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Educational productivity is the extent to which learning is increased while minimizing costs. Costs may be considered broadly as including not only total monetary costs but also the diﬃculty of implementing new programs and procedures, possible staﬀ retraining, educational incentives, student time required, and similar considerations, which may lack explicitly estimated monetary costs. Learning can also be considered broadly as including such things as knowledge, understanding, critical thinking, creativity, skills, attitudes, and career success and other long-term adult outcomes. Despite such broad concerns, most empirical research on educational productivity has focused on achievement on standardized tests of knowledge and cognitive abilities related to speciﬁc skills and subjects such as reading, mathematics, and science in primary and secondary education. Although the monetary beneﬁts, costs, and their relations are diﬃcult to estimate, much psychological research identiﬁes on educational practices and conditions that increase learning without necessarily increasing costs.
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1. Economic Perspectives
Interest in educational productivity accelerated in the last few decades as policymakers, scholars, and educators realized more fully, the contribution of human, social, and cultural capital to economic growth and individual and national wellbeing—an insight that may be traced to Adam Smith’s 1776 Wealth of Nations and to Aristotle and other thinkers in Western and non-Western civilizations. Indeed, parents and educators have long sought the best means of raising and educating children. Today, moreover, civic leaders and citizens more clearly recognize that in addition to physical assets such as farms, industrial plants, machinery, and inventions, the abilities of individuals and their capacity to work together partly determine their wellbeing and future prospects. In addition, it is in the interests of nations and individuals to have productive or eﬃcient education that conserves public funds, tuition, and students’ time, which can be used for other purposes, including attaining more advanced education, starting careers and pursuing the other aspects of their lives.
The late sociologist James S. Coleman precipitated much interest in educational productivity by suggesting, on the basis of one of the largest student surveys ever undertaken, that learning does not depend on the amount of money spent on schools. This research paper sets forth the factors on which learning does depend, as revealed by psychological research of the last few decades of the twentieth century.
Productivity research in most industries lies in the province of economists who identify and measure activities that maximize beneﬁts and minimize costs. Yet, it has proven diﬃcult to estimate the monetary beneﬁts and costs of education. Economists can estimate wealth and income, but the values of other qualities of life, such as citizenship, creativity, and capacity for teamwork, are diﬃcult to evaluate. Policymakers and educators often have little grasp of the total and component costs of schooling and their bearing on the production of learning. They seldom estimate, for example, the monetary value of parent and student time contributed to the leaning process. Even the often-observed relation of the number of years of education and adult income cannot be unequivocally attributed to schooling, since both may partly be attributable to children’s abilities and their parents’ socioeconomic status and child-rearing practices.
Leading economists, moreover, have compiled a great deal of research on the eﬀects of total and component expenditures on education (see Hanushek 1994). Their analyses show that beyond minimal levels, higher expenditures on teachers’ years of experiences and advanced university degrees, physical facilities, smaller class sizes, and other items for which costs can be estimated have little or no inﬂuence on how much students learn.
For several reasons, educational productivity research and concerns have been concentrated in the USA. International achievement surveys in mathematics, science, reading, and other subjects show that the longer US students are in school, the more they fall behind those in other countries. Near the end of secondary school, they score last among the economically advanced Asian and European countries. Because they fall behind while in primary and secondary schools, the problem appears to be more attributable to schooling than to families and society. Even so, the per-student costs (adjusted for purchasing power) of schooling are higher in the USA than most other economically advanced countries. Finally, unlike most industries in the past few decades, the productivity of US schooling has declined: Inﬂation-adjusted costs have risen sharply, while students have made similar or smaller gains in achievement. Such analyses of productivity indicators across time and among nations have provoked much discussion among scholars, legislators, and citizens.
2. Research On Psychological Productivity Factors
Since economists have shown little relation between costs and quality, it would seem useful to focus on educational technology, that is, instructional programs, methods, and conditions. Here progress is apparent. Psychologists and educational researchers have measured the chief psychological variables in the education process and discovered their causal relations. Given such information, policymakers and educators can better analyze schooling to make it more productive.
The nine factors, shown in Table 1, have strong, consistent inﬂuences on how much students learn within and outside school. Potent, consistent, and widely applicable to many education settings, these nine psychological factors fall into the three groups— student aptitude, instruction, and environment. From two research traditions, considerable research corroborates the inﬂuence of these nine factors on learning. ‘Meta-analyses’ (statistical syntheses of multiple studies) shows that some methods of instruction produce far greater learning than others. From meta-analyses, Walberg and Lai (1999) compiled sizes of eﬀects of 275 educational methods and conditions on the amount students learn. These eﬀects are based on thousands of comparisons of experimental and control groups. Along with cost, feasibility, philosophy, and other considerations, educators can use the ‘eﬀects’ sizes as criteria in designing and evaluating teacher education and school programs.
A second basis for the factors draws upon the social science tradition of ‘secondary analysis,’ in which large local, national, or international surveys are statistically analyzed to detect the size of simultaneous eﬀects of multiple causes on outcomes. Analyses of several dozen surveys allowed productivity analysts to estimate the relative eﬀects of the factors in Table 1, including those that are diﬃcult to manipulate merely for the sake of research.
These two research traditions strengthen conﬁdence in the productive eﬃcacy of the factors and complement one another. More often employed by psychologists, experiments employing random assignment to treatment and control groups allow strong inferences about causality but, because of costs and other constraints, they are often conducted with small groups of students in homogeneous conditions. Meta-analysis allows inferences from a broad base of many studies and can yield estimates of possible diﬀerential eﬃcacy for educational conditions and types of students. Secondary analysts employ statistical rather than experimental control, which may be less telling of causality but often allows wider generality because samples are drawn randomly from nations or even groups of nations. The most conﬁdent conclusions come from methods and conditions that yield similar results for many kinds of students in widely varying settings, employing both research methods.
An additional basis for the productivity factors is the biographical study of the highest achievers, secondary school students qualiﬁed to compete in the international Mathematical Olympiad from China, Japan, Taiwan, and the USA, and eminent men and women from a variety of ﬁelds. Since research showed that the nine factors each promote achievement, it was hypothesized that the very highest levels of accomplishment require concentrating and optimizing all nine factors to attain peak performance in mathematical problem-solving ability and in preparation for outstanding careers. For top mathematics performance, data collected on biographical questionnaires supported this hypothesis.
To test the hypothesis with respect to adult eminence, a sample of pre-twentieth-century eminent men was drawn, based on the length of their biographies in American and European encyclopedias. A sample of twentieth-century eminent women was also identiﬁed by their outstanding contributions to such ﬁelds as art, music, science, writing, politics, and religion. Ratings made by biographers and ratings made by content analysts from biographies conﬁrmed the hypothesis. Many eminent people, while not necessarily highly successful in school, persevered through diﬃculties and concentrated on their chosen ﬁelds, even in the ﬁrst 13 years of life. Parents, teachers, and others identiﬁed their special talents, encouraged them, and provided opportunities for them to develop to their fullest. The general corroboration of the determining eﬀects of the productivity factors was particularly important in these samples, because nearly all other research focused on academic achievement in school, rather than adult accomplishments.
3. Speciﬁc Eﬀects Of Productivity Factors
The economic idea of scarcity can promote understanding of how the factors aﬀect learning. The ﬁrst two sets of ﬁve factors in Table 1 may be termed essential, since without minimal levels of each, students are unlikely to learn. For example, if they are completely unmotivated, or given no time to learn, or taught in a language completely foreign to them, they learn little. These essential factors, moreover, appear to substitute, compensate, or trade-oﬀ one another, possibly at diminishing rates of return. Immense quantities of time, for example, may be required for a moderate amount of learning, if motivation, ability, or instructional quality is minimal. Thus, no single essential factor overwhelms the others; all appear important. In addition, concentration in one ﬁeld makes high accomplishment in another more diﬃcult; the best chess player is unlikely to be best at physics or drawing.
Although the environment factors in Table 1 are consistent statistically or experimentally-controlled inﬂuences on learning, they may directly supplement as well as indirectly inﬂuence the essential classroom factors. In either case, the powerful inﬂuences of out-of-school factors, especially the home environment must be considered. For example, if students spend 12 years of 180 6-hour days in elementary and secondary school, they will have only spent about 13 percent of the waking, potentially educative time during the ﬁrst 18 years of life in school. If more of the 87 percent of the student’s waking time nominally under the control of parents, that is spent outside school, were to be spent exposed to academically-stimulating media and conditions in the homes and peer groups, then students might learn much more.
The strong inﬂuences of ability, motivation, and age are largely reﬂected in achievement tests in the academic subjects. Achievement at any point in time also reﬂects the previous home environment, exposure to mass media, and amount and quality of instruction. Current achievement predicts subsequent achievement, and the older the student, the better the prediction. High achievers in secondary school science, for example, can be expected to achieve well in science, engineering, medicine, and other university courses and programs with a heavy science content. Although ‘late bloomers’ can be identiﬁed, they are rare, especially in the more cognitively demanding ﬁelds; hence, the importance of a good early start. Another consequence of the cumulative nature of achievement is the need to consider ‘value added’ indicators of school productivity. Schools, for example, with relatively poorly prepared students, who nonetheless make good gains, should be given credit even though their end results may only be average.
Quantity of instruction or engaged learning time, along with the other factors, can be viewed as a scarce ingredient in learning. Quality of instruction can be abstractly understood as providing encouragement, information, questioning, and other stimuli to ensure the fruitfulness of engaged time. Diagnosis and tutoring can help ensure that instruction is suitable to the individual student. Inspired teaching can enhance motivation to keep students persevering. Quality of instruction, then, may be considered as an eﬃcient enhancer of learning time. Some methods are more eﬀective than others, though not necessarily widely employed. Expensive reductions in class sizes, for example, have led to highly inconsistent and debatable achievement results. Some methods of teaching, however, such as direct instruction, have excellent research records and do not necessarily cost extra money, since many teachers can learn these individually, and others during routine staﬀ development. Still other methods are traditional and should always be included in teachers’ repertoires.
The four psychological environments deﬁned in Table 1 can enlarge and enhance learning time. Good classroom morale may reﬂect match of the lesson to student aptitude, the stimulating properties of the academic group, or, in general, the degree to which students concentrate on learning, rather than diverting their energies, because of unconstructive social climates. Peer groups outside school and stimulating home environments provided by parents can help by enlarging learning time and enhancing its eﬃciency; students can both learn in these environments and become more adaptable to formal schooling.
Because students spend so much time outside school, their parents play decisive roles in how well they do inside school. In addition to encouraging and supervising homework, parents can improve academic conditions at home. What might be called ‘the alterable curriculum of the home’ is much more predictive of academic learning than is family socioeconomic status. This curriculum refers to informed parent– child conversations about school and everyday events; encouragement and discussion of leisure reading; monitoring, discussion, and guidance of television viewing and peer activities; deferral of immediate gratiﬁcation to accomplish long-term goals; expressions of aﬀection and interest in the child’s academic and other progress as a person; and perhaps, among such careful eﬀorts, caprice.
Turning to the last and only negative factor, leisure time media exposure including television (although many television programs can be educational) and radio, can displace homework, reading, and other educationally stimulating activities. They may also dull the student’s keenness for academic work. Some of the average of 28 hours a week spent viewing television by US secondary school students might usefully be added to the mere four or ﬁve weekly hours of reported homework (Walberg 1984). Ten of those hours a week for several years could be suﬃcient to learn tennis, a foreign language or a specialized science.
Many experimental studies and analyses of survey data show the pervasive and fairly uniform eﬀect of the productivity factors in the usual school subjects and on students of diﬀerent ages, countries, and cultural groups. One recent study, for example, concerned the possible diﬀerential eﬀect of the factors on mathematics achievement for African-American and other, mostly white students. The question was asked if some factors have greater or lesser eﬀects on the African-American than on the other students. The longitudinal analysis of mathematics scores of a national sample of more than 10,000 students showed equal eﬀects of the factors on African-American and other students. Thus, for example, more study time, better instruction, and a more stimulating home environment had equal eﬀects on how much African-American and other students learned, and how favorably their attitudes toward subject matter changed.
4. Current And Future Initiatives
Even though educational productivity research is extensive, policymakers and practitioners seem to have made only partial use of it. Part of the problem could be an information gap. For this reason, the International Academy of Education, an honorary scholarly group headquartered in Brussels, Belgium, began a series of pamphlets on ‘best practices.’ In Geneva, Switzerland, the International Bureau of Education, a part of UNESCO, distributes about 4,000 copies of the series to education oﬃcials throughout the world, who are free to reproduce the booklets. The booklets are also available on the Internet pages of the two organizations, and educators may download them for reproduction.
The barriers to implementing well established achievement-raising practices, however, may not be completely attributable to a lack of information but to failures in (a) setting achievement as schools’ top priority, (b) choosing the most appropriate procedures to attain speciﬁed achievement goals, (c) specifying system-wide procedural plans and milestones, and (d) monitoring progress, particularly with respect to procedural and achievement goals. Because school systems have been slow to accomplish these things, policymakers are making radical changes to provide incentives for putting eﬀective methods in place. These include closing failing schools, ﬁnancially supporting public school students whose families want to send them to private schools, and giving authority for school governance to mayors and parents, rather than educators. At the same time, public and private education providers are experimenting with curriculum and instruction that can be provided through the Internet, and evaluating other technologies that may provide more convenient and productive education. Although it is diﬃcult to say where all this will lead, it does appear certain that attention to and research on the problem of educational productivity, will continue to expand.
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