). (1) All students. (2) Boys. (3) Girls. English— College preparation (four or more semester grades were required). (1) All students. (2) Boys. (3) Girls. English— General (four or more semester grades were required). (1) All students. (2) Boys. (3) Girls. English— Honors (four or more semester grades were required). (1) All students. (2) Boys. (3) Girls. Foreign Language (four or more semester grades were required). Homemaking (four or more semester grades were required). \ Industrial Arts (six or more semester grades were required). 74 m. Leadership (two or more semesters of this class are required of each student involved in campus government. Therefore, two or more semester grades were required.) n. Mathematics (four or more semester grades were required). (1) All students. (2) Boys. (3) Girls. o. Mathematics— Honors (four or more semester grades were required). (1) All students. (2) Boys. (3) Girls. p. Mechanical Drawing (four or more semester grades were required). (1) All students. (2) Boys. (3) Girls. q. Metals (four or more semester grades were required to qualify). r. Music (four or more semester grades were required). 75 (1) All students. (2) Boys. (3) Girls. s. Photography (four or more semester grades were required). (1) All students. (2) Boys. (3) Girls. t. Science (four or more grades required). (1) All students. (2) Boys. (3) Girls. u. Wood (four or more grades required). 5. Learning Efficiency Quartile Divisions Student files were listed in rank order by mean learning efficiency rating for the identification of Q^, Q2r and Q3. The files were then separated into quartile divisions. Time of Day Analysis This consisted of summarizing the grade point average and learning efficiency data on a period by period basis. The grades and LE ratings were separately 76 accumulated for each period of the day. Data reported on a period by period basis included the number, mean, maxi mum, minimum, range, and standard deviation. A list of individual learning efficiency ratings was made for each period of the day. This list was then summarized in the same format as for the GPA summary. Replication Modification After processing the Class of 1966 data and inspecting the results, it was decided that two slight pro cedural modifications might be of benefit. The computer program had been written to utilize all available data concerning the Class of 1966. This included those students who did not have sufficient test data for the generation of learning efficiency ratings. As a result, this data was added whenever an efficiency rating was not required. The computer program was altered so that the replication on the Class of 1967 used only those student files with enough data to compute LE ratings. The second modification concerned the use of the grade "F." Its reflection of performance during learning was questioned as "F" may indicate failure to complete certain minimum requirements or poor attendance. Also, the 77 lower end of the "F" range may extend far beyond that of the "A" through "D" measures. Chapter Summary The research procedures which were used to support the involvement theory of learning centered around the use of a learning efficiency model. Prior to using this model, a pilot study was completed to ascertain the importance of utilizing the proposed test and grade data. It verified that a unique bit of data was contributed by each informa tional category. The model illustrated the functional relationship of learning efficiency ratings to educational skill and intelligence test data and grades. It was then used in the formulation of learning efficiency ratings for individual students and the courses which they completed. The computer generated learning efficiency ratings which were placed in rank order by student and course. To facilitate analysis, measures of central ten dency and correspondence were reported on computer output tables for the dependent variable learning efficiency and the test and grade data from which the dependent variable was derived. DAT scores were also included to provide an 78 enlarged description of each grouping. The data was grouped for analysis in several differ ent ways. Criteria included schools attended, sex, ability, learning efficiency ratings, and groupings by subject or curricular areas. Grade point averages and learning efficiency ratings were also calculated for each period of the day. Replication of the above was accomplished utilizing data from the Class of 1967. The first chapter analyzed the problem. Chapter II reported on the literature which was directly related to the problem and its solution. This chapter described the research procedures which were developed to obtain the findings reported in the next chapter. CHAPTER IV FINDINGS The interrelated purposes of this study were to ascertain the relationships of the secondary school curric ulum to student learning efficiency and validate the involvement theory of learning. The theory stated that as a student becomes more involved in a learning experience, the efficiency with which he learns increases. The review of the literature contained in Chapter II helped place this theory in perspective and suggested ideas for the development of the learning efficiency model which was explained in Chapter III. Chapter III also outlined the procedures which were utilized to obtain the results which are discussed herein. The findings were obtained by evaluating the effi ciency with which learning takes place in the secondary school. The process involved the use of Learning Efficiency ratings {LE ratings) based on the premise that a student's performance during learning is dependent on what he brings 79 80 with him in the form of ability and educational skills and his level of involvement in the learning situation. It was hypothesized that LE would vary from one course offering to another in relation to the differing uses of materials, equipment and techniques which are designed to enhance learning. An LE rating was developed for each student in every course. LE ratings along with the grade and test data were then grouped by school, sex, student ability, LE rating and subject area for the production of data summa ries and correlation tables. The LE ratings were also grouped by course so that a mean LE rating could be gener ated for each. The findings reported in this chapter parallel the format of the problem delineation in Chapter I. Data which was needed for hypothesis verification is presented first, followed by detailed supportive information. Following this, each of the questions are answered. The procedures used are reviewed first; then the findings are presented. Reference is made to many of the tables on several occa sions so they will not always be found on the page imme diately following their introduction in the text. 81 Hypothesis Verification In order to test the hypothesis reviewed above, the LE ratings were filed by course title irrespective of school or teacher. It was hoped that in this manner those influences might be randomized. From this information a LE rating for every course was generated in the computer and compiled {by class year) into two lists ordered by rank mean LE. Tables 6 and 7 summarize these two lists. Meaning was brought to the lists by imposing refer ence points at (estimated) +2 SD, +1 SD, mean, -1 SD and -2 SD (See Tables 8 and 9). This was accomplished by accumulating LE scores and converting them to percentages of the total N on the basis that LE would be normally distributed. This appeared to be a reasonable assumption as the Class of 1966's calculated mean LE for all students was 1.020 (Table 11, p. 100) and the mean for the course in which the estimated mean was found (P.E.— Girls', Grade 12) was 1.026. For the Class of 1967, the calculated mean LE was 1.206 (Table 12, p. 102) and the mean of the course which contained the estimated mean (English 9— General) was 1.174. Tables 6 and 7 show what percentage of the courses within each division belonged to that particular 82 TABLE 6 DISTRIBUTION OF CLASSIFICATION OF COURSES BASED ON MEAN LE, CLASS OF 1966 Distribution Classification -------------------------------- +2 +1 M -1 -2 1. Individualized Instruction 71.4 60.0 25.5 30.0 10.0 2. Office Service and Assistant 28.6 12.2 4.3 4.5 3. Independent Study 21.4 9.7 2.1 4. Basic and Remedial Reading 14.3 4.8 5. Activity (Excluding #2 and #3) 14.3 53.6 51.0 36.3 4.7 40.0 6. Music 10.7 17.1 2.1 7. Industrial Arts 10.7 9.7 19.1 11.3 8. Metals 3.5 4.8 2.1 9. Wood 3.5 2.4 6.3 10. Mathematics 3.5 2.4 4.2 4.5 28.5 20.0 11. Art 7.3 12. Crafts 7.3 13. Athletics 2.4 14. Homemaking 9.7 8.5 15. Agriculture 4.8 2.1 16. Automotive 2.4 4.2 17. Photography 2.4 2.1 18. Business 19.1 11.3 4.7 19. English— General 3.7 4.5 20. Science 2.1 15.9 4.7 40.0 21. English— College Prep 6.8 22. Leadership 2.2 23. English— Honors 2,2 9.5 24. College Prep 34.0 95.2 60.0 25. Honors _ 9.0 23.8 40.0 26. Foreign Language 6 . 8 33.3 40.0 27. Math— Honors 14.2 Note: The above are percentages which reflect the number of courses per classification in each part of the dis tribution from Table 8. For example: 71.4 per cent of the courses which were found more than two standard deviations above the mean emphasized indi vidualized instruction. 83 TABLE 7 DISTRIBUTION OF CLASSIFICATION OF COURSES BASED ON MEAN LE, CLASS OF 1967 Classification Distribution +2 +1 M -1 2 1. Activity (Excluding #5 and #6 below) 64.2 39.6 66.6 26.6 21.7 14.2 2. Basic and Remedial Reading 21.4 . 7.5 7.1 3. Individualized Instruction (more than 50% of students) 14.2 39.6 35.7 13.3 13.0 4. Industrial Arts 7.1 20.7 16.6 13.3 8.6 5. Office Service and Assistant 7.1 20.7 4.4 6. Independent Study 7.1 9.4 2.3 4.4 7. Homemaking 7.1 7.5 7.1 2.2 8. Agriculture 7.1 1.9 2.2 9. Athletics 1.9 10. Wood 3.7 4.7 11. Art 3.7 4.7 12. Metals 5.6 2.2 13. Music 5.6 9.5 2.2 14. Business 1.9 16.6 13.3 15. Science 1.9 4.7 4.4 26.0 14.2 16. Mathematics 3.7 2.3 4.4 8.6 28.5 17. Mechanical Drawing 4.7 4.4 18. Automotive 2.3 2.2 19. English— General 6.6 20. Leadership 2.2 21. English— College Prep 2.2 8.6 22. College Prep 13.3 86.9 71.4 23. Honors 2.2 30.4 28.5 24. Foreign Language 2.2 26.0 42.8 25. English— Honors 13.4 26 . Math— Honors 8.6 14.2 Note: The above are percentages which reflect the number of courses per classification in each part of the dis tribution from Table 9. 84 TABLE 8 COURSE RANK ORDER LISTING BY MEAN LE, CLASS OF 1966 Course Title Mean SD N Nurse Assistant (A) 2.670 1.557 5 English Review 2.590 .000 2 *Cafeteria Assistant 1.950 .030 2 **Independent Study— Industrial Arts 1.873 .650 11 Reading, Remedial— Grade 9 1.817 .266 7 Independent Study— Homemaking 1.785 .592 4 Independent Study— Drama 1.743 .511 8 *Office Service— Main Office 1.712 .479 14 ♦Electric Shop 1.676 .644 33 General Math— Grade 9 1.663 .643 52 *Office Service— Guidance Center 1.658 .578 18 Reading, Developmental— Grade 9 1.649 .531 43 ♦♦Construction Technology (2 hrs.) 1.621 .671 48 ♦Reading 1, Developmental 1.601 .477 78 Music Theory 1.595 .478 15 Metals 3 1.581 .515 8 U.S. History— Basic 1.579 .574 35 English 11— Basic 1.545 .570 246 ♦♦English 10— Basic 1.540 .521 91 ♦Office Service— Dean of Girls 1.535 .065 4 Office Assistant— Guidance Center 1.522 .094 4 Concert Glee 1.515 .372 24 Office Service— English Dept. 1.505 .025 2 Library Assistant 1.504 .690 28 ♦Independent Study— P.E. 1.495 .025 2 ♦Independent Study— Photography 1.490 .308 13 ♦Independent Study— Fine Arts 1.473 .611 64 Beginning Instruments 1.472 .370 9 ♦Office Service— Attendance Office 1.471 .440 45 ♦Girls' Glee 1.449 .549 191 ♦Nurse Assistant (B) 1.445 .498 27 ♦Industrial Arts Assistant 1.440 .504 48 Music Appreciation 1.433 .557 102 Reading Skills 2 1.428 .515 19 ♦World History— Basic 1.427 .570 139 ♦Band 1.403 .521 111 ♦Metals Technology (2 hrs.) 1.401 .428 40 ♦Office Service— ASB Office 1.393 .239 6 TABLE 8— Continued 85 Course Title Mean SD N *General Math 1.386 .478 375 *Crafts 1 1.385 .637 194 *Agriculture— Introduction 1.368 .422 62 ♦Ornamental Horticulture 1.365 .414 51 ♦Work Experience— General 1.364 .723 10 Band— Grade 9 1.351 .518 103 Clothing— Grade 9 1.345 .389 56 ♦Independent Study--Industrial Arts 1.345 .567 105 ♦Crafts 2 1.331 .450 175 Journalism 2 1.318 .379 28 Independent Study— Art 1.303 .524 29 ♦Art 3 1.302 .452 34 Art 2 1.298 .494 292 Band— B, Grade 9 1.295 .416 12 ♦Choir 1.287 .512 209 Wood 3 1.287 .548 55 Independent Study— Crafts P.E.— Boys1 Grade 9 1.277 .423 25 1.276 .592 527 ♦Metals 1 1.273 .574 131 ♦Clothing 2 1.265 .557 86 Photography 2 1.261 .692 63 ♦Athletics 1.260 .505 876 Art 1 1.240 .448 254 ♦Band 1.239 .434 185, 36 ♦Drama 2 1.239 .671 Auto 2 1.235 .145 2 ♦Independent Study— Science 1.230 .306 58 ♦Radio Workshop 1 1.205 .539 45 Homemaking— Grade 9 1.203 .496 191 ♦Office Service— Social Studies 1.203 .280 11 Independent Study— Foreign Language 1.203 .168 3 .......................... +1 SD . . P.E.— Boys' Restricted (B) 1.200 .451 224 Clothing 1 1.191 .457 261 Journalism 1 1.191 .439 86 Agricultural Science 1.184 .413 34 Radio Workshop 2 1.177 .399 10 Drama 3 1.171 .482 8 P.E.— Boys' Grade 11 1.170 .518 526 Foods 1.170 .408 206 Data Processing 1.162 .512 89 Orchestra 1.160 .459 154 Bookkeeping 1 1.151 .429 263 Introduction to Industrial Educ. 1.149 .409 121 Speech— Grade 9 1.148 .449 86 P.E.— Boys' Grade 10 1.147 .472 488 Home Management 1.146 .476 168 86 TABLE 8— Continued Course Title Mean SD N Modern Science 1.145 .479 735 Wood 1 1.145 .444 123 General Math— Grade 9 1.144 .374 484 P.E.— Girls' Grade 10 1.140 .415 750 Buying and Selling 1.133 .421 52 Introduction to Business 1.132 .377 452 Advertising 1.125 .436 30 P.E.— Girls' Grade 11 1.118 .414 806 Wood 2 1.118 .471 205 P.E.— Boys' Grade 12 1.116 .485 732 Home Nursing and Child Development 1.1X1 .432 157 Senior Math 1.110 .482 66 Office Assistant— P.E. 1.104 .432 7 P.E.— Girls' Grade 12 (A) 1.103 .389 256 Automotive Technology (2 hrs.) 1.097 .422 202 English 10— General 1.095 .392 807 Secretarial Practice 1.090 .193 22 Woodshop— Grade 9 1.087 .422 126 Independent Study— English 1.085 .176 10 World History— General 1.077 .395 930 Clerical Practice 1.069 .404 206 English 11— General 1.062 .406 868 Auto 1 1.062 .390 366 Mechanical Drawing 3 1.061 .387 109 Mechanical Drawing 4 1.058 .419 56 Typing 2 1.046 .358 472 Salesmanship 1.045 .425 28 P.E.— Girls' Grade 9 1.043 .412 500 Photography 1 1.039 .425 305 Metal 2 1.034 .392 286 Office Service— Business 1.030 .020 2 P.E.— Girls' Grade 12 (B) 1.026 .395 542 P.E.— Boys' Restricted (A) 1.023 .150 3 Typing 1— Grade 9 1.005 . 354 928 English 9— Honors 1.003 .260 73 Typing 1— Grades 10-12 1.002 .421 232 Mechanical Drawing 2 .999 .381 309 Psychology--General .998 .314 663 Spanish 1 .988 .406 329 P.E.— Boys' Restricted Grade 9 .985 .309 18 American Citizenship— General .985 .365 721 English 9— General .985 .378 674 Business Law .984 .381 26 Speech 1 .975 .307 190 U.S. History— General .968 .337 1066 Mechanical Drawing 1 .962 .359 287 87 TABLE 8— Continued Course Title Mean SD N Humanities .958 .314 229 Business Economics .956 .412 23 English 12— General .956 .368 633 Biology .954 .331 635 Speech 2 .944 .314 15 Shorthand 2 .941 .206 49 Electronics 1— (A) .940 .333 23 English 10— College Prep. .939 . 319 560 Office Service— English .930 . 000 1 American Government— General .930 .328 669 Economics .928 .305 20 Business English .925 .291 226 Leadership .920 .307 78 Electronics 1 (B) .912 .325 77 Drama 1 .909 .501 173 Office Service— P.E. .906 .130 6 Physical Science .902 . 361 92 English 11— College Prep. .899 .272 1085 Electronics 2 (A) . 898 .231 26 Electronics 2 <B) .896 .245 10 Physiology .892 .328 593 Chemistry— CBA .873 .246 42 Advanced Math— Statistics .872 .355 26 English 11— Honors . 870 .203 145 Algebra 1— Grade 9 .866 .236 157 French 1 .862 .255 139 Spanish 1— Grade 9 .852 .371 467 World History— Advanced .846 .253 817 Biology— BSCS . 846 .225 59 English 12— College Prep. .845 .304 593 Spanish 2 .843 .309 552 Algebra 1 . 842 . 369 375 Shorthand 1 .842 .270 147 American Government— Advanced .831 .246 157 *French 4 .831 .168 24 *U.S. History— Advanced .815 .242 537 *Geometry .811 .270 826 *Algebra 2— Honors .810 .166 63 ♦Spanish 3 .808 .315 304 International Affairs .808 .243 29 ♦Latin 2 .802 .354 37 ♦English 10— Honors .801 .216 102 ♦French 2 .796 .292 147 ♦French 3 .795 .239 60 ♦Psychology— Advanced .793 .215 141 ♦Trig-Solid Geometry .780 .287 865 88 TABLE 8— Continued Course Title Mean SD N ♦Spanish 4 .779 .205 100 ♦Geometry— Honors .774 .174 92 Chemistry .761 .267 463 ♦Analytics .761 .198 78 ♦English 12— Honors -2 SD . , .744 .245 140 ♦♦Latin 1 .731 .287 58 ♦♦Algebra 2 .717 .297 395 ♦♦Chemi stry--CHEM .681 .220 32 *Physics— PSSC .677 .221 201 French— Introduction to .670 .178 66 *Course appeared on the 19 66 and 1967 lists more than 1 SD away from the mean. **Course appeared on both lists more than 2 SD away from the mean. (A) or (B) indicates that the course code number was changed resulting in two separate listings for the course. Note; Total N for LE ratings was 3 8,397. Mean estimated at 19,198 (median) cumulative N +2 SD estimated at 876 cumulative N +1 SD estimated at 6,094 cumulative N -1 SD estimated at 32,302 cumulative N -2 SD estimated at 37,521 cumulative N LE was assumed to be normally distributed in the above table. Thus it was possible to obtain crude estimates of the standard deviations by accumulating Ns and marking the points at 2.28, 15.87, 50.00, 84.12 and 97.71 per cent of the total. 89 TABLE 9 COURSE RANK ORDER LISTING BY MEAN LE, CLASS OF 1967 Course Title Mean SD N **Independent Study— Industrial Arts (A) 3.201 .000 1 *World History--Basic 3.020 2.001 12 Mixed Chorus • 3.019 1.807 13 Girls' Chorus 2.783 2.754 16 English 10— Basic 2.441 1.622 63 Radio Workshop 2 2.420 .000 2 *Drama 2 2.406 1.666 6 English 9— Basic 2.286 1.822 76 *Crafts 2 2.235 1.245 8 *Work Experience— General 2.233 .890 44 *Office Service— Attendance Office 2.232 1.872 44 **Construction Technology (2 hrs.) 2.227 .799 24 *Clothing 2 2.196 1.790 23 *Agriculture— Introduction 2.074 1.441 12 *Ornamental Horticulture 2.066 1.215 23 Work Experience— Vocational 2.037 .904 4 independent Study— Fine Arts 1.985 .858 36 *Choir 1.980 1.857 70 Math 9 1.977 1.175 50 Art 9 1.971 .973 46 ♦Office Service— Social Studies 1.950 .000 2 Drama 3 1.920 .462 5 Clothing 9 1.889 1.303 43 Clothing 1 (A) 1.876 .633 3 Crafts 1 1.872 .786 38 *Band— A 1.863 1.620 62 Reading Skills— Grade 9 1.860 .342 5 Office Service— Fine Arts 1.846 .551 6 P.E. Assistant 1.823 . 633 6 *Work Experience— Cafeteria 1.810 .000 1 *Office Service— Industrial Arts 1.807 .641 9 American Citizenship— Basic 1.803 1.268 65 Senior Math 1.801 1.119 37 independent Study— P.E. 1.783 .636 3 *Reading 1— Developmental 1.765 .825 70 Reading 9— Remedial 1.760 .441 25 Office Service— Business 1.740 .000 2 *Office Service— ASB Office 1.720 .000 1 *Nurse Assistant (B) 1.704 .423 21 Speech 2 1.700 .000 2 Data Processing— Introduction to 1.657 .442 14 Home Management 1.647 1.098 54 *Radio Workshop 1 1.637 .625 14 TABLE 9— Continued 90 Course Title Mean SD N ♦Independent Study— Industrial Arts (B) 1.631 .790 24 ♦Office Service— Guidance Center 1.626 .672 13 ♦independent Study— Science 1.617 .898 22 Office Service— P.E. 1.615 .419 7 Crafts 9 1.604 .649 103 Introduction to Industrial Education 1.598 .531 16 ♦Electric Shop 1.596 .740 23 ♦Office Service— Dean of Girls 1.590 .339 3 Foods 1.587 1.258 65 Metals Technology (2 hrs.) 1.568 .686 27 ♦Athletics 1.503 .675 322 Metal 2 1.501 .548 44 ♦General Math 1.493 .670 153 ♦Girls' Glee 1.486 .801 30 ♦Band 1.485 1.087 111 ♦Work Experience— Main Office 1.482 .456 7 Modern Science 1.475 .965 185 Wood 2 1.459 .893 36 Data Processing 1.456 .355 40 ♦Independent Study— Photography 1.456 .480 15 ♦Art 3 1.452 .579 8 Electric Shop— Grade 9 1.451 .580 20 Office Service— Vocational-Fine Arts 1.450 .000 1 ♦Metal 1 1.443 .673 55 U.S. History— Basic 1.438 .374 8 Wood 1 (A) 1.436 .532 59 Band— B 1.430 .260 2 Speech and Drama 1.428 .455 39 Bookkeeping 2 1.419 .391 48 Home Nursing and Child Development 1.415 .877 53 Advertising P.E.— Boys' Grade 9 1.412 .647 20 1.412 .648 321 English 11— Basic 1.405 .470 6 Art 2 1.389 .780 32 Metals— Grade 9 1.387 .488 45 P.E.— Girls' Grade 12 1.382 .955 210 P.E.— Boys' Grade 11 1.361 .755 19 Wood 3 1.356 .454 5 P.E.— Girls' Grade 9 1.339 .872 285 P.E.— Boys' Grade 12 1.334 .733 247 Orchestra 1.330 .526 97 Pre-Algebra 1.327 .754 217 Mechanical Drawing 3 1.325 .405 25 Journalism 2 1.322 .296 14 P.E.— Girls' Grade 10 1.309 .895 407 TABLE 9— Continued 91 Course Title Mean SD N P.E.— Girls' Grade 12 1.304 .833 205 Homemaking 9 1.295 .715 104 P.E.— Girls' Grade 11 1.282 .524 40 Clothing 1 (B) 1.281 .491 76 Wood 1 (B) 1.276 .450 59 Art 1 1.266 .771 108 Reading Skills 2 1.258 .502 33 Introduction to Business 1.256 .442 176 Clerical Practice 1.254 .566 94 Bookkeeping 1 1.246 .368 44 P.E.— Boys' Grade 10 1.239 .651 269 P.E.— Boys' Restricted Grade 9 1.229 .772 56 Auto 1 1.224 .511 76 Mechanical Drawing 4 1.204 .466 9 World History— General 1.203 .672 481 Biology 1.203 .564 126 Photography 1 1.193 .490 79 Orchestra— Advanced 1.193 .441 52 Independent Study--English 1.188 .206 9 Typing 1 1.179 .513 470 English 9— General 1.174 .438 294 Agricultural Science (A) 1.170 .340 5 Driver Education 1.169 . 292 21 Introduction to Journalism 1.163 . 356 20 American Citizenship— General 1.156 .475 306 Metals 3 1.150 .000 1 Automotive Technology 1.138 .449 92 Office Service— Social Studies 1.136 .221 3 Psychology— General (A) 1.128 .497 329 Shorthand 2 1.127 .298 29 Leadership 1.114 .462 26 Library Assistant 1.114 . 309 7 English 12— General 1.111 .485 286 Psychology— General (B) 1.110 .306 59 Salesmanship 1.107 .392 17 Business English 1.106 .455 106 Photography 2 1.094 .412 24 English 10— General 1.083 . 374 331 English 11— General 1.077 .441 41 Journalism 1 1.067 .380 27 Mechanical Drawing 1 1.065 .321 123 Physical Science 1.063 .399 23 Drama 1 1. 058 .480 56 American Government— General 1.056 .675 336 Advanced Math— Statistics 1.052 .272 11 92 TABLE 9— Continued Course Title Mean SD N Typing 2 1.051 .521 188 Humanities 1.027 .313 125 Shorthand 1 1.025 .324 31 Electronics 1 (B) 1.021 .408 12 Chemistry 1.017 .303 31 Agricultural Science (B) 1.015 .350 65 English 9— Advanced 1.011 .284 283 U.S. History— General 1.011 .331 63 Mechanical Drawing 2 1.001 .322 46 Speech 1 .987 . 325 57 Algebra 1 .974 .345 599 Physiology .968 .307 96 Business Economics .967 .332 12 American Citizenship— Advanced .965 .257 230 Business Law .961 .358 12 Independent Study— Foreign Language .943 .139 8 Independent Study— Math .933 .153 3 Spanish 1 .932 .327 258 American Government— Advanced .929 .299 80 English 9— Honors .927 .223 18 English 10— College Prep .924 .297 347 English 12— College Prep .913 .306 323 *Geometry— Honors .894 .216 91 Woodshop— Grade 9 .892 .340 66 *Analytics and Calculus . 890 .205 54 Electronics 2 .890 .230 11 ♦Psychology— Advanced .885 .269 84 Electronics 1 (A) .883 .237 18 World History— Advanced .881 .259 339 English 11— College Prep . 873 .380 30 ♦U.S. History— Advanced .872 .238 32 ♦Spanish 2 .866 .324 263 ♦Spanish 3 .865 .277 87 ♦English 10— Honors .852 .166 85 Biology— BSCS . 830 .257 101 ♦French 2 .823 .260 48 ♦English 12— Honors .822 .190 70 ♦French 3 .817 .203 11 French 1 .808 .255 96 ♦Trig-Solid Geometry .806 .318 65 ♦Physics PSSC .804 .218 101 ♦French 4 .795 .186 12 Chemistry— CBA .795 .125 4 English 11— Honors .795 .125 2 -2 SD TABLE 9— Continued 93 Course Title Mean SD N , -2SD . . ♦Geometry .778 .254 363 ♦♦Algebra 2 .773 .295 44 *Latin 2 .770 .140 2 **Latin 1 .765 .256 8 ♦Spanish 4 .754 .241 37 ♦♦Chemistry--CHEM .630 .159 25 ♦Algebra 2--Honors .565 .105 2 *Course appears on the 1966 and 1967 lists more than 1 SD away from the mean. ♦♦Course appears on both lists more than 2 SD away from the mean. (A) or (B) indicates that the course code number was changed resulting in two separate listings for the course. Note: LE Rating N's totaled 14,754. Mean estimated at 7,377 cumulative N. +2 SD estimated at 337 cumulative N. +1 SD estimated at 2,342 cumulative N. -1 SD estimated at 14,412 cumulative N. -2 SD estimated at 14,417 cumulative N. LE was assumed to be normally distributed in the above table. Thus it was possible to obtain crude estimates of the standard deviations by accumulating Ns and marking the points at 2.28, 15.87, 50.00, 84.12 and 97.71 per cent of the total. 94 classification. Because of the nature of the classifica tions, some of the courses were included in more than one category— making the percentages within a division total more than 100 per cent. From the examination of Tables 6 and 7, it was found that LE ratings vary in relation to differences in the curriculum. The hypothesis contended that these dif ferences would exist and that those areas of the curriculum which included the greatest amount of materials, equipment, and techniques for involving students would yield the highest LE ratings. The preponderance of activity-type courses (courses which utilize specialized materials, equipment, and instructional techniques) in the upper parts of the distri bution, support this contention. By subject area this included Industrial Arts, Homemaking, Agriculture, Music, Art, Athletics and Mathematics (non-college prep), as hav ing courses appearing on both lists located more than 1 SD above the mean. In contrast, those categories which were found only below the mean on both lists were leadership, college prep, and honors. A rather important technique for involving students is the individualizing of instruction. This was also 95 elucidated in the hypothesis. The courses which employed this technique {with more than half of the students for the majority of the instructional time) had their major impact on both tables more than 1 SD above the mean. Other evi dence of individualized instruction was found in the independent study and office service classifications. These listings were also found to be clustered significantly above the mean on both tables. The last part of the hypothesis stated that those courses which do not attend to the unique needs of each student will have relatively lower LE ratings. These are the courses in which an arbitrary amount of material must be taught before the student can advance to the next higher level of instruction. Primary emphasis is usually on covering the material. Traditional college prep courses fit this classification. For both years of the study, these were located at the bottom of the lists. None of the courses were above the mean. They made up the majority of the courses below -1 SD and all of the courses beyond -2 SD. Validation of the Usefulness of the LE Index as a Reflector of Curricular Influences It was hypothesized that if the LE's were randomly distributed, the results would be the same regardless of the grouping criteria. Range Restriction Tables 8 and 9 indicated the results of grouping LE's by course title and placing them in rank order. Tables 27 and 28 (pages 119 and 120) show the results of grouping student files by curricular area of specialization. These tables included all LE's in the student file. The LE distributions were compressed when the curricular influences were diluted by the inclusion of LE's from other courses. Table 10 indicates these changes. Rank Order Relocation When the LE rank ordering of curricular areas as shown on Tables 27 and 28 was compared with the course listings of Tables 6 through 9, certain disparities were noted indicating a curricular influence. 1. Industrial Arts curricular groupings were located near the mean (as expected since these groupings consisted of student files which appeared to approximate 97 TABLE 10 LE RANGE RESTRICTION 1966 1967 Min. Mean Max. Min. Mean Max. Course Listings .67 1.02 2.67 .56 1.17 3.20 (Tables 8 and 9) Curricular Groupings .75 .96* 1.35 .83 1.20* 2.62 (Tables 27 and 28) *Mean of the grouping in which the estimated mean of the total distribution was found. 98 the "All" groupings). Industrial Arts course listings were found predominantly above the mean. 2. English-General curricular groupings were found 1 SD above the mean when the mean was based on all courses in the grouping. When group mean LE's were com puted using English— General courses only, they were found less than 1 SD above and below the mean. 3. The LE's for the Athletics groupings were at the mean when all courses were considered. When Athletics courses only were used to calculate the group mean LE's, they were found more than 1 SD above the mean. Computer Output Summary Tables The questions posed in Chapter I were designed to facilitate an understanding of the LE mechanism and serve as further testing of the involvement theory and hypothesis. The evidence for answering these questions consisted of 284 computer output data summary tables. It was found that the "back-up tables" with incomplete DAT information and the tables grouped by "school attended" only served to verify the representative findings reported herein. The tables which follow are organized with both years of the study adjacent to each other for comparison. 99 The findings about all students are discussed first. Each series of correlations with LE is discussed separately. Computer Output Summary Table Format The computer output summary tables all followed the same format (see Table 11). Across the top are the column identifiers. From left to right they are: Language, Non- Language and Total CTMM IQ scores; Reading, Writing and Mathematics STEP achievement test scores; followed by Grade Point Average and Grade Point Average for Elective Courses Only. The LE rating column is identified by EFF for LE rating. E.E1 identifies the column for efficiency ratings for elective courses. Differential Aptitude Test informa tion makes up the last nine columns. The top five items of the vertical listing on the right side of the table identify information about the test data. The lower part of the table consists of a correla tion coefficient matrix for the data summarized in the top portion of the table. The Relationships of Learning Efficiency Ratings to Test Results and Grade Point Average Data Question number one was posed to ascertain the LE correlations with the following data: (a) CTMM, (b) STEP, TABLE 11 ALL STUDENTS, STUDENT FILE SUMMARY, CLASS OF 1966 LAN N-L totl RDNG WRTN MATH gpa G. EL eff E.EL VERB NUMR ABST SPAT MECH CLER 5PEL SENT V1N 107 109 107 71 70 66 2579 2567 1020 1027 53 56 58 61 61 63 49 44 55 mean 15 14 13 25 25 24 590 575 308 321 30 30 27 27 27 28 30 29 30 SIGHA 1&0 162 150 099 099 099 4000 4000 2931 2998 099 099 099 099 099 099 099 099 099 MAXIMUM 06* 057 0?Z 001 002 003 739 700 502 500 001 001 001 001 001 001 001 001 001 minimum 096 105 078 1 098 097 096 3261 3300 2429 2498 098 098 098 098 098 098 098 098 098 RANGE ♦ 541 *671 +716 + 671 ♦ 627 ♦ 558 ♦ 502 -710 -726 +728 ♦ 607 ♦505 ♦ 367 ♦ 426 ♦ 304 ♦ 620 +677 ♦ 728 LAN *665 *412 ♦ 371 ♦ 530 ♦ 367 ♦404 -683 -647 +474 ♦522 ♦ 558 ♦539 ♦493 ♦305 ♦287 + 419 ♦536 N-L +644 + 595 + 662 +536 ♦ 513 -800 -788 ♦ 684 ♦639 ♦ 602 ♦ 523 ♦521 ♦ 341 ♦ 523 ♦ 625 ♦718 TOTt. + 640 ♦ 668 ♦ 599 + 524 -626 -6*9 ♦ 764 ♦ 611 ♦ 578 ♦ 423 ♦ 434 ♦ 319 ♦ 545 +667 ♦ 748 RONS ♦ 615 ♦ 672 ♦ 586 -526 -572 + 701 ♦ 605 ♦ 512 ♦343 ♦ 368 • 400 ♦ *79 +6*6 ♦ 712 WRTN ♦ 521 + 481 -622 -632 ♦ 655 + 675 ♦625 ♦ 492 ♦495 ♦ 294 ♦ 435 +580 +734 MATH *939 -124 -171 +570 ♦ 588 ♦455 ♦346 ♦301 ♦393 ♦505 +551 ♦626 GPA -111 -78 +508 ♦ 541 ♦ 456 ♦ 353 ♦320 ♦368 ♦ 445 ♦ 500 ♦ 564 g+el +9*9 -555 -477 -506 -432 -464 -224 -371 -SOfl -566 EFF -582 -493 .490 -410 -434 -241 -402 -526 -588 E.EL • 667 ♦ 570 ♦ 493 ♦ 464 ♦261 ♦607 ♦ 712 ♦ 904 VERS ♦ 574 ♦ 453 ♦ 433 ♦ 378 +497 +613 ♦ 914 NUMR ♦ 603 ♦ 525 ♦ 233 ♦ 332 + 515 +621 A85T ♦ 558 ♦ 196 *193 ♦ 366 ♦ 519 SPAT ♦ 206 ♦ 198 + 424 + 493 MECH +626 *599 SPEL ♦729 SENT 100 101 (c) GPA, and (d) DAT. The answer to this question was divided into several parts. In each part the findings are reviewed based on all students (the total population for which data was available) and subgroups based on sex, ability, LE, and curricular groupings. Relationships among CTMM Test Data and Learning Efficiency All Students (Tables 11 and 12) A high negative correlation between LE and CTMM scores was found in the data summaries for both years, 1966 and 1967. The total CTMM scores yielded a higher negative correlation than either of the partial scores. All were significant beyond the .01 level. Sex Groupings (Tables 13 through 16) The groupings based on sex differences exhibited relationships between LE and CTMM scores which were similar to each other. These patterns were very much like the findings for "All Students." Minor variations in the amounts of correlation were noticed but as these differences failed to exhibit a consistent pattern, they were judged to be insignificant. LAN N-l 103 10( 14 U 1*1 151 066 066 073 063 ♦ 531 5IZEI TABLE 12 ALL STUDENTS, STUDENT FILE SUMNAKY, CLASS OF 1967 TOTL RDNG WRTN MATH gpa G«EL eff E.EL verB NUHR ABST spat MECH cler SPEL SENT VlN 106 68 64 67 2666 2662 1206 1216 57 55 61 65 58 58 49 43 57 MEAN 12 27 27 25 586 608 594 606 29 30 26 26 27 29 31 30 30 SIGMA 139 099 099 099 3961 aOoq 6268 6347 099 099 099 099 099 099 099 099 099 MAXIMUM 073 003 002 001 1260 U66 596 546 001 001 003 001 001 001 005 001 001 MINIMUM 066 096 097 098 2701 2834 5672 5101 098 098 096 098 098 098 094 098 098 RANGE ♦676 ♦689 ♦ 719 ♦ 702 ♦ 611 ♦ 564 -56B -569 +710 ♦665 *564 ♦452 ♦ 416 ♦ 311 ♦634 ♦ 671 ♦ 752 LAN ♦675 *481 ♦ 474 ♦ 633 ♦ 464 ♦447 -575 -572 ♦51* ♦ 570 ♦599 ♦591 ♦ 456 ♦ 330 ♦ 423 ♦ 464 ♦ 589 N-L ♦ 670 ♦ 683 ♦ 766 ♦ 625 ♦ 576 -656 -654 ♦ 700 ♦708 +666 +595 ♦ 499 *363 +606 +649 ♦ 770 totl ♦ 864 ♦ 715 ♦602 ♦537 .550 -560 ♦ 774 ♦ 623 ♦ 544 ♦ 436 ♦ 378 ♦ 253 +646 ♦ 676 ♦ 767 rbng ♦690 ♦650 ♦594 -497 -500 ♦ 778 ♦ 661 ♦ 559 *468 ♦367 ♦291 ♦ 635 ♦692 ♦ 785 WRTN ♦ 604 ♦ 550 -556 -560 ♦ 716 ♦ 769 ♦ 668 ♦ 606 ♦ 523 ♦ 331 ♦ 544 ♦612 ♦819 MATH ♦949 -213 -214 ♦ 565 ♦598 *491 ♦ 412 ♦317 ♦370 .501 ♦560 ♦631 GPA -191 -150 ♦ 515 ♦ 546 ♦ 475 ♦ 396 ♦ 306 ♦ 342 ♦ 438 ♦ 509 ♦ 571 G.EL ♦ 989 -500 -454 -484 -423 -392 -197 -394 -408 -526 EFF -505 -459 -473 -418 -386 -202 .406 -421 -534 E.EL ♦651 ♦596 ♦ 465 ♦ 406 ♦ 266 + 667 ♦ 70+ ♦ 905 verb ♦ 580 ♦ 512 + 445 ♦ 359 ♦578 ♦ 614 ♦ 908 NUMR ♦ 659 ♦ 557 ♦ 200 ♦ 385 ♦ 484 ♦ 648 ABST ♦ 547 ♦ 278 ♦292 ♦ 390 ♦ 545 SPAT 411 ♦ 144 ♦ 259 ♦ 342 ♦ 463 MECH ♦ 334 ♦281 ♦337 CLER ♦ 661 ♦ 681 SPEL *723 SENT 102 TABLE 13 BOYS STUDENT FILE SUMHABY LAN N-L TCTl RDNG NRTN hath G P ft G.EL EFF E.EL VERB NUMR 107 111 106 66 64 72 2453 2451 979 983 53 56 16 12 12 26 25 23 562 541 287 307 29 30 160 162 150 099 099 099 3900 3967 2207 2169 099 099 064- 072 073 003 002 006 739 700 502 500 001 001 096 090 077 096 097 O93 3161 3267 1705 1669 096 O9B *539 + B09 ♦ 70H ♦ 691 *654 +569 + 490 -732 -740 + 712 ♦ 594 *649 *390 *396 *524 ♦ 374 *400 -642 -5B8 +470 +499 *637 *631 *681 *543 ♦ 50 3 -795 -772 +682 ♦ 627 *616 *731 +555 + 435 -648 -691 + 772 +630 *682 + 630 ♦ 479 -587 -658 + 718 +624 + 548 ♦ 490 -647 -651 +673 + 694 *903 -110 -180 + 527 ♦ 587 -79 -26 + 438 ♦ 507 + 953 -599 -501 -622 -523 + 645 SIZE* 299 CLASS OF 1966 ABST SPAT MECH cler SPEL SENT V J N 58 61 61 61 47 43 56 mean 26 25 26 26 31 2a 30 SIGMA 099 099 099 099 099 099 099 HAXIHUM 003 001 001 001 001 001 Q01 minimum 096 09B O98 098 098 09a 098 RANGE +433 +285 +362 +264 +567 +610 ♦ 722 LAN +407 ♦417 + 409 +290 +248 *335 ♦ 532 N-L ♦520 +404 + 436 ♦317 ♦477 +547 + 721 TDTl +542 +368 ♦ 377 ♦ 243 +493 + 625 ♦ 770 RDNG +510 ♦274 +312 +34B +548 ♦ 641 + 741 NRTN +625 +422 + 450 ♦ 263 + 414 ♦ 550 ♦ 76 3 MATH +403 +258 + 182 +338 ♦ A54 +504 +610 gpa +401 ♦271 +245 +306 +363 + 443 ♦ 514 GiEL -460 -328 -428 -196 -355 -461 -608 EFF -434 -292 -354 -214 -393 -473 -635 E.EL ♦ 552 +453 +416 + 188 ♦ 547 + 664 ♦ 899 VERB ♦ 570 + 409 + 364 ♦ 335 + 460 + 550 +912 NUHR ♦ 553 +466 ♦ 178 +310 +472 ♦619 ABST ♦ 530 + 132 + 104 *277 + 479 SPAT ♦ 97 ♦93 +300 +431 MECH + 105 + 130 + 297 CLER +583 ♦ 549 SPEL *656 SENT LAN N - L 107 ioa 15 15 154 150 071 057 063 093 *562 SIZE! GIELS totl RUNG WRTN HATH GPA G.EL 107 74 75 64 2703 2722 13 24 23 24 591 576 142 099 099 099 4000 4000 072 001 002 003 1043 1130 070 098 097 096 2957 2870 *B65 *731 ♦ 672 ♦ 623 + 564 ♦ 533 ♦ 691 *473 ♦ 429 ♦ 521 ♦ 460 ♦ 4B0 ♦673 *616 ♦ 647 ♦ 576 ♦ 575 ♦ 662 ♦ 712 ♦ 627 ♦ 590 ♦672 ♦685 ♦653 ♦ 599 ♦ 584 ♦ 964 303 TABLE 14 3T0DEHT FILE SUMMARY, CLASS OF 1966 EFF E.EL VERB NUHR A65T SPAT MECH cler 5PEL SENT YIN 1061 1070 53 56 58 62 60 65 51 45 55 MEAN 322 329 30 30 28 28 28 30 30 31 31 SIGMA 2931 2998 099 099 099 099 099 099 099 099 099 maximum 545 550 001 001 001 001 001 001 001 001 001 MINIMUM 2386 2448 098 09B 098 098 098 098 098 098 098 RANGE -715 -735 + 751 ♦ 616 *581 ♦ 449 ♦ 492 ♦ 344 ♦ 673 ♦ 743 ♦ 739 LAN -70S -665 ♦ 463 ♦ 546 +626 ♦ 637 ♦ 561 ♦333 ♦ 339 ♦ 489 ♦ 547 N-L -All -808 *692 ♦ 652 + 682 ♦ 623 ♦ 598 +373 ♦ 575 ♦ 696 ♦ 722 TOTL -659 • 662 + 770 ♦ 599 ♦ 622 ♦ 476 ♦ 497 ♦379 ♦589 ♦712 ♦738 RDNG -568 -589 + 724 ♦ 617 ♦ 541 ♦ 425 ♦ 451 ♦ 444 *611 ♦ 699 *726 WRTN -584 -598 ♦ 654 ♦ 673 ♦ 644 ♦ 564 ♦542 ♦ 348 ♦ 486 ♦ 623 ♦ 723 math -191 -230 ♦ 637 ♦ 616 ♦ 522 ♦ 429 ♦ 422 ♦ 429 ♦552 *606 +673 GPA -204 -189 ♦ 601 *604 + 529 ♦ 433 ♦ 408 ♦ 408 *518 ♦ 964 + o45 G.EL *962 -533 -465 -557 -523 -497 -267 -410 -558 -539 EFF -557 -475 -549 -517 -507 -286 -435 -585 -558 E *EL ♦ 690 ♦ 590 ♦ 527 ♦ 507 ♦ 324 ♦ 670 ♦ 754 ♦ 912 VERB ♦579 ♦ 493 *496 ♦ 416 *535 ♦ 669 ♦ 920 NUMR ♦647 ♦580 ♦281 ♦353 ♦ 554 ♦ 626 ABST ♦ 579 ♦ 244 ♦ 270 + 435 ♦ 554 SPAT +296 ♦299 ♦ 526 ♦ 548 HECH ♦ 354 ♦ 354 ♦ 389 cler *663 *649 SPEL *774 SENT 104 TABLE IS BOYS STUDENT FILE SUMMAEY, CLASS OF 1967 LAM N-L TOTL rDng WRTN math GPA G>EL EFF E.EL VER0 NUMR ABST SPAT MECH cler SPEL SENT VIN 103 110 107 63 58 70 2567 2554 1143 1139 55 56 60 64 38 56 46 43 57 MEAN 14 13 12 29 28 25 572 608 436 430 29 29 26 25 27 29 32 31 30 sigma 135 151 139 099 099 099 3961 3937 4730 4312 099 099 099 099 099 099 099 099 099 maximum 0?0 066 073 003 002 006 1260 1166 606 546 001 001 003 001 003 001 005 003 001 minimum 065 065 066 096 097 093 2701 2771 412A 3766 098 098 096 098 096 098 094 096 096 RANGE *524 *878 ♦ 715 ♦ 7b5 *695 ♦ 602 ♦ 553 -648 -649 ♦ 73-* ♦659 ♦ 577 *485 ♦ 341 ♦ 270 *634 ♦ 706 ♦ 752 LAN *665 ♦ 505 ♦ 549 *603 ♦ 473 ♦ 445 -653 -639 ♦ 534 *582 ♦ 607 ♦ 591 ♦ 427 ♦ 268 *44l *509 ♦ 602 N-L *703 ♦ 750 ♦ 746 *615 *570 -747 -740 *728 ♦ 711 *677 ♦ 612 ♦440 ♦307 ♦ 617 ♦ 697 ♦ 777 TOTL *863 ♦ 773 ♦ 579 *491 -654 -681 ♦ 773 ♦ 656 *343 ♦ 463 ♦341 ♦ 165 *661 *68$ ♦ 761 RDNG ♦789 •669 *593 -631 -647 *804 *744 ♦589 *538 •386 ♦ 229 «668 ♦727 *841 WRTN ♦ 609 *567 -691 -686 ♦ 731 ♦ 765 *689 ♦ 629 ♦ 505 ♦243 *581 *637 *821 MATH *926 -240 -242 ♦ 365 *626 ♦ 483 ♦ 386 ♦ 275 ♦ 366 *526 ♦ 584 ♦ 633 GPA -228 -145 ♦ 513 ♦ 569 ♦ 484 *392 ♦ 282 ♦338 *434 ♦ 489 ♦ 571 G.EL .971 -599 -540 -549 • 505 -414 -172 .446 -498 -625 EFF •610 -545 -524 -490 -401 -172 .476 -526 • 636 E.EL ♦ 677 *629 ♦ 556 *416 ♦ 2I9 ♦ 665 *698 ♦ 909 VERS *631 ♦ 559 ♦ 515 ♦330 *604 ♦ 632 ♦ 916 NUMR *694 ♦ 556 ♦ 134 *432 *478 ♦ 694 ABST *566 *217 *364 *381 + 606 SPAT SIZE J 207 ♦ 91 ♦ 240 + 317 ♦ 502 MECH .262 *256 ♦ 293 CLER ♦681 *694 SPEL *717 SENT 105 LAN 103 14 141 066 073 N-L TOTL RDNG WRTN HATH 106 103 72 71 64 14 12 25 24 26 147 138 099 09$ 099 068 075 007 00s 001 079 063 092 091 096 ♦548 + 874 *674 *713 *709 ♦886 +326 +511 *655 ♦678 +690 *772 ♦859 +714 ♦ 691 TABLE 16 GIJZL3 STDDEHT FILE SOMMAKY, GPA G*EL EPf E.EL VERB NUMr 2766 2812 1269 1295 39 53 583 5eQ 713 735 29 31 3925 4000 6266 6347 0$9 099 1300 1400 596 623 001 001 2625 2600 5672 5724 098 096 ♦640 *604 >552 -362 *6&5 *673 ♦563 *540 -544 -548 *520 ♦SSS ♦680 +64* -619 -628 *679 +694 ♦607 +559 -365 -580 ,778 *633 ♦604 +549 -526 -541 +773 +658 ♦659 +614 .487 -301 *718 ♦766 ♦973 -282 -253 +564 ♦615 -227 -219 +520 +58A ♦996 -475 -412 -488 -421 ♦636 CLASS OF 1967 SIZE! Z04 SPAT HECH CLER SPEL SENT V I N 66 59 59 50 43 56 MEAN 26 27 28 29 29 31 SISMA 099 099 099 099 099 099 MAXIMUM 001 001 001 003 001 001 MINIMUM 098 098 098 094 098 098 RANGE ♦ 420 ♦ 494 ♦ 354 ♦ 635 ♦633 ♦ 753 LAN ♦ 617 + 498 ♦ 410 ♦ 434 + 435 ♦ 585 N-L ♦ 585 ♦ 560 ♦ 431 ♦ 605 ♦ 603 +736 TOTL ♦ 408 • 426 ♦ 344 *627 ♦ 683 ♦ 775 RDNG ♦ 402 ♦ 366 ♦ 366 ♦ 612 ♦ 698 ♦ 767 WRTN -♦600 ♦ 548 ♦ 435 ♦323 ♦ 593 ♦ 818 MATH ♦ 440 *365 ♦ 370 ♦ 474 +595 ♦ 650 GPA ♦ 405 ♦ 342 ♦ 34l *444 ♦ 357 ♦ 604 G.EL • 402 -404 -234 .399 -378 -488 EFF • 412 -406 -245 -409 -391 -502 E.EL ♦ 413 ♦ 395 ♦ 310 ♦ 666 ♦ 713 ♦ 900 VERB ♦ 476 ♦ 382 ♦ 393 ♦ 562 ♦ 602 ♦ 898 NUMR ♦ 623 +559 ♦ 266 ♦ 326 ♦ 491 ♦ 604 ASST ♦ 530 ♦ 339 ♦ 214 ♦ 401 ♦ 485 SPAT ♦196 ♦282 ♦ 370 ♦ 423 MECH ♦ 413 ♦311 ♦ 382 cler ♦ «39 ♦ 670 SPEL ♦ 730 SENT 106 107 Ability Groupings (Summary Tables 17 and 18) Student files were classified as "Mentally Gifted," "General," "Basic," and "Educable Mentally Retarded." The "Educable Mentally Retarded" files were discarded. This provided three divisions based on ability and achievement. For purposes of comparison, the ability grouping data was combined with the "All," "Boy," "Girl" groupings. This information was taken from Tables 29 through 34. These tables demonstrate the negative correlations between CTMM scores and LE in another way. The "Mentally Gifted" group ing had the lowest mean LE rating and the "Basics" grouping had the highest. Student Files by Rank Order of Learning Efficiency (Tables 19 through 26) Relationships similar to the above emerged from the data when student files were placed in rank order by mean LE and examined. The highest negative correlations between CTMM and LE were observed in the top quartile division grouping for both years (-.674 and -.633) with a lowering trend to the bottom groupings where the negative correlations were -.352 and -.235 for the Classes of 1966 108 TABLE 17 STUDENT GROUPINGS IN RANK ORDER BY MEAN LE, GPA AND CTMM, CLASS OF 1966 Group No. Name N LE Group No. GPA Group No. CTMM 1 Basics 74 1.39 6 3.30 6 129 2 Girls 303 1.06 2 2.70 4 109 3 All 602 1.02 4 2.61 5 108 4 General 483 .99 3 2.57 2 107 5 Boys 299 .97 5 2.45 3 107 6 Mentally Gifted 45 .73 1 1.89 1 91 109 TABLE 18 STUDENT GROUPINGS IN RANK ORDER BY MEAN LE, GPA AND CTMM, CLASS OF 1967 Group No. Name N LE Group No. GPA Group No. CTMM 1 Basics 32 2.40 6 3.14 6 118 2 Girls 204 1.26 2 2.76 4 107 3 All 411 1.20 3 2.66 3 106 4 Boys 207 1.14 5 2.62 2 105 5 General 332 1.13 4 2.56 5 105 6 Mentally Gifted 61 .94 1 2.07 1 86 TABLE 19 TOP QUARTILE DIVISION SUMMARY, STUDENT FILES BASED ON LE, CLASS OF 1966 (N = 209) Lan N-L Totl Rdng Wrtn Math GPA G.El Eff E.El 91 94 92 48 50 44 2416 2443 1465 1479 Mean 10 11 7 25 25 21 564 588 290 307 Sigma 125 125 109 097 098 098 3815 3857 2931 2998 Maximum 064 057 072 001 002 003 739 666 1202 943 Minimum 061 068 037 096 096 095 3076 3191 1729 2055 Range +109 +691 +499 +497 +305 +565 +512 -508 -497 Lan +762 -106 — +104 +286 +356 -472 -353 N-L +240 +323 +262 +573 +594 -674 -579 Totl +768 +472 +599 +479 -330 -389 Rdng +357 +668 +564 -316 -363 Wrtn +513 +471 -255 -252 Math +922 -65 -81 GPA -77 +50 G.El +926 Eff TABLE 20 THIRD QUARTILE DIVISION SUMMARY, STUDENT FILES BASED ON LE, CLASS OF 1966 (N = 215) Lan N-L Totl Rdng Wrtn Math GPA G.El Eff E.El 103 105 104 68 66 60 2619 2617 1075 1076 Mean 10 9 7 22 24 21 609 610 66 99 Sigma 138 127 127 099 099 099 4000 4000 1196 1416 Maximum 077 074 085 012 002 Oil 940 1173 971 798 Minimum 061 053 042 087 097 088 3060 2827 225 618 Range -19 +683 +631 +630 +385 +750 +695 -332 -340 Lan +668 -48 -28 +155 +303 +322 -300 -146 N-L +393 +408 +394 +768 +746 -429 -324 Totl +769 +473 +746 +680 -238 -301 Rdng +448 +768 +695 -221 -326 Wrtn +664 +637 -177 -166 Math +952 -112 -178 GPA -99 +46 G.El +703 Eff TABLE 21 SECOND QUARTILE DIVISION SUMMARY. STUDENT FILES BASED ON LE, CLASS OF 1966 (N = 209) Lan N-L Totl Rdng Wrtn Math GPA G.El Eff E.El 110 111 110 80 76 76 2656 2647 899 900 Mean 12 9 7 17 20 17 589 573 39 76 Sigma 144 140 129 099 099 099 4000 4000 970 1171 Maximum 080 073 089 024 018 017 1194 1111 828 709 Minimum 064 067 040 075 081 082 2806 2889 142 462 Range + 95 +762 +525 +487 +385 +775 +709 -245 -352 Lan +669 .+ 47 +131 +233 +440 +425 -261 -205 N-L +405 +441 +333 +842 +777 -358 -410 Totl +718 +481 +716 +649 -201 -319 Rdng +463 +737 +702 -188 -234 Wrtn +629 +600 -195 -216 Math +939 -138 -302 GPA -131 - 11 G.El +539 Eff 112 TABLE 22 BOTTOM QUARTILE DIVISION SUMMARY, STUDENT FILES BASED ON LE, CLASS OF 1966 (N = 213) Lan N-L Totl Rdng Wrtn Math GPA G.El Eff E.El 121 120 120 89 85 84 2612 2605 729 730 Mean 11 11 9 13 15 16 614 615 76 90 Sigma 160 162 150 099 099 099 3916 4000 827 950 Maximum 089 088 097 025 025 013 837 937 475 500 Minimum 071 074 053 074 074 086 3079 3063 352 450 Range +309 +784 +464 +529 +433 +673 +638 -256 -291 Lan +822 +166 +234 +357 +514 +536 -287 -201 N-L +386 +457 +493 +727 +717 -352 -324 Totl +654 +458 +563 +495 -216 -308 Rdng +456 +661 +593 -145 -245 Wrtn +600 +559 -219 -260 Math +955 +213 +101 GPA +213 +248 G.El +861 Eff TABLE 23 TOP QUARTILE DIVISION SUMMARY, STUDENT FILES BASED ON LE, CLASS OF 1967 <N = 122) Lan N-L Totl Rdng Wrtn Math GPA G.El Eff E.El 88 94 91 40 39 40 2467 2508 1850 1871 Mean 10 11 7 26 24 22 535 591 880 886 Sigma 116 123 110 099 096 099 3818 4000 6268 6347 Maximum 066 066 073 002 002 001 1380 1181 1354 1249 Minimum 050 057 037 097 094 098 2438 2819 4914 5098 Range - 8 +642 +548 +454 +370 +614 +618 -383 -364 Lan +754 +141 + 70 +319 +373 +371 -498 -479 N-L +464 +354 +485 +675 +677 -633 -604 Totl +826 +628 +721 +650 -364 -361 Rdng +536 +648 +589 -268 -255 Wrtn +661 +627 -326 -309 Math +941 -254 -236 GPA -250 -189 G.El +989 Eff TABLE 24 THIRD QUARTILE DIVISION SUMMARY, STUDENT FILES BASED ON LE, CLASS OF 1967 (N = 101) Lan N-L Totl Rdng Wrtn Math GPA G.El Eff E.El 101 105 103 64 61 64 2675 2683 1197 1204 Mean 9 10 7 24 23 21 563 566 77 118 Sigma 124 126 119 099 099 099 3878 3826 1353 1646 Maximum 081 078 087 009 013 Oil 1538 1375 1073 883 Minimum 043 048 032 090 086 088 2340 2451 280 763 Range + 60 +677 +245 +373 +307 +614 +567 -247 -249 Lan +768 +151 +191 +396 +605 +598 -303 -218 N-L +267 +378 +486 +831 +794 -375 -317 Totl +727 +382 +610 +549 -285 -307 Rdng +362 +694 +646 -233 -231 Wrtn +637 +589 -401 -346 Math +946 -162 -195 GPA -155 + 39 G.El +696 Eff TABLE 25 SECOND QUARTILE DIVISION SUMMARY, STUDENT FILES BASED ON LE, CLASS OF 1967 (N = 112) Lan N-L Totl Rdng Wrtn Math GPA G.E1 Eff E.E1 108 113 110 79 75 79 2812 2827 988 998 Mean 10 9 7 19 20 18 615 612 51 90 Sigma 131 135 128 099 099 099 3967 4000 1072 1287 Maximum 085 086 089 021 013 018 1580 1533 890 722 Minimum 046 049 039 078 086 081 2387 2467 182 565 Range +182 +792 +520 +645 +469 +803 +743 -171 -279 Lan +753 + 64 +109 +373 +504 +471 -226 -211 N-L +400 +506 +564 +864 +802 -270 -329 Totl +780 +554 +693 +641 -162 -236 Rdng +554 +758 +735 -130 -157 Wrtn +721 +678 -197 -263 Math +942 - 21 -186 GPA - 5 + 88 G.E1 +611 Eff 116 TABLE 26 BOTTOM QUARTILE DIVISION SUMMARY, STUDENT FILES BASED ON LE, CLASS OF 1967 (N = 115) Lan N-L Totl Rdng Wrtn Math GPA G. El Eff E.E1 117 120 118 87 83 86 2712 2719 789 793 Mean 10 12 9 16 18 16 676 697 77 95 Sigma 141 151 139 099 099 099 3925 4000 889 1104 Maximum 086 092 098 022 035 021 1260 1166 596 546 Minimum 055 059 041 077 064 078 2665 2834 293 558 Range +354 +796 +474 +614 +650 +744 +723 -128 -131 Lan +854 +369 +323 +580 +600 +565 -239 -241 N-L +511 +555 +748 +810 +775 -235 -238 Totl +795 +517 +646 +602 -116 -173 Rdng +624 +725 +692 - 64 -102 Wrtn +738 +710 -154 -163 Math +976 +264 +198 GPA +287 +320 G.E1 +877 Eff 117 118 and 1967 respectively. (All of these are significant beyond the .01 level.) Curricular Groupings (Tables 27 and 28) The significance of the negative correlations between LE and CTMM scores was also apparent through inspection of the results of groupings based on curriculum offerings. (Student files grouped for analysis on the basis of several courses in the same area.) The computer output tables were placed in rank order by student LE, GPA, and CTMM. They are summarized in Tables 27 and 28. The groupings which ranked high on the LE listing ranked low on the CTMM listing and vice versa. Relationships among STEP Test Data and Learning Efficiency All Students (Tables 11 and 12) Substantial negative correlations (all significant beyond the .01 level) were found between each of the STEP achievement scores and LE scores though they were not as high as correlations between the CTMM scores and LE scores. The "Reading" and "Mathematics" scores yielded higher negative correlations than the "Writing" scores. These findings were verified by investigating the "back up" data and also the output from the groupings based on school 119 TABLE 27 CURRICULAR GROUPINGS IN RANK ORDER BY MEAN LE, GPA AND CTMM, CLASS OF 1966 Jroup No. Name N LE Group NO. GPA Group No. CTMi 1 Metals 12 1.35 20 3.57 20 129 2 Woods 19 1.23 16 3.37 21 128 3 Photography 23 1.19 21 3. 36 16 118 4 Agriculture (+2 SD) 17 1.17 18 2.96 18 115 5 Homemaking 112 1.15 19 2.93 19 115 6 Crafts 30 1.15 15 2.77 15 112 7 Automotive 41 1. 08 14 2.70 17 112 8 English— Gen. (+1 SD) 317 1.07 9 2.67 14 111 9 Music 79 1. 04 13 2.67 13 111 10 Art 52 1.04 17 2.56 9 107 11 Business 547 1.03 11 2.56 11 107 12 Ind. Arts 192 1.00 10 2.55 10 107 13 Athletics (Mean) 111 .96 3 2.44 12 106 14 Science 301 .94 5 2.40 3 102 15 Mathematics 403 .94 8 2.32 7 102 16 Leadership 28 .92 12 2.30 • 8 102 17 Mech. Drawing 73 .90 1 2.26 5 100 18 Foreign Lang. (-1 SD) 261 . 89 2 2.20 4 98 19 English— C.P. 228 00 00 « 7 2 .12 1 97 20 Math— Honors (-2 SD) 24 .79 4 1.96 2 97 21 English— Honors 46 .75 6 1.89 6 97 120 TABLE 2 8 CURRICULAR GROUPINGS IN RANK ORDER BY MEAN LE, GPA AND CTMM, CLASS OF 1967 Group No. Name N LE Group No. GPA Group No. CTMM 1 Agriculture 3 2.62 18 3.58 19 126 2 Homemaking (+2 SD) 44 1.54 19 3.56 18 124 3 Woods 5 1.35 12 3.22 17 114 4 Art 9 1.30 17 3.09 16 113 5 Metals 5 1.24 7 3.03 12 112 6 Ind. Arts 47 1.20 14 2.98 14 112 7 Music 32 1.22 11 2.94 7 111 8 English— Gen. (+1 SD) 60 1.21 4 2.92 11 111 9 Business (Mean) 411 1. 20 15 2.87 15 111 10 Automotive 12 1.16 16 2.80 5 108 11 Athletics 28 1.104 9 2.66 13 108 12 Leadership 10 1.103 13 2.55 4 107 13 Mech. Drawing 7 1.09 5 2.49 9 106 14 Mathematics 144 1.02 2 2.43 3 102 15 English— C.P. (-1 SD) 69 1.02 8 2.32 6 101 16 Science 36 .982 3 2.18 8 100 17 Foreign Lang. 66 .981 6 2.16 10 99 18 Math--Honors (-2 SD) 12 .91 1 2.16 2 98 19 English— Honors; 16 .83 10 1.99 1 90 Note: GPA and LE scores are higher than the 1966 scores as all "F" grades were excluded from the data. Data about "Crafts" and "Photography" missing and Ns are smaller because some 1967 Class data was unavailable. 121 attended. The depressed writing correlations were even more evident in the "school attended" outputs. Sex Differences The negative correlations remained beyond the .01 level. The "Reading" and "Mathematics" scores continued to exhibit higher negative r's than "Writing" for both years of the study for boys. This trend continued for the girls of 1966, but for the 1967 girls' grouping, "Mathematics" was the lowest. Ability Groupings The groupings identified as "Mentally Gifted" for the Class of 1966 (Table 2 9) yielded negligible negative correlations between LE and STEP data, with "Writing" almost significant at the .05 level (the highest of the three negative r's). The "General" grouping for the Class of 1966 (Table 30) exhibited substantial negative correlations with "Reading" and "Mathematics" (-.480 and -.541) and a lower negative r with "Writing" (-.318); all significant beyond the .01 level. The "Basic" grouping for the Class of 1966 (Table 31) contained lower negative correlations than the "General" TABLE 29 MENTALLY GIFTED STUDENT FILE 30MMAKI, CLASS OF 1966 LAN N-L totl RDNG WRTN HATH GPA G.El EFF E.EL VERB NUMR ABST SPAT HECH cler SPEL SENT VIN 131 129 129 97 95 94 3302 3304 734 736 92 92 88 85 85 80 83 85 95 HEAN ]0 11 B 2 4 6 469 519 117 122 9 11 10 15 15 24 18 16 7 SIGHA 160 162 150 099 059 099 4000 4000 1021 1010 099 099 099 099 099 099 099 099 O99 HAXIHUM 110 097 109 088 083 077 2304 2178 510 ;00 050 045 045 030 025 001 005 030 055 HI NI HUfi 050 065 041 Oil 016 022 1696 1822 511 510 049 054 054 069 074 09B 094 069 044 RANGE +97 ♦626 + 130 ♦377 ♦ 89 +96 ♦ 106 -456 -425 ♦252 ♦ 169 ♦ 17 + 139 ♦72 -214 +224 ♦ 413 + 322 LAN ♦ 800 -59 -134 ♦ 232 ♦ 68 + 99 -409 -368 -26 ♦ 264 ♦ 407 +419 +60 +336 -128 -178 ♦ 98 N-L + 41 ♦ 132 ♦ 226 ♦ 55 ♦ 74 -643 -593 + 122 +321 +341 + 381 + 106 ♦ 87 + 19 ♦ 105 ♦ 266 TOTL +383 ♦ 109 ♦ 290 +318 -46 - ♦ 462 ♦ 186 ♦ 159 + 176 +22 - ♦ 133 +265 +392 RDNG + 211 ♦ 195 ♦ 196 -221 -194 ♦ 572 + 320 +308 ♦ 128 - -234 ♦ 405 + 631 ♦ 590 WRTN ♦ 330 ♦ 365 -105 -29 ♦360 ♦ 529 +411 +50 + 162 + 146 ♦ 114 +?14 ♦ 442 h a t h ♦ 984 ♦ 641 ♦ 691 +245 +251 ♦ 144 +226 -61 ♦ 306 ♦ 157 ♦ 284 ♦ 161 GPA ♦ 605 ♦ 664 +261 + 274 ♦ 156 +246 ♦ 23 ♦ 279 ♦ 157 ♦ 319 ♦ 201 G« EL ♦ 984 -146 -177 .262 -92 -146 +231 -36 -58 -325 eff -88 -120 -218 -44 -68 + 212 -21 -4 -257 e.el + 352 ♦ 324 + 165 + 165 -155 ♦ 256 ♦ 512 + 782 VERB +275 ♦ 288 +161 ♦ 43 ♦ 154 + 213 ♦ 752 NUMR ♦ 255 + 142 +82 ♦ 281 ♦ 213 ♦328 AflST S12E: ♦250 +182 +84 +242 +243 SPAT -101 +28 +72 +219 MECH -76 -127 -149 CLER + 506 +314 SPEL ♦441 SENT 122 TABLE 30 GENERAL STUDENT FILE SUMMARY, CLASS OF 1966 LAM M-L TOIL RDNG WRTK MATH GPA G.EL EFF E.EL VERB NUHR A8ST 106 109 106 75 74 70 2616 2614 990 993 55 58 59 12 12 10 20 20 21 514 499 255 266 26 27 25 154 141 138 099 099 099 3900 3668 2931 2998 099 099 099 066 057 073 022 002 003 1300 1291 502 514 001 001 003 006 064 065 077 097 096 2600 2597 2429 2464 096 098 096 +390 +818 +615 +523 +507 + 396 +319 -656 -685 +636 + 446 +364 +631 +229 ♦ 172 + 424 +205 +234 -694 -596 +321 + 397 + 436 + 496 + 408 ♦555 ♦ 355 ♦327 -782 -769 +566 +499 ♦ 465 +738 +549 *466 +365 .460 .533 1 +674 +438 ♦459 + 442 +580 +472 -316 -367 +573 +425 +376 +360 +322 -541 -546 ♦534 +561 ♦537 +917 ♦ 125 +51 +420 ♦ 457 ♦ 335 + 134 + 168 ♦ 338 +403 +336 +960 -456 -334 -368 -494 -356 -374 -346 *536 *453 *424 *456 *376 ♦ 545 SIZE* 48-3 MECH CLER SPEL SENT YIN 61 65 50 45 56 MEAN '26 26 29 27 27 SIGMA 099 099 099 099 * 099 MAXIMUM 001 001 001 001 003 MINIMUM 096 ' 098 098 098 096 RANGE +322 + 145 ♦ 523 ♦ 566 +615 LAN +415 ♦ 165 ♦ 118 +263 +403 N-L +441 ♦ 175 +382 + 492 +603 TOTL +343 ♦ HI +422 ♦ 565 +625 rdng ♦259 ♦ 244 ♦ 453 +541 *566 WRTN +436 + 110 +287 + 452 +642 MATH + 200 ♦ 259 +376 + 421 ♦ 493 GPA ♦ 223 +240 + 304 ♦ 344 + 416 G.EL .360 -55 -241 -394 -449 EFF -346 -72 -283 -433 -483 E.EL ♦370 ♦ 97 ♦ 499 ♦ 606 +861 VERB +342 • 244 +346 ♦ 493 ♦ 876 NUHR ♦ 459 ♦ 101 ♦ 189 ♦ 397 +513 A6ST +506 +82 ♦ 81 ♦ 257 ♦ 457 SPAT + 104 ♦ 74 +323 ♦ 408 MECH + 115 ♦102 + 191 CLER + 524 + 472 SPEL ♦ 619 SENT 123 ZABLE 31 BASIC STUDENT FILE SUMMARY, CLASS OF 1966 «7 97 91 30 31 37 1891 1978 1391 U27 7 U 7 18 18 17 415 460 384 386 106 121 110 093 081 081 2872 2916 2662 2618 064 071 072 001 002 003 739 700 788 823 042 050 036 092 079 076 2133 2216 1874 179$ ♦ 183 +598 4-191 *295 -115 -62 +2 -529 -485 ♦857 4143 424 4136 -76 *158 -571 -514 ♦199 *131 490 ♦10 4126 -737 -661 SIZEi 74 VER5 NUHR ABST SP*T MECH CLER SPEL SENT VIN 16 19 33 46 41 35 20 15 15 MEAN 16 16 23 26 26 26 17 17 14 SIGHA 060 080 095 099 099 099 075 065 065 MAX IHUH 001 001 001 001 001 001 001 001 001 minihuh 059 079 094 098 098 098 074 064 064 range ♦ 72 -15 -144 ♦ 66 ♦ 68 •4 ♦ 29 ♦117 + 44 LAN 489 ♦ 101 ♦ 556 ♦566 ♦ 429 ♦ 153 .66 ♦86 ♦ 112 N-L 450 ♦ 63 ♦ 346 ♦ 466 ♦ 366 ♦ 144 -7 ♦ 152 ♦65 TOTL ♦501 ♦281 ♦ 416 ♦239 ♦ 138 ♦ 87 ♦ 181 ♦ 565 ♦ 493 RDNG 4388 ♦ 292 ♦ 187 ♦ 75 -6 ♦ 135 ♦ 366 ♦ 511 ♦ 441 WRTN 4215 ♦ 37 ♦361 ♦341 ♦ 131 ♦ 155 -51 ♦ 271 ♦ i 73 HATH ♦ 140 ♦ 118 .9 ♦ 109 -166 + 149 ♦ 170 ♦ 73 ♦ 181 GPA ♦ 136 ♦ 139 ♦ 109 ♦ 230 •42 ♦ 131 ♦ 146 ♦189 ♦ 207 G.EL -126 -69 -377 -352 -362 -38 ♦ 16 -275 -118 EFF -89 -52 • 301 -269 -299 -50 ♦ 8 -195 -93 e.el ♦ 306 ♦ 292 ♦ 368 ♦ 231 -118 ♦ 265 ♦585 ♦799 VER8 ♦ 388 ♦ 257 ♦ 95 ♦ 140 ♦ 299 ♦ 253 ♦ 795 NUHR ♦ 635 ♦ 394 ♦9 -116 ♦ 227 ♦ 426 ABST ♦ 556 ♦ 198 -128 ♦245 ♦ 412 SPAT ♦98 -195 ♦ 257 ♦ 218 HECH -34 ♦ 176 ♦39 CLER ♦ 325 ♦349 SPEL ♦ 531 SENT 124 125 grouping and a change in profile shape. "Reading" was -.442; "Writing" was -.342 (both significant at the .01 level); "Mathematics" was -.280 (significant at the .05 level). Some of the student files which were included in the Class of 1967 grouping for "Mentally Gifted" appeared to be improperly placed. (See Table 32.) Examination of the minimum test data scores indicated that they were much too low, and the large number of 61 does not parallel the number of 45 which was indicated for the larger Class of 1966. This information reduced the confidence in the find ings of this grouping. The "General" grouping for the Class of 1967 (Table 33) contained findings which were similar to those of the Class of 1966 grouping. The "Basic" grouping for the Class of 1967 (Table 34) exhibited negligible negative correlations of -.016 for "Writing" and -.088 for "Mathematics." The "Reading" correlation with LE was higher at -.272 but still insignificant. The profile was somewhat similar to the 1966 grouping in that the highest negative correlation with LE was in "Reading." It was also similar in that the TAHLB 32 MENTALLY GIFTED STUDENT FILE SUMMARY, CLASS OF 1967 LAN N-L TOTL RDNG WRTN math GPA G.EL eff E.EL 116 121 118 84 82 87 3143 3190 946 968 14 12 11 20 22 21 593 584 243 274 139 151 139 099 099 099 3961 4000 1831 1754 079 087 ' 087 025 008 001 1766 1687 606 546 060 064 052 074 091 098 2195 2313 1225 1208 ♦ 645 ♦ 923 ♦ 781 ♦ 813 ♦ 715 ♦ 665 ♦ 566 -774 -794 ♦ 895 ♦ 471 ♦ 542 ♦ 581 ♦ 409 ♦ 316 -737 -746 ♦ 70S ♦ 754 ♦ 720 ♦ 598 ♦ 489 -83? • 855 ♦ 904 ♦782 ♦ 707 ♦ 574 -629 -692 ♦ 793 ♦ 742 ♦ 629 -671 -716 ♦628 ♦ 549 -710 -714 ♦ 936 -215 -278 -131 -HO ♦ 965 SIZE* 61 VER0 NUMR ABST SPAT MECH CLER SPEL SENT VIN 80 81 eo 82 74 72 72 69 83 25 26 21 IB 22 23 26 32 26 099 099 099 099 099 099 099 099 099 010 001 020 015 010 003 005 001 00 3 089 098 079 064 089 096 094 096 096 ♦ 781 ♦ 746 ♦686 ♦ 595 ♦ 476 ♦ 453 ♦ 651 ♦ 781 ♦ 796 ♦ 561 ♦ 599 ♦ 502 ♦ 525 ♦ 393 ♦ 430 .428 ♦ 544 ♦ 610 ♦ 751 ♦ 748 ♦ 66^ ♦ 624 ♦ 464 ♦ 467 ♦ 605 ♦ 739 ♦ 755 ♦ 646 ♦ 625 ♦ 636 ♦ 576 ♦ 462 ♦ 353 ♦ 713 ♦ 797 ♦ 686 ♦ 860 ♦ 829 ♦626 ♦ 539 ♦ 520 ♦ 421 ♦ 675 ♦ 826 ♦ 895 ♦ 636 ♦ 696 ♦ 735 ♦639 ♦ 496 ♦393 *649 ♦ 728 ♦ 916 ♦ 624 ♦ 718 ♦ 668 ♦ 531 ♦ 343 ♦ 420 *497 ♦575 ♦ 688 ♦ 511 ♦ 605 ♦ 614 ♦ 458 ♦ 312 ♦ 326 ♦ 377 ♦ 435 ♦ 563 -722 -646 .490 -505 -515 -366 -594 -681 -734 -758 .693 -50T -529 -499 -405 .636 -736 -782 ♦ 778 ♦ 607 ♦595 ♦ 575 ♦310 ♦ 754 ♦ 864 ♦ 936 ♦ 769 ♦ 692 ♦ 522 ♦430 ♦ 645 ♦ 706 ♦ 944 ♦ 660 ♦ 317 ♦316 ♦ 485 ♦ 517 ♦ 736 ♦ 546 ♦ 292 ♦ 515 ♦ 442 ♦ 677 ♦290 ♦ 307 ♦ 48? ♦ 568 ♦ 309 ♦ 400 ♦ 364 783 *733 ♦ 837 MEAN SIGMA MAXIMUM MINIMUM RANGE LAN N-L TOTL RDNG WRTN HATH GPA G.EL EFF E.EL verb NUMR A8ST SPAT MECH CLER SPEL SENT 126 TABLE 83 GEHERAL STUDEKT FILE SUMMARY, CLASS OF 1967 LAM N-L TOTL ROhO KfiTN hath GPA G.EL EFf E.EL VER8 NUHR ■A6ST Spat HECH ClEr SPEL SENT V 1 N i02 107 1q5 62 55 66 2625 2648 1138 1153 56 54 61 65 58 57 47 '4 0 56 HEAN 12 12 10 25 24 22 535 553 283 309 27 26 24 24 26 28 29 2B 27 SIGHA U1 142 134 099 099 099 3925 4000 2166 2103 099 099 099 099 099 099 099 099 099 MAXIMUM 07< 072 06J 006 004 001 1260 1166 596 553 001 001 003 001 001 001 005 001 003 MINIMUM 067 070 053 091 095 098 2665 2634 1590 1550 098 098 096 098 098 09? 094 O96 096 RANGE .351 ♦030 ♦ 577 ♦ 626 ♦ 587 .4 81 ♦ 418 -653 -636 ♦ 595 ♦ 541 ♦ 378 ♦ 276 ♦ 263 ♦ 200 ♦ 555 ♦ 53? ♦ 652 LAlJ .622 ♦ 322 ♦ 297 ♦ 506 ♦ 333 .266 -590 -568 ♦ 355 ♦ 430 ♦ 455 ♦ 473 ♦ 331 ♦ 189 ♦ 307 ♦ 27® ♦ 442 N.-l ♦ 550 ♦ 566 ♦ 668 ♦ 495 ♦ 429 -763 -739 ♦ 58* ♦ 594 ♦ 509 .454 ♦ 361 ♦ 236 ♦ 529 ♦ 491 ♦ 668 TOTL ♦ 612 ♦ 592 ♦ 511 ♦ 422 -569 -561 ♦ 666 ♦ 491 ♦ 356 ♦ 259 ♦ 215 ♦ 145 ♦ 572 ♦ 580 ♦ 668 RDNG ♦ 565 .553 ♦ 479 -53? -537 ♦ 69J ♦ 543 ♦ 374 ♦298 ♦ 208 ♦ 184 ♦ 552 ♦ 593 ♦ TOO WRTN ♦ «37 .412 -616 -617 ♦ 606 ♦ 687 ♦516 ♦ 468 ♦ 384 ♦ 207 .414 ♦ 466 ♦ 741 HATH 1 ♦ 932 ♦21 -6 ♦ 428 ♦ 466 ♦328 ♦251 ♦ 172 ♦276 ♦ 379 ♦ 454 ♦ 506 GPA ♦71 ♦ 140 .♦368 ♦ 399 •308 ♦229 ♦ 153 ♦248 .304 ♦ ■*67 ♦ 429 G.EL ♦ 959 -548 -544 -462 -470 -420 -395 -379 -367 -330 -321 ‘ -98 -111 .431 -447 -384 -407 -578 -584 EFF E.EL . . ♦524 ♦ 436 ♦ 422 ♦ 337 ♦ 350 ♦562 ♦ 270. ♦ 308 ♦ 473 ♦ 436 ♦150 ♦274 ♦ 50 ♦184 .591 ♦ 460 ♦ 203 ♦ 129 ♦ 597 ♦ 475 ♦ 288 ♦zoO ♦ 868 ♦ 868 ♦ 494 ♦ 387 VERB NUHR ABST SPAT SIZE 1 332 ♦ 33 ♦ 109 ♦ 175 *326 HECH .259 ♦ 160 ♦ 567 ♦ 238 ♦ 597 ♦ 613 CLER 5PEL 5£NT 127 TABLE 34 BASIC STUDENT FILE SUMMARY, CLASS OF 1967 LAN N-L TOTL RDnG WRTN MATH GP & G. EL EFF E.EL VER® NUHR ABST SPAT M’ M ClER SPEL SENT VIN 83 as. 66 21 23 23 2071 2043 2408 2369 15 17 19 31 29 38 19 7 12 MEAN 7 12 6 16 14 15 309 339 1421 1449 14 15 11 22 23 29 2l 7 10 SIGMA 102 121 100 06 t 059 071 2500 2625 6268 6347 055 065 055 095 090 085 080 035 040 MAXIMUM 066 066 073 003 002 001 1380 nai 1312 1215 001 001 003 001 001 001 005 001 OUI MINIMUM 036 055 027 058 057 070 1120 1 444 4956 5132 054 064 052 094 089 084 075 034 039 RANGE -240 ♦ 349 + 437 + 45 -25 ♦ 123 ♦ 63 -452 -459 ♦ 261 ♦25 ♦ 33 -100 + 75 *127 -29 ♦366 ♦ 253 LAN ♦ 340 -232 -121 ♦ 135 + 226 + 166 -526 -516 -209 ♦ 114 ♦ 356 ♦ 368 ♦ 292 ♦ 353 -200 -50 -95 N-L + 37 -85 ♦ 111 ♦ 285 ♦ 186 -771 -765 -32 ♦ 131 ♦ 3*2 ♦ 310 + 331 ♦ 254 -205 ♦ 157 ♦ 57 70TL ♦ 528 ♦ 178 • 144 -250 -272 -292 ♦ 638 ♦ 111 ♦ 16 -189 ♦ 44 -176 *356 ♦ 475 ♦ 562 RUNG -66 ♦ 167 ♦ 130 -16 -9 ♦ 39« ♦26 ♦ 269 ♦ -68 -75 *275 ♦ 331 ♦ 301 WRTN ♦ 110 ♦ 129 -88 -77 -103 ♦ 120 ♦ 166 ♦ 299 ♦591 ♦ 134 *199 -86 ♦25 math ♦ 929 ♦ 33 ♦ 47 ♦ 48 ♦ 17 ♦ 199 ♦ 143 -50 ♦ 229 -48 -122 ♦ 13 GPA ♦ 87 ♦ 128 -96 ♦ 78 ♦ 147 ♦ 136 ♦ 2 ♦ 217 -34 .184 -49 G.EL ♦ 996 -164 -205 -261 .118 -297 -46 .184 -241 .286 EFF -186 -180 -254 -114 -277 -49 -177 -655 -287 E.EL -93 ♦ 176 -285 -369 -159 *220 ♦ 128 ♦ 551 VERB -97 ♦ 410 ♦ 291 -205 .336 ♦ 25 + 742 NUMR ♦ 308 ♦ 249 ♦ 20 ♦ 68 ♦ 78 ♦ 40 ABST ♦ 462 -11 -126 ♦ 113 ♦ 164 SPAT SIZEl 32 ♦ 17 ♦ 202 ♦ 201 ♦43 MECH -110 -173 -300 CLER ♦151 +432 SPEL ♦147 SENT 128 129 correlations were lower negative than those of the "General" grouping. Learning Efficiency— Rank Order Groupings Examination of Tables 19 through 26 indicated that as the LE rating decreased, the negative correlations with STEP scores also decreased. This was similar to the effect of the CTMM scores and LE. For both years of these test data summaries, the trend was more pronounced in the top and third quartile divisions than it was in the second and bottom quartile divisions. Curricular Groupings The "Honors English" student file summary for the Class of 1966 (Table 35) contained negligible negative correlations between STEP scores and LE. The Class of 1967 (Table 36) failed to verify these findings for the LE- "Mathematics" relationship as an insignificant positive relationship was demonstrated. The "College Prep English" grouping for the Class of 1966 (Table 37) contained correlations which were more nearly like those of the "All Students" groupings though not as robust. "Reading" had the highest negative LAN 133 10 160 114 046 ( TABLE 35 HONORS ENGLISH STUDENT FILE SUMMARY, CLASS OF 1966 N-L totl RUNG WRTN HATH L26 128 57 96 93 14 8 2 3 7 162 150 099 099 099 073 105 089 086 068 089 045 010 013 031 -212 ♦ 348 ♦ 209 ♦ 186 ♦ 25 + 785 -155 -158 ♦ 405 -54 -34 ♦241 ♦ 320 ♦293 ♦ 150 SIZE! 46 GPA G.EL EFF E.EL VERB 3362 3355 753 751 91 397 436 101 106 9 4000 4000 974- 1000 099 2440 2343 528 503 050 1560 1657 446 4-97 049 -47 -2 -333 -274 ♦ 158 ♦ 260 ♦223 -353 -3*5 -81 ♦ 103 ♦ 116 -634 -578 - ♦ 239 ♦274 -25 ♦ 8 ♦ 403 ♦52 ♦70 -160 -114 ♦ 544 ♦ 370 ♦ 352 -165 -153 ♦ 390 +978 ♦ 611 +654 + 82 ♦ 580 + 659 ♦ 980 ♦ 83 -187 -166 NUMR ABST 5PAT MECH CLER 86 89 83 62 80 15 11 17 18 22 099 099 099 099 099 035 045 025 025 001 0&4 054 074 074 098 ♦ • 244 -226 ♦ 69 -281 ♦310 ♦428 ♦634 ♦31 ♦ 240 ♦ 292 ♦ 261 ♦ 522 ♦ 166 ♦ 32 ♦ 112 ♦ 79 -76 ♦241 ♦ 94 ♦ 396 ♦ 101 -61 -165 -24 ♦ 473 ♦262 ♦ 190 + 172 ♦ 316 ♦ 137 ♦ 147 ♦ 319 -14 ♦ 225 ♦ 122 ♦ 143 ♦ 312 ♦ 52 ♦ 187 -263 -175 -133 -174 ♦ 107 -236 -158 -108 -122 ♦ 78 ♦ 526 ♦ 65 -30 ♦ 171 ♦ 130 ♦ 153 ♦ 376 + 64 ♦ 229 ♦ 320 ♦ 40 ♦ 333 ♦ 123 ♦ 192 ♦ 26 SPEL SENT YIN 65 66 93 MEAN 16 14 10 SIGMA 099 099 099 MAXIMUM 02 0 040 045 HINiMUM 079 059 054 RANGE ♦ 352 + 379 ♦ 106 LAN -67 -151 ♦ 80 N-L ♦ 103 ♦ 70 ♦ 129 TOTL ♦ 334 ♦ 275 +205 RONS ♦ 328 ♦ 508 ♦ 524 WRTN ♦ 399 ♦361 ♦ 433 HATH ♦ 36 ♦ 106 -6 6PA + 51 ♦ 161 -19 g.el -168 -177 -303 EFF -143 -110 -289 e.el ♦ 406 ♦ 638 ♦ 846 VERB ♦ 292 ♦ 342 ♦ 627 NUMR ♦ 123 ♦ 89 ♦ 34 ABST -142 ♦ 20 ♦ 110 SPAT ♦ 236 ♦ 133 ♦ 68 HECH ♦93 ♦ 27 *189 CLER ♦420 ♦ 401 SPEL ♦ 524- sent 130 TABLE 36 HONORS ENGLISH STUDENT FILE SUMMARY, CLASS OF 1967 LAN N-L TOTL RUNG WRTN HATH GPA G. EL eff E.EL verb NUHR ABST 5PAT MECH CL ER SPEL sent V IN 126 126 126 97 97 97 3561 3584 839 846 94 95 91 93 83 84 92 89 97 MEAN 6 6 5 1 2 2 281 300 74 78 4 3 7 4 17 11 6 11 1 S1GHA 139 147 138 099 099 099 3925 3954 955 966 099 099 099 099 099 099 099 099 099 MAXIMUM 116 U3 118 095 088 091 3032 3000 687 698 085 085 075 085 035 065 070 065 095 MINIMUM 023 034 020 ' 004 Oil 008 893 954 268 268 014 014 024 014 064 034 029 034 004 RANGE -69 ♦ 534 ♦ 275 ♦ 378 ♦ ♦ 418 ♦ 420 -125 -113 ♦ 216 ♦ 38 .207 « ♦ 345 -77 ♦ 432 ♦ 336 ♦ 89 LAN +784 ♦ 213 ♦ B4 -153 -53 -98 -652 -644 ♦ 84 ♦ 301 ♦225 ♦ 269 ♦ 275 ♦ 39 -28 -20 ♦ 69 N-L ♦356 ♦ 280 -85 ♦ 210 ♦ 169 • 636 -626 ♦ 233 ♦ 251 ♦ 53 ♦ 204 ♦ 447 - ♦ 233 ♦ 167 ♦ 115 TOTL ♦ 324 ♦ ♦ 461 ♦ 457 -231 -196 ♦ ♦ ♦ ♦ ♦ 691 ♦ 154 ♦ 108 ♦ 233 ♦ RDNG — ♦ 171 ♦ 119 -406 -432 -170 -91 .342 ♦ 74 ♦ 772 -272 ♦ 340 ♦ 396 .210 WRTN ♦ 673 ♦657 ♦ 396 ♦ 398 ♦ 517 ♦ 111 ♦ 357 ♦ 634 ♦ 279 ♦ 442 ♦ 103 -148 ♦ 511 MATH ♦ 968 ♦ 518 ♦ 508 + 52* ♦354 ♦ 480 ♦ 368 + 445 ♦ 524 ♦ 192 ♦ 191 ♦ 472 GPA ♦ 524 ♦569 ♦ 475 ♦394 ♦ 506 ♦ 404 ♦ 390 ♦ 529 ♦ 164 ♦ 162 ♦ 466 G.EL * ♦976 ♦ 201 ♦ 79 ♦ 370 -50 -293 ♦ 374 -95 ♦ 13 ♦ 197 EFF ♦ 178 ♦ 133 ♦ 413 ♦ 2 .307 ♦ 390 -112 -11 ♦ 202 E.EL ♦ 610 ♦ 489 ♦ 347 ♦278 ♦363 ♦ 425 -20 ♦ 840 VERB ♦ 632 ♦ 320 ♦ 149 ♦ 130 ♦ 396 ♦ 175 ♦ 754 NUMR ♦ 428 ♦ 24 ♦301 -114 -292 ♦ 463 ABST ♦304 ♦ 194 ♦ 124 -195 ♦367 spat SIZEl 16 ♦ 34 ♦ 292 ♦ 159 ♦206 MECH -272 -267 *299 CLER '♦722 ♦420 SPEL ♦50 SENT 131 TABLE 37 COLLEGE PEEP ENGLISH STUDENT FILE SUMMASI, CLASS OF 1966 LAN N-L TOTL rdng WRTN NATH GPA G.EL EFF E.EL VERB NUHR ABST SPAT HECH CLER SPEL SENT VIN 116 115 115 88 87 82 2932 2875 888 874 73 74 69 71 72 71 67 62 77 hean 11 12 9 12 12 16 409 443 200 197 21 22 24 23 23 26 24 24 21 SIGMA 150 162 148 099 099 099 3900 3906 2176 1922 099 099 099 099 099 099 099 099 099 MAXIMUM 083 057 077 026 030 017 1820 1740 510 500 010 003 003 001 005 001 005 001 005 MINIMUM 067 105 071 073 069 082 2080 2166 1666 1422 089 096 096 098 094 098 094 098 094 RANGE +329 + 760 ♦ 540 ♦ 373 ♦ 373 + 145 ♦ 130 -630 -637 ♦ 535 ♦379 +295 + 114 + 182 +82 + 448 ♦ 406 + 533 LAN +838 + 188 ♦ 19 + 322 -17 ♦ 39 • 64 B -599 + 184 +291 +403 ♦ 401 ♦ 350 ♦ 155 -26 + 59 +273 N-L ♦ 408 +220 + 400 ♦ 57 ♦85 -789 -763 ♦422 ♦ 392 +422 +356 ♦345 ♦ 127 +2 42 + 274 + 468 totl + 630 + 424 +264 ♦ 239 -509 -515 +667 + 430 +380 ♦236 +268 +76 ♦ 305 + 472 +625 RDNG ♦ 292 ♦ 406 + 361 -256 -254 + 467 + 404 +263 +89 + 164 ♦ 202 ♦ 359 + 4B3 + 497 WRTN + 116 + 149 -488 -467 + 408 + 609 + 482 +319 + 346 + 21 ♦ 191 + 259 +598 math +942 ♦355 +365 + 189 ♦ 247 +208 +84 +58 +202 + 169 +273 +239 GPA +306 +386 + 166 +256 *266 + 119 + 112 ♦219 ♦ 134 ♦265 + 230 6 + EL ♦ 974 -454 -381 -374 -316 -337 -41 -206 -274 -488 eff -46* -361 -325 -285 -296 -13 -230 -267 -484 E•EL ♦499 +324 +260 +288 +32 +433 +513 +847 VERB *415 +242 +330 *182 +248 *379 +864 NUHR +423 +446 +105 +105 +310 +421 ABST ♦511 +8 -57 +121 +299 SPAT ♦110 -24 +191 *365 HECH - 4 +44 +114 CLER SUE: 226 +440 +JB6 SPEL ♦504 SENT 132 133 correlation -.509 followed by "Mathematics," -.488 and "Writing," -.256 (all significant beyond the .01 level). The "College Prep English"grouping for 1967 (Table 38) did not substantiate the differentiation among the scores though all were significant beyond the .01 level. The "General English" groupings (Tables 39 and 40) both demonstrated marked negative correlations for the "Reading" and "Mathematics" scores and depressed negative correlations with the "Writing" scores though all were significant beyond the .01 level. The "Foreign Language" groupings contained substan tial negative correlations (P< .01) between LE and STEP scores with "Writing" again being the least significant of the three negative correlations. Table 41, the Foreign Language Student File Summary for 1966 is typical. The "Crafts" correlations (Table 42) were lower negative than the "Foreign Language" groupings. "Reading" and "Writing" were significant at the .01 level; and "Mathematics" was significantly correlated with LE at the .05 level. The "Metals" grouping for 1966 (Table 43) contained "Reading" and "Mathematics" scores which were almost sig nificantly correlated with LE at the .05 level and TABLE 38 COLLEGE PREP ENGLISH STUDENT FILE SUMMARY, CLASS OF 1967 LAN N-L TOTL RDNG WRTN HATH GPA G.EL eff E.EL VER0 NUHR ABST SPAT MECH CLER SPEL SENT VIK 110 112 111 84 78 80 2878 2850 1021 1012 74 70 70 73 66 65 65 57 75 M£4N 9 12 9 15 16 17 384 421 258 251 19 22 20 21 23 25 26 26 19 S1GHA 134 142 13A 099 099 099 3750 3826 2186 1986 099 099 099 099 099 099 099 099 099 HAX1HUH 077 oei 082 025 031 032 1933 1789 606 546 025 005 003 005 005 010 005 001 020 MINIMUM 057 061 052 074 068 067 1817 2037 1580 1440 074 094 096 094 O94 089 094 098 079 RANGE *338 + 764 + 413 ♦ 605 +481 ♦ 174 ♦ 179 -6 80 -662 ♦ 601 ♦291 +236 ♦ 152 ♦ 13 + 12 + 417 ♦ 603 + 523 LAN + 869 + 222 ♦251 +520 ♦ 61 ♦ 55 -641 -629 ♦ 175 ♦ 368 ♦ 289 + 481 + 206 ♦ 35 *45 + 142 ♦ 309 N-L ♦ 377 + 499 + 613 ♦ 134 + 133 -803 -785 ♦ 437 ♦ 417 ♦ 329 ♦ 4O5 ♦ 156 ♦ 26 + 251 + 423 ♦ 495 totl + 683 +290 ♦ 96 ♦ 133 .562 -526 ♦ 229 ♦ 159 ♦ 251 +69 ♦93 -205 ♦ 355 ♦ 281 ♦ 239 RDNG • 435 ♦ 260 +268 -581 -561 + 502 ♦ 376 ♦ 200 + 122 ♦ 17 -94 + 510 + 589 + 555 WRTN + 164 ♦ 118 -587 -611 ♦ 370 ♦ 707 ♦ 534 ♦ 239 + 375 -43 ♦201 + 326 ♦ 653 MATH ♦ 948 ♦ 299 + 331 -55 + 73 -134 -97 -157 ♦ 179 ♦ 192 ♦ 195 -40 GPA ♦ 254 ♦ 342 -40 ♦89 -85 -37 -79 ♦164 + 1B3 + 144 -29 G.EL ♦ 981 -533 -437 .468 -400 -278 ♦163 -2ZO -369 -608 EFF -530 -434 .440 -362 -249 ♦ 164 .210 -385 -612 E.EL ♦ 337 + 416 + 309 ♦ 159 -55 + 414 + 546 ♦ 818 VERB ♦ 417 ♦ 173 ♦ 340 -35 + 167 + 245 ♦ 790 NUHR ♦ 426 ♦ 429 -79 + 19 + 167 +525 ABST ♦ 437 ♦168 -73 -27 ♦ 279 SPAT size* 69 -55 -77 -69 + 265 HECH + 64 + 56 -112 CLER + 568 + 349 SPEL ♦ 472 SENT 134 TABLE 39 GENERAL ENGLISH STUDENT FILE SUMMARY, CLASS OF 1966 LAN N-L TOTL BONG HRTN NATH GPA G.EL EFF E.EL VERB NUHR ABST SPAT MECH CLER SPEL SENT V IN 100 106 102 63 61 59 2326 2367 1074 1093 40 45 50 55 54 58 37 31 42 MEAN 10 12 9 22 21 21 449 437 279 296 23 25 24 26 27 27 26 22 23 SIGMA 127 141 125 099 098 099 3640 3535 2931 2998 095 099 097 099 099 099 097 095 097 MAXIMUM 068 066 073 003 002 003 1300 1291 502 514 001 001 003 001 001 001 001 001 001 MINIMUM 059 075 052 096 096 096 2340 2244 2429 2464 094 098 O94 098 098 098 096 094 096 RANGE +333 +759 +473 ♦ 390 +378 + 120 ♦5 -654 -686 +454 ♦234 +209 + 107 ♦246 ♦ 77 +294 ♦352 ♦ 413 LAn + 635 ♦ 93 ♦ 46 +340 ♦ 111 ♦ 172 -596 -535 + 238 + 301 ♦ 374 + 400 ♦ 399 ♦ 123 -3 ♦ 179 ♦ 322 N-L +316 +236 ♦ 438 ♦ 129 + 109 -770 -744 +396 ♦ 316 ♦355 ♦317 ♦383 ♦ 109 ♦ 155 ♦ 301 ♦ 429 TOTL ♦ 685 ♦ 479 ♦ 267 ♦ 122 -424 -486 + 567 ♦ 270 +384 ♦ 227 ♦ 241 ♦ 33 +255 ♦ 405 ♦ 487 rdng +322 +411 +24l -264 -352 ♦ 427 ♦242 ♦ 259 ♦ 63 ♦ 113 ♦ 179 ♦ 283 ♦ 340 ♦ 401 wrtn + 183 + 132 -493 -497 ♦ 406 ♦ 407 ♦ 453 ♦ 317 ♦ 348 ♦ 103 ♦67 ♦ 310 +504 math ♦ 859 + 339 ♦237 ♦ 157 ♦ 307 + 158 ♦ 91 ♦28 ♦ 216 ♦ 146 ♦ 86 ♦ 282 gpa ♦338 ♦ 394 ♦ 67 +238 + 162 + 125 ♦ 79 ♦ 183 +52 -17 ♦ 187 G.EL ♦ 951 -373 -176 -307 -251 -335 -9 -114 -322 -327 eff -399 -203 -288 — 216 -288 -3b -156 -357 -359 E.EL ♦ 356 +385 ♦329 ♦274 ♦ 4 ♦302 ♦423 ♦789 VERB +348 ♦ 255 ♦ ZOO ♦ 205 ♦ 155 ♦ 296 +843 NUMR ♦ 564 ♦ 406 ♦ 14 -9 ♦ 240 ♦ 439 ABST ♦ 449 ♦ 81 -106 ♦ 138 ♦ 359 SPAT ♦ 38 -122 +207 ♦ 287 MECH StZEi 317 +9 - +133 CLER ♦321 *263 SPEL ♦429 SENT 135 TABLE 40 GENEEAL ENGLISH STUDENT FILE SUMMARY, CLASH OF 1967 LAN N-L TOTL RDnG WRTN HATH GPA G»EL EFF E.EL VERB NUMR *SST SPAT MECH ClEr SPEL SENT VIN 95 104 100 55 49 60 2322 23?4 1212 1244 46 36 56 61 55 48 34 30 42 MEAN 10 12 a 22 23 20 375 397 262 293 24 25 22 26 26 2T 25 20 23 SIGMA 114 134 121 099 094 099 3000 3222 1895 2005 090 097 095 099 097 099 095 085 090 HAXIMUM 074 072 086 008 005 018 1551 1357 703 663 003 001 010 005 003 001 005 001 003 HINI MUM 040 062 035 091 089 081 1449 1865 1192 1342 087 096 085 094 094 098 090 084 087 RANGE +48 +668 +256 +360 +447 +191 *75 -560 -562 +317 +392 +160 +131 *139 +96 *200 *90 +405 LAN ♦788 +186 +120 +575 +311 +290 -544 -512 +32° +306 +542 +565 *455 +210 +I9I +101 +356 N-L +306 +324 +721 +357 +267 -762 -741 +437 +466 +509 +5I8 +423 *226 +277 +131 +528 TOTL ♦709 +478 +354 +226 -477 *494 +468 +279 +213 +147 +122 +27 .240 +292 +446 RDNG +416 +513 +328 -346 -409 +488 +316 +199 +143 -6 +45 .266 +122 +463 WRTN ♦321 +188 -714 -716 +530 +420 +461 +493 +193 +41 *276 +184 +543 HATH +876 +146 +82 +76 +293 +187 +201 +62 +172 +83 +134 +217 GPA ♦210 +292 -15 +247 +176 +153 +160 *116 -67 +19 +141 G.EL +948 -573 -342 -457 -405 -350 -81 -295 -108 -526 EFF -587 -321-422 -396 -254 -111 -355 -170 -521 E.EL ♦398 +332 +261 +176 -190 +380 +291 +823 VERB ♦206 +218 +165 +156 +296 +271 +843 NUMR ♦603 +520 +46 +132 +25 +306 ABST ♦521 +8 +134 -7 +286 SPAT SIZEl 60 + 9 9 . 7 3 +7 *187 MECH ♦102 +24 -33 CLER ♦170 +415 SPEL +348 SENT 136 TABLE 42 FOREIGN LANGUAGE STUDENT FILE SUMMARY, CLASS OF 1966 LAN N-L TOTL RDNG WRTN 117 115 115 89 86 13 14 11 13 13 160 162 150 099 099 079 057 077 026 022 0&1 105 073 073 077 >456 >822 >607 >508 >873 >373 >542 >300 >458 >719 SIZEi 261 HATH GPA G.EL EFF E.EL VERB 81 2961 2901 899 883 73 17 472 516 212 205 22 099 4000 4000 2176 1922 099 017 1562 1583 510 900 005 082 2438 2417 1666 1422 094 >520 >392 >387 .681 -679 >616 >510 >311 >346 -69O -649 >411 >586 >392 >414 -806 -781 >583 >511 >444 >427 -553 -554 >652 >451 >495 >484 -422 -4O9 >588 >395 >396 -554 -539 >487 >960 >60 >83 >391 >15 ♦ 95 >375 >976 -541 -546 NUMR AB5T SPAT HECH CLER 73 71 72 69 71 23 23 23 25 26 099 099 099 099 099 001 003 001 003 001 098 096 098 O96 098 >496 >426 >240 >386 >98 ♦ 523 ♦ 558 ♦ 537 >429 >286 >583 ♦578 ♦ 4B3 >486 >220 ♦ 458 >426 >272 >434 >121 >432 >398 >236 >389 ♦ 172 ♦ 677 ♦ 559 >412 ♦459 ♦ 99 >436 ♦ 374 >233 ♦ 251 ♦ 224 >415 >411 >276 >266 ♦ 240 -463 -478 -4 06 -452 -108 -479 • 436 -364 -421 -79 ♦ 533 >418 >362 >441 ♦ 81 >462 >368 >411 ♦ 20b >474 >470 >538 ♦ 165 ♦ 87 >146 SPEL SENT VIN 67 62 76 HEAN 25 26 22 SIGMA 099 099 099 MAX1HUH 005 001 003 mjnimuh 094 098 096 range >500 >554 >635 LAN ♦ 204 *342 ♦ 5lfl N-L >402 >517 >659 totl >365 >516 >629 RUNG ♦ 377 ♦ 527 ♦ 578 wrtn >327 >433 >679 math >342 >450 *455 GPA >320 >438 ♦ 433 G.EL -287 -412 -591 EFF -294 -4 03 -593 E.EL ♦ 474 >564 >860 VERB >348 ♦ 500 ♦ 874 NUMR >230 *409 >495 ABST >76 ♦ 247 ♦ 419 SPAT >132 >386 ♦ 487 MECH ♦ 75 ♦116 ♦ 147 CLER >504 ♦ 467 SPEL ♦ 611 SENT 137 TABLE 42 CRAFTS STUDENT FILE SlftSiAW, CLASS OJ 19GB IAN N"L TOTL RDNG WRTN HATH GPA S3 J01 97 45 40 48 1891 S 12 9 25 25 17 494 115 12Z 115 097 091 085 3160 075 076 079 001 002 oie 739 040 046 036 096 089 067 2421 + 370 ♦ 772 + 568 + 568 +565 + 257 + 36« ♦272 *205 ♦ 325 + 122 ♦498 + 410 ♦ 491 + 206 ♦ 792 ♦ 566 + 425 ♦602 *349 ♦ 451 SIZEi • ' 30 G.EL EFF E.EL VERB NUHR ABST spat 2 u 5 3 1151 1206 27 26 35 46 493 323 285 22 20 22 29 3041 2190 Z11 7 090 080 085 097 700 610 685 001 003 001 001 2341 1580 1432 089 077 084 096 *447 >687 -653 + 507 ♦ 325 + 188 ♦ 219 ♦ 362 -600 -510 +475 ♦ 333 ♦ 566 +417 ♦ 464 -769 -676 ♦ 569 + 385 ♦473 ♦ 418 ♦ 46l -468 -530 ♦ 651 ♦ 575 + 504 ♦ 385 ♦ 312 -511 -642 ♦503 + 545 ♦ 404 ♦ 200 ♦ 527 -430 -455 + 520 ♦ 634 + 580 ♦ 530 ♦ 856 ♦ 280 *205 +241 +522 ♦253 ♦ 263 • 11 -♦98 + 491 ♦ 479 ♦ 337 ♦ 433 ♦ 926 .-522 -242 -374 •286 -411 • 314 ♦ 461 -376 +575 ♦637 •188 ♦ 505 ♦ 438 ♦ 693 HECH CLER SPEL SENT Vltl 53 45 27 19 26 KEAN 27 26 24 17 21 SlGHA 095 095 065 070 070 HAXJHUM 001 001 001 001 003 hinikuh 0 9« 094 084 069 067 RANGE + 382 -37 +528 + 602 +49e LAN ♦531 -66 *20 8 + 343 + 396 N-L ♦ 591 -105 ♦ 429 ♦ 547 ♦ 519 totl ♦ 242 *106 ♦ 434 ♦ 607 ♦ 727 RDNG ♦ 161 +225 +351 ♦ 535 + 649 WRTN ♦224 ♦ 185 + 202 + 544 ♦ 671 HATH ♦ 81 ♦ 281 + 131 + 316 + 466 6PA ♦ 255 ♦ 172 + 211 ♦ 515 + 565 G.EL -410 + 166 -380 -415 -393 EFF -339 ♦ 92 -398 -319 -400 F»El ♦ 381 ♦ 70 ♦514 ♦ 633 ♦368 VERB ♦ 319 ♦ 64 ♦ 195 ♦ 303 ♦ 817 NUHR ♦ 404 -21 ♦ 240 ♦ 391 *664 ABST ♦537 ♦ 129 ♦ 157 + 54fl ♦ 527 SPAT ♦ 43 ♦ 237 ♦ 376 + 386 HECH -Z98 ♦ 200 ♦ 127 CLER +290 ♦464 SPEL **86 sent 138 / TABLE 43 METALS STUDEET FILE SUMMARY, CLASS OF 1966 LAN N-l TCTE RDNG WRTN NATH GPA G.EL EFF E.EL VERB NUHR ABST SPAT HECH CLER SPEL SENT VIH 9l 105 97 40 36 44 2266 2526 1354 1-4 64 28 38 37 54 56 42 29 24 31 MEAN 10 12 5 16 17 17 359 506 357 371 19 26 19 29 21 27 16 14 17 SIGH A 115 132 120 072 067 076 2740 3250 2102 2169 060 065 085 095 080 090 06 5 050 050 HAXIHVh 077 050 066 D16 010 017 1600 1615. 795 902 003 010 010 003 005 005 . 005 003 005 NINIHUM 035 042 034 054 057 059 1140 1635 1307 1267 057 075 075 092 075 085 060 047 045 RANGE ♦ 741 ♦ 677 ♦ 551 ♦ 141 + 419 ♦ 100 ♦ 460 -628 -426 ♦ 350 ♦ 212 —66 ♦ 163 ♦ 209 ♦ 212 ♦ 119 -6 ♦ 353- LAN * *960 ♦ 474 ♦ ♦ 445 -380 -91 -691 -814 -213 ♦ 242 ♦ 205 ♦ 328 ♦ 364 ♦ 366 ♦ 148 -145 ♦79 K-L ♦ 513 ♦ 72 ♦ 439 -190 ♦ 116 .840 -716 -58 ♦ 246 ♦ 115 ♦ 315 ♦ 299 ♦ 362 ♦ 42 -121 ♦ 166 TOTL ♦ 431 ♦ 773 ♦ 112 ♦ 232 -546 -507 ♦209 ♦ 651 ♦ 565 ♦ 581 ♦ 366 *133 -162 ♦ 326 ♦ 725 PONG -10 ♦ 187 ♦ 89 -104 -170 + 150 ♦ 141 ♦ 503 *256 -202 ♦ 568 -266 ♦ 440 ♦ 135 WRTN ♦ 21 ♦ 172 -510 -*47 ♦ 78 ♦ 725 ♦ 312 *426 ♦ 245 -94 -37 *160 ♦ 723 hath . ♦856 ♦505 ♦ 671 ♦717 ♦ 174 -52 ♦ 134 ♦236 ♦ 109 -169 ♦ 619 ♦ 562 CPA ♦284 ♦5 Iff ♦ 703 ♦ 304 -77 -77 ♦305 ♦ 161 ♦ 65 ♦ 4 32 ♦ 634 g.el ♦945 ♦ 383 -254 -263 • 236 -109 -256 -26 ♦ 271 -4 EFF ♦ 474 -172 -309 -379 -41 -212 ♦ 86 ♦ 234 ♦96 E.EL -126 -252 ♦ 10 *202 •186 ♦ 252 ♦516 ♦ 464 VERB ♦ 496 ♦307 ♦ 245 ♦ 154 -189 ♦ 218 ♦ 012 NUHR ♦495 ♦ 407 ♦ 535 -140 ♦251 ♦293 ABST ♦27B ♦ 127 -564 ♦168 ♦334 SPAT ♦214 ♦ 340 + 406 ♦ 363 HECH -15 *358 -24 CLER '512 E.* 12 ♦120 -50 SPEL *522 SENT 139 140 "Writing" scores which were negative in direction but lacked any significance. For 1967, "Reading" was nega tively correlated at the .01 level with "Mathematics" and "Writing" negatively correlated at the .05 level of signif icance. A similar pattern existed in Table 44, the "Wood- shop" grouping for 1966. "Reading" and "Mathematics" correlations with LE were significant at the .01 level. "Writing" was significant at the .05 level. The Class of 1967 table exhibited a similar tendency. The "Industrial Arts" tables (45 and 46) contained correlations between STEP and LE which were similar to the "All" groupings of Tables 11 and 12 in that the negative correlations were significant beyond the .01 level and "Writing" was the lowest of the three. The "Honors Mathematics" grouping for the Class of 1966 (Table 47) contained slight negative correlations for "Reading" and "Mathematics" and a negligible positive correlation with "Writing" (+.098). For the Class of 1967 (N = 12), a similar profile shape existed. "Reading" and "Mathematics" negative correlations were significant at the .01 level and the "Writing" was significant negative at the .05 level. TABLE 44 WOODSHOP STUDENT FILE SUMMARY. CLASS OF 1966 LAN W-L TOTL RDNG HR Til HATH gpa G.EL EFF E. EL VERB 95 101 97 49 41 61 2200 2516 1237 1384 32 12 9 9 21 23 22 436 399 390 398 23 118 116 116 093 086 098 2680 3250 O O* ( V I ZI69 080 O79 063 086 016 002 021 1448 1909 722 781 001 039 033 030 075 084 077 1432 1341 1468 1388 079 *609 + 91 A +549 ♦ 336 ♦ 561 ♦ 79 ♦ 10 .690 -751 ♦404 +860 ♦ 465 ♦ Z95 + 458 ♦ 69 ♦ 271 -606 -545 ♦ 465 +575 ♦ 296 ♦ 546 ♦ 34 +51 -744 -770 ♦ 468 ♦ 582 ♦ 775 ♦ 9 -82 -655 -724 ♦ 362 ♦ 674 ♦ 224 + 165 -477 -544 ♦ 55 ♦ 175 ♦ 154 -637 -676 ♦ 184 ♦ 817 ♦ 460 +346 ♦ 15 ♦ 332 ♦ 389 + 146 ♦ 955 -272 -201 SIZE: 19 NUHR ABST SPAT HECH CLER SPEL SENT VIM 32 46 46 46 52 21 22 30 HEAR 21 25 25 26 22 19 13 21 S16HA 085 O9O 097 095 097 065 045 075 MAX1HUH 010 010 003 001 015 005 003 003 min imum 075 080 O94 094 082 060 042 072 RANGE ♦ 334 ♦ 350 -73 ♦ 82 ♦ 256 -59 -25B +477 LAN + 372 ♦472 ♦298 ♦ 420 ♦ 377 -383 -177 ♦ 505 N—L ♦ 394 ♦ 444 ♦ 134 ♦ 199 ♦ 319 -334 -246 +537 TOTL ♦ 593 ♦ 670 ♦ 304 ♦ 366 ♦ 467 -233 ♦ 162 ♦ 605 RUNG ♦200 ♦308 ♦ 82 ♦ 136 ♦ 545 ♦ 27 ♦47 + 180 HRTN ♦ 559 ♦ 675 ♦ 216 ♦ 239 ♦ 353 -30 + 59 ♦ 465 MATH *136 -36 -405 -6 ♦ 45 -6 -119 ♦ 125 gpa ♦ 246 -77 *377 ♦ 245 ♦ 2 ♦ 189 • 36 + Z17 G.El -337 -525 -332 -187 -403 ♦ 161 + 67 -363 eff -314 -534 -292 -99 -451 + 263 ♦ 137 -335 e .el ♦ 446 ♦ 330 ♦342 ♦ 528 -78 -45 +391 ♦ 839 VERB ♦ 557 -51 +734 -117 • 108 ♦ 341 ♦ 853 NUMR ♦ 500 ♦ 457 ♦ 15« -451 ♦ 333 ♦ 538 ABST ♦ 171 ♦ 63 -410 +209 + 169 SPAT -111 -168 -125 ♦ 406 -276 + 46 ♦ 734 -82 -123 ♦ 386 mech CL£R 5P£L sent 141 TABLE 45 INDUSTRIAL ARTS STUDENT FILE SUMMARY, CLASS OF 1966 LAN N-L TOTL R8NG WRTN MATH GPA G.EL EFF E.EL VERB NUMR ABST SPAT MECH CLER 5PEL SENT YIN 103 110 106 62 57 69 2309 2361 1006 1028 46 51 54 59 61 59 40 36 49 MEAN 14 1Z 1Z Z6 25 22 476 476 303 3Z6 28 29 26 24 26 26 28 25 28 SIGMA 14Z 16Z U B 099 O99 899 3900 3675 2207 2169 099 099 097 099 O99 099 O99 099 099 max imum 064 071 078 003 002 o n 1300 1222 510 5PO 001 001 003 001 O01 003 005 003 001 min imum 078 0 91 070 096 097 068 2600 2653 1697 1669 098 O98 094 098 O98 096 O94 096 098 RANGE .58« .901 .716 .666 ♦ 629 ♦ 501 ♦ 37.8 -754 -760 ♦ 696 ♦ 531 ♦ 373 ♦ 226 ♦ 390 ♦ 304 ♦ 558 ♦ 520 ♦ 692 LAN + 860 ♦500 .471 ♦ 558 ♦357 ♦ 353 -687 -6+0 ♦ 501 *478 +478 ♦ 408 +439 +278 ♦288 ♦319 + 559 N-L ♦ 689 ♦ 638 + 672 ♦ 483 ♦ 400 -813 -795 *673 ♦ 566 ♦ 469 ♦ 358 +456 ♦ 325 ♦ 472 ♦ 475 ♦ 703 TOTL ♦ 785 ♦ 730 ♦505 ♦ 374 -706 -724 .743 ♦ 583 ♦589 ♦ 347 ♦ 399 ♦ 226 ♦ 498 ♦ 594- +748 rung ♦ 681 ♦ 568 + 394 -625 -670 +656 .557 ♦ 500 ♦ 255 ♦ 335 ♦ 372 ♦ 495 ♦ 579 ♦ 686 WfiTN ♦ 499 ♦ 410 -661 -666 ♦ 612 ♦ 649 .595 ♦ 331 *403 ♦ 302 ♦ 34? ♦ 457 ♦ 723 MATH ♦ 873 -80 -129 ♦ 462 ♦ 536 ♦ 326 ♦ 234 ♦ 250 ♦ 310 ♦ 371 ♦ 429 ♦ 557 GPA -25 ♦ 56 ♦ 370 ♦ 440 + 316 ♦ 233 ♦ 304 + 290 ♦ 257 ♦ 330 ♦ 449 G'EL .959 -587 -458 -465 -285 -383 -241 -375 -433 -594 eff -585 -468 -433 -254 -320 -239 -401 -444 -600 e.el + 579 +505 ♦ 386 ♦ 425 ♦ 141 + 547 ♦ 626 +87 7 VERB + 527 ♦ 367 + 357 ♦ 336 ♦ 402 ♦468 + 894 NUMR ♦ 504 ♦ 414 ♦ 154 *239 .408 ♦ 584 ABST ♦ 510 ♦ 115 ♦ 84 ♦221 ♦ 433 SPAT ♦ 123 ♦ 103 ♦ 285 ♦ 452 MECH ♦ 65 + 131 ♦ 278 CLER 11ZE i 192 ♦ 544 +525 SPEL ♦ 59 9 sent 142 TABLE 46 INDUSTRIAL ARTS STUDENT FILE SUMMARY, CLASS OF 2967 LAN N-L TOTL rong WRTN HATH GPA G-'EL Eff e .el VERb NUMR ABST SPAT I1ECH Cl Er SPEL SENT V I N 95 107 101 45 37 54 2160 2212 1240 1260 38 39 54 58 55 49 31 26 37 MEAN 12 13 10 26 25 27 404 434 388 392 25 26 26 28 26 29 27 24 25 S1GHA 125 142 134 098 096 098 3187 3OB6 2613 2637 097 097 097 099 099 099 095 095 099 MAXIMUM 070 079 084 003 002 009 1260 1166 669 670 001 001 010 005 003 001 005 003 001 MINIMUM 055 063 050 095 094 069 1927 1920 1944 1967 096 096 087 094 096 098 090 092 098 RANGE ♦ 338 ♦ 800 ♦ 560 ♦ 607 ♦ 615 ♦ 423 ♦ 392 -686 -664 ♦ 581 ♦ 528 ♦ 514 *374 ♦ 278 ♦ 175 *459 ♦ 564 ♦ 623 LAN ♦830 ♦ 282 ♦ All ♦ 580 ♦ 467 *466 -517 -496 *447 *541 ♦ 606 ♦ 694 ♦ 465 ♦ 260 + 394 ♦ 44* +548 N-L ♦ 506 ♦ 617 ♦ 727 ♦ 544 ♦ 522 -731 -706 ♦ 617 ♦ 657 ♦ 685 *660 ♦ 455 + 270 *526 ♦ 613 + 712 TOTL ♦ 806 ♦ 660 ♦ 387 *2«7 -690 -713 ♦ 649 ♦ 417 ♦ 434 ♦ 459 ♦ 322 -15 ♦ 6*1 ♦ 575 ♦ 602 rdng ♦720 *551 ♦469 -652 -663 *710 ♦ 657 ♦ 542 ♦ 579 ♦ 395 ♦ 48 ♦ 612 ♦ 677 ♦ 770 WRTN ♦ 430 *445 -793 -757 ♦ 602 ♦ 563 *694 ♦ 691 *564 ♦ 165 +501 ♦ 558 ♦ 665 MATH ♦ 895 -105 -117 *304 ♦ 577 ♦ 477 ♦ 334 ♦ 267 ♦ 240 ♦ 351 ♦ 454 ♦ 481 GPA -131 -29 *305 ♦ 560 ♦ 562 *365 ♦ 371 ♦281 ♦ 219 *404 ♦ 472 G.EL ♦ 965 -639 -482 -598 -653 -481 -47 .499 -534 -650 EFF -616 -454 -5 IS -623 -418 • 4 -541 -535 -622 E.EL ♦ 523 ♦ 579 ♦ 496 ♦ 272 -71 *436 ♦ 543 ♦ 861 VERB ♦ 618 ♦ 559 ♦ 457 ♦ 240 *412 + 567 ♦ 876 NUMR *647 ♦ 544 ♦ 168 ♦ 282 +476 ♦ 684 ABST ♦ 525 ♦10B ♦ 310 ♦ 476 ♦ 603 SPAT SIZEl 47 ♦184 *260 ♦ 377 ♦ 414 MECH *166 ♦ 132 + 107 CLER ♦602 ♦ 480 SPEL *633 SENT 143 TABLE 47 H0H02S MATHEMATICS STUDENT FILE SUMMARY, CLASS OF 1966 LAN N-L TOTL RDN6 HRTN HATH GPA G.EL 130 129 129 95 96 96 3377 3607 13 11 9 6 6 4 226 243 160 130 150 099 099 099 4000 4000 111 111 113 072 070 077 3000 3058 0*9 039 037 027 029 022 1000 942 ♦ 24* ♦ 769 ♦ 435 ♦ 205 ♦ 301 ♦ 133 ♦ 138 ♦ 767 • 106 -196 -158 -417 -402 ♦ 203 -18 ♦ 72 -195 -182 ♦ 222 ♦326 ♦ 477 ♦ 444 • 108 ♦ 633 ♦ 53* ♦ F24 ♦ 286 ♦ 909 51ZE« 1 24 EFF E.EL VERB NUMR ABST SPAT MECH 790 799 93 93 92 88 85 106 108 7 8 a 15 17 973 1022 099 099 099 099 099 575 599 075 070 065 030 040 400 ♦23 024 029 034 069 059 -752 -744 ♦ 639 ♦ 173 ♦ 246 ♦81 ♦ 148 -717 -717 ♦ 165 ♦ 181 ♦549 ♦240 ♦53 -927 -923 ♦ 476 ♦ 180 ♦ 43B ♦ 198 ♦ 142 -275 -272 ♦ 735 ♦ 250 -41 -11 ♦ 224 ♦ 98 ♦ 64 ♦ 129 ♦ 291 -20 ♦ 110 -185 -227 -133 ♦ 536 ♦ 108 - -71 ♦ 673 ♦ 360 ♦ 342 ♦ 313 ♦ 211 -258 ♦ 66 ♦ 10 ♦ 298 ♦ 375 ♦ 3*1 ♦ 269 -272 ♦ 132 ♦ 276 ♦ 974 -539 -236 -525 -126 -223 -504 -1B9 -540 -75 -77 ♦ 438 ♦ 208 ♦ 339 ♦ 163 -124 ♦ 130 ♦ 402 ♦ 263 -72 ♦ 197 CLER SPEL SENT V IN 75 80 84 95 MEAN 25 23 17 5 SIGMA 099 C99 099 099 KAX1HUH 001 005 035 080 MINIMUM 098 094 064 019 RANGE -235 ♦338 ♦ 562 +502 LAN ♦ 190 -190 -95 + 192 N-L -73 ♦ 23 + 246 ♦ 405 TOTL -174 ♦ 277 + 389 ♦ 641 R0H6 -19 ♦ 227 +779 ♦96 WRTN ♦ 160 ♦362 +317 +376 math ♦ 5 ♦ 383 + 527 + 227 gpa ♦ 7 ♦ 331 ♦ 525 ♦ 318 G.EL ♦88' .36 -257 -492 EFF ♦ 88 -5* -237 -422 F.El -45 ♦ 499 ♦ 501 ♦ 849 VERB ♦ 130 + 154 +336 *793 NUMR ♦ 14 + 147 + 166 ♦ 284 ABST ♦ 87 4 ♦ 274 -38 SPAT ♦ 237 +69 + 136 ♦ 416 MECH -36 -72 -74 CLER + 487 +3*5 ♦ 371 SPEL SENT 144 145 Relationships between GPA and LE In general, there appeared to be a significant negative correlation between GPA and LE. For the "All” groupings, P <.01 (Tables 11 and 12). This -r held for both boys and girls, though for the Boys' 1966 grouping (Table 13) the -r was only significant at the .05 level. Tables 17, 18, 27, and 2 8 demonstrate these inverse rela tionships . Ability Groupings Examination of the findings which were based on ability groupings yielded results which were different from those outlined above. That is, substantial positive corre lations (P < .01) existed when the 1966 student files were grouped on the basis of ability. (See Tables 29, 30, 31.) For the 1967 groupings (Tables 32, 33, and 34), no rela tionship existed. Learning Efficiency— Rank Order Groupings For the Class of 1967, the trend was that as LE decreased, the negative correlations with GPA decreased. This trend was not as clearly defined for the Class of 1966 as illustrated by the following table of data extracted from Tables 19 through 26. 146 TABLE 48 CORRELATIONS BETWEEN LE AND GPA (From Tables 19-26) Quartile Divisions (Based on Rank Order LE) Year 1966 1967 Top -.065 -.254 (P < .01) Third -.112 -.162 -.128 Second (P < .05) -.021 + .213 + .264 Bottom (P <.01) (P < .01) Curricular Groupings Positive correlations between GPA and LE were identified in many of the curricular groupings. The table which follows shows the significant positive and negative correlations between LE and GPA for both years of the study Relationships between the Differential Aptitude Test Scores and Learning Efficiency All Students (Tables 11 and 12) Generally, the correlations were found to be nega tive. All correlations were significant beyond the .01 level. "Clerical Speed and Accuracy" exhibited the lowest negative correlation for both the 1966 and 1967 groupings. "Spelling" and "Mechanical Reasoning" alternated with each other for the second and third lowest negative correlations The highest negative correlations were with the "Verbal/ Numerical" scores (Table 50). Sex Groupings (Tables 13 through 16) The differences between the profiles of boys and girls appeared to be negligible and in keeping with the profile shape of the "All" groupings. 148 TABLE 49 CURRICULAR AREAS, CORRELATIONS BETWEEN LE AND GPA Year Curricular Area 1966 1967 N r P N r P 1. English— Honors 46 + .611 <.01 16 + .518 <.05 2. English— CP 288 + .355 <.01 69 + .299 in o « V 3. English— General 317 + .339 H O • V 60 + .146 - - 4. Wood 19 + .460 <.05 5 -.024 - - 5. Metals 12 + .585 <.05 5 -.757 - - 6. Music 79 -.152 - - 32 -.459 <.05 7. Athletics 111 -.121 - - 28 -.386 <.05 8. Business 547 -.100 <.05 411 -.213 <. 01 149 TABLE 5 0 ALL STUDENTS, CORRELATIONS BETWEEN DAT SCORES AND LE (Summary Table) DAT Test Name 1966 (N=602) 1967 (N=411) Clerical Speed and Accuracy -.224 -.197 Spelling -.371 -.394 Mechanical Reasoning -.464 -.394 Verbal/Numerical -.566 -.526 Note: P < .01 in all cases. 150 Ability Groupings The "Mentally Gifted" grouping for 1966 produced an r of +.231 between Clerical and LE (almost significant at the .05 level). The most significant negative r (signifi cant at the .05 level) was between the Verbal/Numerical score and LE. The remaining correlations between LE and DAT were negative and insignificant (see Table 29). For the 1966 "General" grouping (Table 30), the profile shifted downward enough that a negative correlation significant beyond the .01 level existed for all scores except Clerical (-.055). The highest negative correlation was with Verbal, -.456? second was Verbal/Numerical, -.449. The "Basic" grouping (Table 31) exhibited a -.038 correlation between Clerical and LE. The highest negative correlation with LE was Abstract Reasoning (-.377), fol lowed by Mechanical (-.362), and then Spatial (-.352). These three r's were all significant at the .01 level. There was one negligible positive r, Spelling (.016). The Verbal/Numerical correlation dropped to a negligible -.118. For the Class of 1967, the "Mentally Gifted" (Table 32) grouping contained a DAT-LE profile which was different from that of the 1966 grouping in that all DAT-LE correlations were negative and significant beyond the 151 .01 level. As mentioned earlier, this may in part have been due to an error in the identification of student files. However, it was similar in that Clerical had the lowest negative r and the highest negative r was Verbal/ Numerical. The "General" grouping results for 1967 (Table 33) were very similar to the "All" grouping (Table 12) and the "General" grouping for 1966. Clerical again had the lowest r -.098. Results from the "Basic" grouping for 1967 all con tained negative but insignificant correlations with the lowest being "Clerical" and the highest "Verbal/Numerical." Student Files by Rank Order of Learning Efficiency (Tables 19 through 26) These tables utilized all of the student files for which mean LE ratings could be generated. Many of the files contained only partial DAT information. Because of this incompleteness, little confidence was placed in the DAT correlations which resulted. Curricular Groupings Business was the only curricular area with negative correlations which were significant beyond the .01 level 152 between LE and all of the DAT scores for both years of the study. Athletics, Industrial Arts, and Music for the Class of 1966 were the only other curricular areas which had negative correlations beyond the .01 level with all DAT scores. English— Honors and Wood were the only areas of the curriculum which for both years did not contain any corre lations between LE and DAT which were significant beyond the .01 level. Tables 51 and 52 summarize the findings. Summary— Relationships among LE, Test Scores, and Grade Point Average Data In general, significant negative correlations were found to exist between LE and test and Grade Point Average data. 1. Significant negative correlations were found between LE and CTMM. a. The Mentally Gifted groupings showed very low mean LEs while the Basic groupings presented the highest ratings. b. As LE decreased, the negative correlation with CTMM also decreased. TABLE 51 CURRICULAR GROUPINGS— CORRELATIONS BETWEEN LE AND DAT, CLASS OF 1966 Course N Verb Numr Abst Spat Mech Cler Spel Sent V/N 1. Metals 12 + .383 -.254 -.263 -.283 -.109 -.256 -.026 + .271 -.004 2. Wood 19 -.272 -.337 -.524* -.332 -.187 -.403 + .161 + .067 -.363 3. Photography 23 -.264 -.211 -.451* -.630 -.645 + .039 -.243 -.520* -.293 4. Agriculture 17 -.773 -.639 -.448* -.567* -.641 -.024 -.230 -.771 -.739 5. Homemaking 112 -.442 -.414 -.596 -.394 -.395 -.188 -.329 -.467 -.442 6. Crafts 30 -.522 -.242 -.374* -.286 -.410* + . 166 -.380* -.415* -.393 7. Automotive 41 -.686 -.133 -.271 -.162 -.288 -.108 -.183 -.438 -.466 8. English— General 317 -.373 -.176 -.307 -.251 -.335 -.009 -.114* -.322 -.327 9. Music 79 -.652 -.476 -.497 -.547 -.501 -.342 -.296 -.604 -.640 10. Art 52 -.559 -.595 -.506 -.389 -.321* -.328* -.403 -.524 -.622 11. Business 547 -.544 -.464 -.503 -.429 -.461 -.215 -.363 -.509 -.552 12. Industrial Arts 192 -.587 -.458 -.465 -.285 -.383 -.241 -.375 -.433 -.594 13. Athletics 111 -.661 -.651 -.496 -.396 -.573 -.272 -.388 -.476 -.720 14. Science 301 -.581 -.486 -.558 -.466 -.515 -.181* -.350 -.467 -.602 15. Mathematics 403 -.512 -.388 -.409 -.352 -.417 -.125* -.319 -.428 -.504 16. Leadership 28 -.751 -.638 -.688 -.476* -.599 -.531 -.422* -.631 -.726 17. Mech. Drawing 73 -.675 -.504 -.376 -.243* -.303 -.133 -.480 -.578 -.651 18. Foreign Language 261 -.541 -.483 -.478 -.408 -.452 -.108 -.287 -.412 -.591 19. English— CP 228 -.454 -.381 -.374 -.316 -.337 -.041 -.206 -.274 -.488 20. Math— Honors 24 -.539 -.283 -.525 -.126 -.223 -.088 -.136 -.257 -.492* 21. English— Honors 46 -.187 -.263 -.175 -.133 -.174 -.107 -.188 -.177 -.303* Notes: Underlined correlations indicate significance beyond the .01 level. ♦Correlation is significant beyond the .05 level of confidence. Groupings are listed in rank order by mean LE. 153 TABLE 52 CURRICULAR GROUPINGS— CORRELATIONS BETWEEN LE AND DAT, CLASS OF 1967 Course N Verb Numr Abst Spat Mech Cler Spel Sent V/N 1. Agriculture 3 -.852 -.559 -.922 -.891 -.982 -.484 -.636 + .355 -.667 2. Homemaking 44 -.396 -.343* -.481 -.583 -.481 -.163 -.324* -.362* -.401 3. Wood 5 -.710 -.422 -.242 -.474 -.040 -.599 -.432 -.181 -.693 4. Art 9 -.875* -.700* -.739* -.948 -.493 -.758* -.563 -.539 -.827 5. Metals 5 -.853 -.996 -.974 -.942* -.999 -.083 -.843 -.834 -.967 6. Industrial Arts 47 -.639 -.482 -.598 -.653 -.481 -.947 -.499 -.534 -.650 7. Music 32 -.556 -.567 -.554 -.422* -.340 -.372 -.478 -.482 -.638 8. English— General 60 -.573 -.342 -.457 -.405 -.350* -.081 -.295* -.108 -.526 9. Business 411 -.500 -.454 -.484 -.423 -.392 -.197 -.394 -.408 -.526 10. Automotive 12 -.530 -.620* -.602* -.701* -.701* -.362 -.625* -.644* -.653* 11. Athletics 28 -.774 -.741 -.696 -.696 -.475* -.341 -.724 -.725 -.828 12. Leadership 10 -.868 -.776 -.180 -.519 -.390 + .165 -.682* -.550 -.877 13. Mech. Drawing 7 -.742 -.519 -.580 -.404 -.636 + .571 -.224 -.429 -.720 14. Mathematics 144 -.611 -.445 -.443 -.410 -.266 + .056 -.454 -.490 -.616 15. English— CP 69 -.533 -.437 -.468 -.400 -.278* + .163 -.220 -.369 -.608 16. Science 36 -.541 -.539 -.460 -.520 -.358* -.081 -.485 -.397* -.626 17. Foreign Language 66 -.537 -.422 -.353 -.349 -.256* -.062 -.376 -.384 -.560 18. Math— Honors 12 -.725 -.586* -.077 -.496 -.781 -.312 -.405 -.618 -.775 19. English— Honors 16 + .201 + .079 + .370 -.050 -.293 + .374 -.095 + .013 + .197 Notes: Underlined correlations indicate significance beyond the .01 level. *Correlation is significant beyond the .05 level of confidence. Groupings are listed m rank order by mean LE. t—■ (Jl c. Those curricular groupings which contained high CTMM scores tended to have the lower mean student LE ratings. Significant negative correlations were found between LE and STEP achievement data. a. "Reading" and "Mathematics" scores yielded higher negative correlations than "Writing" scores. b. The high ability student groupings contained insignificant negative correlations between LE and STEP as well as the lower ability groupings. c. As LE decreased, the negative correlations with STEP also decreased. d. "Writing" scores generally exhibited the lowest negative correlations with LE when the student files were grouped by subject area. In general, significant negative correlations existed between LE and GPA. These negative corre lations, however, were not nearly so robust as the negative correlations between LE and CTMM, and LE and STEP. a. As LE decreased, the negative correlation with GPA also decreased. b. The curricular groupings English— Honors, English— College Preparation, and English— General contained significant positive correla tions between LE and GPA. The Business group ings contained significant negative correlations between LE and GPA. Significant negative correlations were found among most DAT scores and LE. The tendency was generally that the lowest negative correlations were between LE and "Clerical" followed by "Spelling" and "Mechanical Reasoning." The highest negative correlation was generally with "Verbal/Numerical." a. Pattern relocation was evidenced in that as ability and achievement scores increased, nega tive correlations with LE tended to increase and then decrease. b. Significant negative correlations (P < .01) between LE and DAT were apparent in the Busi ness curricular groupings for both years of the study. 157 Validity of the Negative Relationships among LE and Test and Grade Point Average Data The negative correlations between LE and test data suggested that students with low capability functioned with greater learning efficiency than students with high capability. This finding is in keeping with the propo nents of the achievement quotient (Pintner and Marshall, 1921) who suggested that the negative correlations between IQ and AQ showed that the more intelligent were working below capacity and the dull child was most accelerated. Greene et al. (1954) took a similar position and explained that higher AQ's are more frequently attained by the intel lectually inferior ". . . because of the fact that instructional levels of most schools are geared to the average and inferior pupils and that the curriculum fre quently does not have enough 'top' to adequately interest and motivate superior pupils" (pp. 267-68). The review of the literature pointed out other reasons for a lack of con fidence in the findings of the AQ and thereby cast suspicion on the similar findings of this study. 158 Functions of the LE Mechanism The LE rating was computed from data which was standardized by being converted to T scores. CTMM and STEP test data provided evidence of the ability and educational skill which the student brought with him to the learning situation and the grade which he received represented his performance. The use of grades as the performance criterion imposed limits on the range of possible evaluations. A five point scale was employed (grades A through F) for the Class of 1966 and a four point scale (A through D) was used for the Class of 1967. Students with test data above the "A" parameter were unable to receive LE's of more than 1.00 and those students whose test data placed them near the bottom of the distribution were limited to LE scores of 1.00 or more. Thus it was easier for students with high capability to achieve low LE ratings than it was for them to achieve high LE ratings and vice versa. These artificial controls undoubtedly affected the results and enhanced the negative correlations which were observed. An understanding of the significance of the problem was gained through the identification of the parts of the 159 tails of the LE distributions in which a minimum of confi dence might be placed. Table 53 contains these limits. TABLE 5 3 ARTIFICIAL PARAMETERS FOR LE RANK ORDER LISTINGS Class Grade T Score Per Cent SD 1966 A 61 13.57 F 27 1.07 +2.3 1967 A 62 11.51 -1.2 D 35 6.68 +1.5 Note: Grade to T score conversion was accomplished by consulting the computer generated tables for this data. The use of these limits in examining the findings failed to detract from the trends of the LE course listings. Therefore, confidence was placed in the use of the entire listings to demonstrate curricular differences. It was also found (by inspection) that the variability of LE within the confines of these artificial parameters exceeded that which would be expected without the intervention of the independent variable curriculum. LE Function Verification The DAT correlations were used to independently verify the negative relationships between the test data and LE. This was possible because DAT scores were not employed in the calculation of LE. It was found that "Verbal/Numerical" had the high est negative correlations with LE. This was as expected since the composite score "Verbal/Numerical" serves as an indicator of student ability for purposes of DAT interpre tation. The Relationships of Courses to Learning Efficiency Ratings To discover these relationships, Tables 8 and 9 were inspected. The courses which appeared on both lists more than 1 SD above the mean and contained N's of thirty or more were: 1. English— Grade 10 Basic. 2. Office Service— Attendance. 3. Independent Study— Fine Arts. 4. Choir. 5. Crafts I. 6. Reading I— Developmental. 161 7. Athletics. 8. General Math. 9. Girls' Glee. • o 1 —1 Band. • 1 —1 rH Metals I. Those courses found more than 1 SD below the mean for both years of the study and containing N's of 30 or more were: 1. Spanish 4. 2. Algebra 2. 3. Geometry. 4. Physics— PSSC. 5. Trig. and Solid Geometry. 6. English— Grade 10 Honors. 7. French 2. 8. English— Grade 12 Honors. 9. Spanish 3. 10. Spanish 2. 11. U.S. History— Advanced. 12. Psychology— Advanced. 13. Analytics and Calculus. 14. Geometry— Honors. The courses which had high LE ratings: Tended to be "non-academic" in content. Generally did not have course prerequisites. Tended to utilize activities and action in teaching. Stressed individualized instruction in more than 54 per cent of the courses. Used specialized instructional equipment in 36.3 per cent of the courses. The courses with low LE ratings: Were "academic" in content and methodology. Required that prerequisite courses be completed with a recommending grade. Required the completion of a specified amount of material at a preconceived level of competency. Used specialized equipment for instruction (other than language labs) in 7.1 per cent of the courses. It is interesting to note that: Wilhelms (1968, p. 2) stated that PSSC Physics ". . . isn't exactly a howling success. . ." and that language labs stand idle because teachers do not believe in them or are afraid to use them. 163 2. Young (1967) found that students in college prep programs who also take art, music, homemaking, industrial and business courses do better in col lege than others who pile on academic subjects. He concluded that non-academic electives often are better than academic courses in helping a student develop judgment, independent thought, vocational interest, creativity and other qualities that pro-, duce success in college and in later life. Sex Differences The critical ratio technique was used to ascertain the presence or absence of significant differences between the data which was gathered for boys and girls. Tables 54 and 55 show the results. 1. GPA and LE means were both significantly higher for girls than for boys. 2. There was no significant difference between the CTMM totals (used in the calculation of LE ratings) of boys and girls; however, the CTMM "non-language" mean scores were significantly higher for boys. 164 TABLE 54 SEX DIFFERENCES— CRITICAL RATIO DATA SUMMARY, CLASS OF 1966 Item Group Means Boys Girls C.R. CTMM Language Non-language Total 107 111 108 107 108 107 2.18 .98 < .05 STEP Reading Writing Mathematics 68 64 72 74 75 64 2.94 5.64 4. 81 < .01 < .01 < .01 GPA 2.45 2.70 5.31 < .01 Mean LE 979 1.061 3.31 < .01 DAT Per Cent Verbal Numerical Abstract Spatial Mechanical Clerical Spelling Sentence Verbal/Numerical 59 53 56 58 61 61 61 47 43 56 58 53 56 58 62 60 65 51 45 55 .56 .48 .45 .75 .60 .81 .40 165 TABLE 55 SEX DIFFERENCES— CRITICAL RATIO DATA SUMMARY CLASS OF 1967 Item Group Means Boys Girls C.R. CTMM Language Non-language Total 103 110 107 103 106 105 3.00 1.69 < .01 STEP Reading Writing Mathematics 63 58 70 72 71 64 3.67 5.05 2.35 < .01 < .01 < .05 GPA 2.56 2.76 3.53 < .01 Mean LE 1,143 1.269 2.15 < .05 DAT Per Cent Verbal Numerical Abstract Spatial Mechanical Clerical Spelling Sentence Verbal/Numerical 61 55 58 60 64 58 56 48 43 57 62 59 53 62 66 59 59 50 43 56 .47 1.39 1.68 .78 .83 .37 1.67 .66 .33 166 3. Mean scores in STEP "Reading" and "Writing" were significantly higher for girls and "Mathematics" mean scores were significantly higher for boys. 4. There were no significant differences between the boys' and the girls1 DAT mean scores as these were computed from percentiles derived from tables which use separate norms for boys and girls. Time of Day Effect The sixth question was, "Does time of day appear to have an effect on GPA or LE ratings?" In order to ascertain the influence of "time of day," LE ratings and grades were accumulated for each period of the day. Mean scores were then computed for each. The findings are reported in Table 56. Differences between Students with High and Low Mean LE Ratings To ascertain the differences between students with high and low mean LE ratings, all student files were placed in rank order by mean LE. The listings for each year were grouped by quartile divisions and a data summary was pro duced for each (see tables 19 through 26). An understanding 167 TABLE 56 MEAN LE AND GPA BY PERIODS OP DAY Class of 1966 Class of 1967 Period --------------------- -------------------- N LE GPA N LE GPA 1 6153 1.03 2.37 3863 1.24 2.68 2 6210 1.04 2.33 3881 1.20 2.59 3 6193 1.04 2.35 3868 1.23 2.64 4 6266 1.03 2.34 3931 1.25 2.67 5 6132 1.02 2.31 3880 1.22 2.63 6 6225 1.03 2.41 3876 1.29 2.72 168 of the differences between high and low LE students was achieved through critical ratio analysis of the mean scores of those files which made up the interquartile range (the central 50 per cent of the student files). Tables 57 and 58 summarize the differences between the student files belonging to the upper and lower halves of the interquartile range. The high LE groups contained test data means which were significantly lower (beyond the .01 level) than the means of the low LE groups. There was a lack of significant difference between the mean GPA's of the groups. The DAT profile was also used to ascertain the dif ferences between high and low LE students. The profiles which were compared came from the ability groupings for both years of the study ("Gifted" being low LE and "Basic" having the highest LE) since complete DAT summaries were not available as part of the LE rank order groupings. (See Tables 29 through 34.) The profiles of low LE groupings indicated rela tively equivalent high aptitudes in all areas. The profiles of the groupings which contained the central por tion of the LE's were within the "normal" range of DAT. The "Basic" groupings, which contained the high LE's, were 169 TABLE 57 DIFFERENCES BETWEEN STUDENT FILES FROM THE UPPER AND LOWER HALVES OF THE INTERQUARTILE RANGE CRITICAL RATIO DATA SUMMARY, CLASS OF 1966 Item Group Means C.R. P Upper Lower L.E. 1.075 .899 3.45 < .01 GPA 2.619 2.656 .64 - - . CTMM— Total 104 110 8.82 < .01 STEP— Reading 68 80 2.63 < .01 STEP— Writing 66 76 4.67 < .01 STEP— Math 60 76 8.64 < .01 Note: Data is from Tables 19 and 20. 170 TABLE 58 DIFFERENCES BETWEEN STUDENT FILES FROM THE UPPER AND LOWER HALVES OF THE INTERQUARTILE RANGE CRITICAL RATIO DATA SUMMARY CLASS OF 1967 Item Group Means C.R. P Upper Lower L.E. 1.197 .988 2.26 < .03 GPA 2.675 2.812 1.71 - - CTMM— Total 103 110 7.36 < .01 STEP— Reading 64 79 5.03 < .01 STEP— Writing 61 75 4.73 < .01 STEP— Math 64 79 5.57 < .01 Note: Data is from Tables 23 and 24. 171 found to have profiles with the language and numerical scores very low and the "Spatial," "Mechanical" and "Clerical" scores approaching the normal range. This ten dency was verified by examining high and low LE curricular groupings. Elective Course Effect Question number eight of Chapter I was posed to investigate the notion that the GPA's and LE's of elective * courses might be different from the total cumulative GPA's and LE's. Total and elective course mean LE's and GPA's were computed and entered in each student file. This information was summarized for use in the computer output tables for each grouping. There were not any significant differences between the total and elective groupings. Replication Modification Results After the results from the Class of 1966 were inspected, two procedural modifications were accomplished. One was to discard those student files for which there was not enough data to compute LE ratings. This did not appear 172 to have any effect on the results except to clean up the raw data. The second modification was to delete the "F" grades from the LE ratings. As a result, the total number of available LE ratings was reduced somewhat and the mean LE's and GPA's for the Class of 1967 were raised. This change did not appear to modify the ordering of results in an appreciable manner. Chapter Summary The findings supported the hypothesis that learning efficiency would vary from one course to another in rela tion to the differing uses of materials and techniques. A preponderance of the courses with the higher LE ratings utilized specialized materials and equipment and emphasized individualized instructional techniques. The courses with lower LE ratings were "academic" in nature and generally conventional in their uses of instructional techniques and materials. None of the inde pendent study programs or the courses which were designed to aid the academically less able were found in the lower half of the LE rank order listings of courses. 173 In general, significant negative correlations were found to exist between LE and test data. The groupings with the higher CTMM scores had the lower LE ratings. The correlations between LE and the STEP achievement data demonstrated a lower negative r with "Writing" than with "Reading" or "Mathematics." Significant negative correlations also existed between LE and GPA. As LE decreased, the negative corre lations with GPA also decreased until highly significant positive correlations were found among the high ability groupings. DAT scores were significantly negatively correlated with LE. The tendency was that the lowest negative corre lations were found between LE and "Clerical" and the highest between "Verbal/Numerical" and LE. Courses such as Basic English— Grade 10, Office Service, Independent Study, Choir, Crafts, Developmental Reading, Athletics, and Metal Shop were found to contain the greatest number of high LE ratings. Low LE ratings were encompassed by courses such as second and third year Foreign Language, PSSC Physics, Advanced Mathematics, and Honors English. 174 Evidence of a sex bias was discovered. CTMM "Non language" and STEP "Mathematics" means were both signifi cantly higher for boys. The girls' groupings achieved significantly higher mean scores in STEP "Reading," STEP "Writing," GPA and LE. There did not appear to be any significant differ ences in LE or GPA as a result of grouping on an "elective" versus "total" or "time of day" basis. Student files which exhibited high LE ratings con tained test scores which were significantly lower than the scores contained in the student files which made up the lower LE groupings. The grade data differences were generally insignificant. The problem was delineated in Chapter I. Chapter II consisted of a review of the relevant literature. The third chapter explained the procedures utilized to obtain the findings reported herein. Chapter V which follows summarizes the study and presents conclusions, classroom implications, and recommendations. CHAPTER V SUMMARY, CONCLUSIONS AND RECOMMENDATIONS This chapter begins with a compendium of the prob lem and research procedures. This is followed by conclu sions and recommendations which have implications for the improvement of the secondary school curriculum. Summary of the Problem and Research Procedures The Problem The need for a comprehensive learning theory which could serve as a basis for curriculum planning and research was identified. The following theory was proposed as a functional response to this need: Learning accrues with the concordant involvement of the individual. That is, the more profoundly a student becomes involved in a learning experience, the more efficient his learning will be. This theory served as a basis for the generation of an hypothesis with which to attack the problem. It was hypothesized that a student's learning efficiency (LE) 176 would vary from one course to another in relation to differences in the curriculum and that those learning situations which yielded the highest LE would include the greater amounts of materials and techniques for involving students in the instructional process. Emphasis would be on individualized instruction. Conversely, the courses which utilized a minimum of instructional materials and failed to attend to the unique needs of each student would be found to have relatively lower LE ratings. Verification of this hypothesis required that a procedure be devised for evaluating the normal in-school educational process. Usually an evaluation of the effec tiveness of learning experiences takes place on empirical bases or inferences are obtained from the learner's perform ance data. Consideration is usually not given to the individual's ability and past educational experiences. As a result, the evaluation of normal in-school learning situations has been difficult. More properly, considera tion must be given to the uniqueness of each individual's equipment for learning. Operationally, the problem was to develop a method of rating _learning efficiency as evidenced by a student's performance in light of his ability and past educational 177 experiences. Learning efficiency (LE) ratings had to be developed to provide normalized statistical evidence of student behavior which was directly related to specific areas of the secondary school curriculum. The review of the literature uncovered suggestions for solution of the problem described above. It was divided into two problem areas: (1) the search for a learning theory with comprehensive implications, and (2) the identification of procedures which might as_sist in the evaluation of normal in-school learning experiences. The literature supported the involvement theory and pro vided clues which aided in the development of a method for evaluating the curriculum. An "ordered" hypothesis was suggested to increase the value of the findings. It had to be one which consid ered the total educational process and yet dealt with few major variables. The literature suggested that individual differences in performance, ability, and achievement (edu cational skill) be -the dependent variables, and learning situations should serve as the independent variables. Procedure It was proposed that the results of the California Test of Mental Maturity be used as evidence of ability and 178 that evidence of past educational experiences could be gained from the "Reading," "Writing" and "Mathematics" sec tions of the Sequential Tests of Educational Progress. This data was used in conjunction with the semester grade (performance evidence) to calculate LE. The follow ing model shows the relationships. WB ^ 2 Learning Efficiency = --------------------- I R + W + M \ CTMM (-------- J Pilot Study A pilot study was accomplished in the spring of 1965. The purpose was to ascertain whether there were sig nificant relationships among student course patterns, grades, sex, ability and achievement test data for 384 students who were enrolled in twelfth grade "College Prep" and "Honors" English classes at two comprehensive high schools within the same school district. From an evaluation of the findings, it was con cluded that a unique bit of data was supplied by each measure and that a composite indicator such as the LE rating could be developed. Learning Efficiency Ratings Student records for the Classes of 1966 (N = 1,042). and 1967 (N = 1,073) were processed by the University of Southern California Computer Center. The test and grade data were transmuted into T-scaling prior to use for calculating LE. This procedure also ensured that all of the data was represented as being normally distributed. LE ratings were generated from every semester course grade for which student test data was available. -The iS“ratings"were then grouped by course title. Prom these groupings, a mean LE was computed for each course. The courses were then placed in rank order by mean LE so that the high and low LE courses could be identified. Individual student files were made which contained CTMM, STEP and Differential Aptitude Test results as well as the LE ratings and grades for all of the students1 courses. Student files were grouped on the basis of school attended, sex, ability, student mean LE scores and curricu lar areas. Computer output summary tables were generated for each grouping. Each table contained a statistical description of the grouping and an intercorrelation matrix from Which the LE functions were ascertained. 180 In order to find out if "time of day" had any influence on LE, all LE's were grouped on a period by period basis and a mean LE was computed for each. To discover the "elective course.effect" total mean GPA's and LE's, along with elective course only, GPA's and LE's were reported for each grouping. Detailed results of these procedures were reported in the preceding chapter. Summary of the Findings The following findings were summarized from both years of the study without reference to one another unless a discrepancy existed. In nearly all instances, the Class of 1967 replication supported the findings of the previous year. School Groupings The student files were sorted by junior high school attended and senior high school attended for the generation of six different series of computer output summary tables. It was discovered that this grouping procedure failed to result in significant differences among the six divisions. 181 The Relationships of LE to the Curriculum LE ratings were found to vary in relation to differ ences in the curriculum. A significant pattern of differ ences was found to exist as outlined below. High LE curricular areas were found to include: 1. Industrial Arts. 2. Homemaking. 3. Agriculture. 4. Music. 5. Art. 6. Athletics. 7. Non-college Prep Math. 8. Independent Study and Office Service. The courses which comprised these curricular areas: 1. Tended to be "non-academic" in content. 2. Did not have rigid course prerequisites. 3. Tended to utilize activities and action in teaching. 4. Stressed individualized instruction. 5. Often used specialized instructional equipment. Low LE curricular areas contained "College Prep" and "Honors" courses. These courses: 182 1. Were "academic" in content. 2. Required that prerequisite courses be completed with "recommending" grades. 3. Had as an objective the covering of an arbitrary amount of material. 4. Required that a preconceived level of subject matter competency be achieved by each student. 5. Used a minimum of specialized instructional equip ment. 6. Employed a minimum amount of individualized instruction. The Relationships among CTMM and STEP Test Results and LE 1. CTMM scores were negatively correlated with LE beyond the .01 level of significance. 2. STEP achievement scores were negatively correlated with LE beyond the .01 level of significance. a. The negative achievement correlations were not as high as the relationships between CTMM and LE. b. "Writing" yielded lower negative correlations than "Reading" or "Mathematics." 183 3. The negative correlations with test data decreased as LE decreased. 4. Test data from "high ability" students was found to lack significant negative correlations with LE. The Relationships among DAT Scores and LE 1. Generally, "Clerical Speed and Accuracy" and "Mechanical Reasoning" were found to have the low est negative r with LE and "Verbal/Numerical" had the highest. 2. As ability and achievement scores increased, nega tive correlations with LE tended to increase and then decrease. The Relationships between GPA and LE GPA was generally negatively correlated with LE beyond the .01 level of significance. 1. When student files were grouped on the basis of ability, the 1966 class was found to exhibit sig nificant positive correlations between LE and GPA and the 1967 class displayed no significant correlations. 184 2. The curricular groupings English "Honors,” "College Prep" and "General" contained significant positive correlations between LE and GPA. 3. Student files comprising the "Business" groupings contained significant negative correlations between LE and GPA. Sex Differences 1. Mean LE and GPA results were significantly higher for girls. 2. There was not any significant difference between the mean CTMM totals for boys and girls. However, the CTMM "Non-Language" mean scores were signifi cantly higher for boys. 3. STEP achievement scores in "Reading" and "Writing" were significantly higher for girls and "Mathe matics" mean scores were significantly higher for boys. Time of Day Effect There were not any significant differences in GPA and LE as a result of grouping this data on a period by period basis. Elective Course Effect The calculating of mean LE's and GPA's for elective courses only did not result in a significant improvement over the total mean LE's and GPA's. Differences between Student Files Containing High and Low Mean LE Ratings The mean LE's and test data of the upper and lower groups were found to be significantly different from each other whereas their GPA's were not. DAT scores for low LE groups were relatively high in all areas. High LE group ings tended to have "Spatial," "Mechanical" and "Clerical" aptitude scores approaching the normal range and low "Language" and "Numerical" scores. Conclusions The findings of this study appeared to support the following conclusions about the students and curriculum of the institutions which were investigated. 1. The structure of the curriculum was such that students with high ability did not as a rule learn with the comparative efficiency of students who had lower ability. The findings of this study were that the LE ratings of the 186 high ability students were low; yet, LE was usually corre lated positively and more significantly than test data with GPA, while continuing to be negatively correlated with CTMM and STEP test data. 2. The students who learned with the greatest efficiency were commonly referred to as "over-achievers." . The findings indicated that these were the students whose performance evaluations (grades) were comparable to those of students with higher levels of ability. 3. It appeared that the curriculum enabled girls to learn with greater efficiency than boys as evidenced by their higher GPA’s and LE's. This occurred in spite of there being no differences between the CTMM Total scores of the two groups. Further evidence that the curriculum favored girls was that the CTMM "Non-language" and STEP "Mathematics" scores were significantly higher for boys, whereas the STEP "Reading" and "Writing" scores favored the girls. "Writing" scores were in general not as strongly negatively corre lated with LE as the "Reading" and "Mathematics" scores lending credence to the thought that the curriculum favored girls' verbal ability. 187 4. Those courses which tended to limit learning situations to the conventional uses of language and/or mathematical symbols appeared to support inefficient learn ing and "underachievement." Courses of this nature were generally identified as being "academic" and "college prep." They had low LE ratings even though their enroll ments were made up of students having high capabilities in the use of these symbols. 5. Optimum learning usually occurred in those situations which employed techniques for students to have a variety of experiences about events, objects, relationships and feelings. This function was common to the courses which were identified as having high LE ratings. 6. The DAT summaries of low LE groupings indicated that these students had superior aptitudes, all of which are relatively equal. Nevertheless, the curriculum for these students (traditional College Prep) generally con sisted of learning situations which exploited only a segment of these aptitudes. This provided evidence that students were being only partially involved in learning and was reflected in the low LE ratings which were encountered. 7. The high LE groupings had DAT profiles which were low in "Language" aptitudes and approaching the normal 188 range in other areas such as "Mechanical Reasoning" and "Space Relations." This lack of academic proficiency was generally recognized in that the parts of the curriculum which reflected high LE appeared to make some allowances for this while capitalizing on the limited strengths of the groups. 8. The "time of day" does not appear to have a significant influence on the efficiency with which students learn as LE and GPA distributions were found to be similar throughout the school day. 9. Learning efficiency is unaffected by the fact that a particular course is "an elective." Apparently, this influence is outweighed by the impact of the learning situation at hand. Implications of the Study The results of this study dealing with learning efficiency have implications for everyday classroom activi ties as well as for curriculum planning. For optimum learning efficiency, classroom situa tions should be designed to actively involve the student in appropriate learning experiences. The role of the educator is to assist the student in having these experiences; 189 therefore, educational decisions regarding the effective ness of the learning situation should be based upon evidence of the student's involvement. For example, does each facet of the learning situation enhance the student's involve ment? In planning the curriculum is consideration being given to its appropriateness for a particular student? This study has identified some specific curricular areas which appear to be associated with high learning efficiency. The educator might examine these areas to identify techniques, materials and procedures which have been developed largely on an empirical basis to promote learning efficiency. Many of these "innovations" seem to have come about in response to the increasing numbers of reluctant learners who are required to remain in school. A curriculum which was different from the "academic" appeared to be needed. For example, teachers in curricular areas such as homemaking, industrial arts, and music must individualize their instruction because of the nature of the subject matter. Similarly, the athletic program identifies and builds on the specialized talents of its individual students by pro viding each student with a set of course objectives tailored to his specific needs. 190 The "academic" curriculum on the other hand appears to be dependent upon the use of language and/or mathemati cal symbols as substitutes for the real world. The exclusive use of these symbols denies the student use of the variety of combinations of input which he needs for efficient learning. All students should be given the opportunity to utilize the appropriate senses for learning rather than to be limited to symbolic input. Learning efficiency in the "academic" classrooms can be increased by using the student involvement tech niques which are practiced in many of the "non-academic" classrooms. It is the thesis of this study that student involve ment is the crux of learning efficiency. All of the audio-visual aids, teaching machines, programmed instruc tion packages, programs of computer assisted instruction, multi-media devices, gaming simulations, educational television, instructional equipment and machines, and other teaching aids all are directed toward the same goal— they help to create learning situations which enhance student involvement. They make learning easier and more enjoyable by presenting what is to be learned in a more meaningful manner. 191 High LE curricular areas were found to employ individualized instruction. In addition, independent study and office service enrollments were found among the high LE groupings. Teachers involved in situations such as these prescribed for specific needs of each individual. This reduces the irrelevant and redundant which often dampens learning efficiency and brings into sharp focus the prob lems at hand. Recommendations The following specific recommendations were derived as a result of the findings and conclusions of this study. They are divided into three general classifications: Classroom Recommendations, Curriculum Development Recom mendations, and Research Recommendations. Classroom Recommendations 1. Differentiate instruction through a variety of individualized activities. 2. Help students to select their own learning experiences. 3. Relate what is to be learned to the goals and purposes of the student and then plan learning situations which will move him toward his goals. 192 4. Create learning situations in which the learner must do something with what he is trying to learn. Strive to achieve active participation. 5. Provide for fuller utilization of the students' senses. Make actions adequate to the learning situations. 6. Design learning situations which appear to require a minimum of unnecessary effort. 7. Plan success experiences for students— experiences which will prove satisfying. 8. Base decisions about learning situations on the theory that the efficiency with which a student learns increases or decreases with his level of involvement. Curriculum Development Recommendations 1. Place increased emphasis on recognition of the individual student's learning as being separate and different from that of the others in his class. Flexible schedules which provide for large and small group instruction as well as one-to-one relationships should be employed to this end. Design learning situations which will enable the student to function successfully at his own level. Instructional goals must be realistic in terms of achievability and involvability. That is, those objectives for which students fail to react favor ably must be modified until their desirability is recognized. Couch the objectives of learning situations (the expected outcomes) in terms which describe how the student will be different or what he will be able to do as a result of the learning experience. These statements must convey to the teacher and the student what is to be accomplished for several reasons: a. To enable the teacher and student to decide the relevance of the experience. b. So that when the desired goal has been reached, the particular learning experience can be -* terminated. c. For purposes of student and program evaluation. Support the teacher1s role as being that of a catalyst for increasing the involvement level of the student. 194 5. Study the instructional and organizational tech niques which are used by those teachers who must by necessity individualize their approaches for appli cation to areas of the curriculum where improved learning efficiency is desired. 6. Encourage the use of the senses for defining the student's real world and decrease the shifting of this responsibility to the written word. (Recog nizing, of course, that he is learning to process and use symbols as a vital part of his education.) 7. Modify the curriculum of the more able students to include those learning experiences which would increasingly exploit the multiplicity of excep tional aptitudes possessed by this group. Research Recommendations This study demonstrated the use of learning effi ciency ratings as a basis for evaluating the secondary school curriculum. Because of the exploratory nature of this research and the apparent disparities in curricular effectiveness which were discovered, the following research is recommended. 195 1. Develop a more sensitive and reliable device to supply evidence of performance as a result of learning. This measure would be used with the learning efficiency model for additional research in unadulterated classroom situations. 2. Make a causal-comparative study of the curricular areas which were identified as supporting both high and low LE. The purpose of this study would be to delineate in detail the identifiable factors which resulted in these differences. 3. Identify "over" and "under" achievers and study the learning situations in which they participate. 4. Study microcosmic learning situations in the laboratory utilizing the involvement theory as a basis for the contriving of specific techniques and procedures to be employed. Chapter Summary This was the last chapter of a study dealing with how the curriculum affects the efficiency with which students learn. It reviewed the problem, provided an overview of the procedures used, summarized the findings, drew conclusions, pointed out educational implications and made recommendations. BIBLIOGRAPHY 196 BIBLIOGRAPHY Allen, Dwight. "Too Much Grade Mysticism." CTA Action, III, No. 5 (November 6, 1964), 2. Allport, Gordon W. "Psychological Models for Guidance." 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Creator Stead, John Henry (author)

Core Title Some Factors In The Secondary School Curriculum Which Affect Student Learning Efficiency

Contributor Digitized by ProQuest (provenance)

Degree Doctor of Education

Degree Program Education

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag Education, general,OAI-PMH Harvest

Language English

Advisor Georgiades, William (committee chair), Michael, William B. (committee member), Pullias, Earl Vivon (committee member)

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c18-373986

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