CHAPTER 3

FAMILY AND SCHOOL STRUCTURAL CHARACTERISTICS

Introduction

Family and school environments may be considered to consist of sets of conditions that affect children’s life chances. Such conditions might be designated as family and school opportunity structures. As Starr (1993, p. 1336) suggests “Environments provide a range of opportunities for development; the same environments do not have the same effects on all individuals, because individuals construct different experiences from the same environmental opportunities.” Blau (1990, p. 150) proposes that the central subject- matter of sociology “is the study of these structural constraints limiting the opportunities of realizing choices for many while expanding them for some.” In this chapter some of the structural characteristics that define children’s learning environments or opportunity structures are examined, such as family sibling structure, parental presence and family disruption, ability grouping, and the nature of instruction, curriculum, and teachers’ knowledge in schools. It is realized, of course, that membership of social categories with relatively clear boundaries such as racial and ethnic groups, is a major factor in determining family and school opportunity structures. Children, parents, and teachers in their everyday interactions recognize and confront the barriers that are established by their membership of social groups and as a result, family and school learning contexts are influenced by that recognition and confrontation. Therefore, in a number of studies that are presented in this chapter, relationships are investigated between structural characteristics and outcomes for students from different social status and ethnic groups.

Family Structural Characteristics

Sibling Constellation Variables

Sibling constellation variables such as sibsize (the number of children in a family) and the birth order of children within families have been used in many studies as indicators of children’s family learning environments. Quite often it has been shown that these family structural variables have modest inverse associations with measures of children’s cognitive performance, especially verbal measures of achievement. In the following section of the chapter, three conceptual orientations that have been

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used to examine relations between sibling variables and children’s school outcomes are considered. The orientations are: the resource dilution hypothesis, the confluence model, and the admixture hypothesis.

The resource dilution hypothesis

The resource dilution hypothesis proposes that sibling variables are related to the cultural and material resources that parents provide for their children. It is then suggested that such variations in the amount of famiiy educational resources provided for different siblings are related to variations in children’s learning outcomes. Steelman and Powell (1989, p. 844) suggest that although the hypothesis “frequently is used as

an ad hoc explanation for the deleterious impact of sibship size on a wide range of educational phenomena, it seldom is tested directly.” In an analysis of the hypothesis, Steelman and Powell (1991, p. 1505) examined parents’ economic investment in their children, and they indicate that “Parents* reported willingness and ability to pay, along with savings for children’s future education, are shaped first by total income and the number of children who must share that income.” They conclude:

The strengths of the resource dilution hypothesis lie in its recognition of the theoretical merit of sibship size and its specification of the mechanisms by which sibship size renders its effect. Yet few studies have directly tested the hypothesis. Our study corroborates the size/dilution principle with respect to economic resources. However, it also raises other issues complicating this seemingly straightforward hypothesis. For example, we need to identify the relative effects of economic, social. and interactional resources during the developing child’s life span and to see whether these effects cumulate. We also need to ascertain whether youths deprived of resources suffer irreversible damage and whether there are critical junctures at which children more profoundly require certain types of resources (p. 1527).

In another investigation of the hypothesis, I examined relationships between the number of children in families, birth order, and measures of children’s family educational resources (~arjoribanks, 199la). Also, I considered that associations between sibling structure and family-resource measures might vary for children from

different social status backgrounds. Therefore, relations were examined between sibling variables and children’s family educational resources at different levels of family social status. For the analysis, data were collected from 900 Il-year-old Australian children (4.50 boys, 450 girls) and their parents. Information relating to family social status, family sibling structure and the educational resources of families were obtained from interviews with the parents in their homes.

A typology defined by Merton (1968, 1976) to investigate relations between social structure and individual behavior was adopted as a framework for examining the educational resources provided by parents for their children. One of the dimensions of the typology includes the culturally defined goals that are considered as legitimate objectives for members of the society. These prevailing goals comprise a frame of aspirational reference. The second typological dimension is defined by the means that are used to achieve the goals identified in the first dimension. In the study, parents’

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educational and occupational aspirations for their children were considered to reflect a set of family goals. Family activities and orientations that have been shown in prior research to be related to children’s learning outcomes were used to define the means that parents adopted to achieve their aspirations.

A schedule, in the form of a semistructured parent-interview inventory, was constructed to assess family educational resources (also see Marjoribanks, 1993). The schedule which has been designated as the Marjoribanks Family Environment Schedule is provided in Appendix A, for use by other researchers. As Keeves, Morgenstern and Saha (1991, p. 63) indicate “In spite of a substantial body of research there are, however, no commonly agreed measures on the sociocultural level of the home that are available for use in educational research” (also see Greene & Plank, 1994). The presentation of the Schedule may assist in overcoming that restriction of existing research. In the investigation, family factor scales were constructed and labeled as parents’ aspirations and parents’ press for: achievement, individualism, independence, and interaction with children. (The concept of environmental press is discussed more fully in the following chapter.) Parents’ aspirations were gauged by questions such as, “How much education would you really like your child to receive if at all possible?” and “What kind of job would you really like your child to have when she/he grows up, if at all possible?” Press for achievement was assessed by items of the form, “Would you know what topic your child is studying in say arithmetic or English, at school?” and “When your child was small, before she/he started school, did you ever read to her/him?” In the press for individualism scale, parents were asked to react to statements such as, “When the time comes for children to take jobs, they should try and stay near their parents, even if it means giving up good opportunities.” In the press for independence scale there were questions that asked parents to indicate the age they would allow their children to undertake certain activities. Parents’ press for interaction with their children was assessed by items of the form, “How often do you praise your child for work at school?” and “How often do you help your child now with reading?”

Relationships among the sibling variables, family social status, intellectual ability and family educational resources were examined by plotting regression surfaces generated from hierarchical regression models. In the remaining chapters of this review, regression surfaces are used to examine other sets of relationships. Such surfaces are graphical representations of the joint relations of two independent variables with an outcome or dependent variable.

In the models, product terms are included to test for possible interaction effects and squared terms are added to investigate possible curvilinear relationships. That is, the regression models are of the form:

Z=constant + aX + bY + cX.Y + dx;? + eY’J

In this study, Z, X, and Y represented measures of family educational resources, sibling variables, and family social status, respectively. To reduce multicollinearity in the regression models, each variable was transformed by subtracting means from the raw scores of the relevant measures (see Marjoribanks, 1994a). Also, if the addition of interaction or squared terms was not associated with a significant increment in the amount of variance in outcome scores, then the terms were deleted from the particular model.

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GIRLS' SURFACE BOYS' SURFACE

PARmaTS' ASPIRATIONS

~~ OF ~ILDREN SKIAt STATUS

Figure 3.1. Fitted-parents’ aspiration scores in relation to the number of children in families and family social status.

In Figure 3.1, the regression-fitted relations among family social status, the number of children in families, and parents’ aspirations for their children, are shown. Indeed, the surfaces reflect the possible complexity of the relationships among the variables. At each social status level, the surface for boys reveals that the number of children in families has a curvilinear association with parents’ aspirations, such that increases in the number of children are related to decreases in parents’ aspirations. That is, the surface for boys provides tentative support for the sibling resource dilution hypothesis. The surface for girls indicates the presence of significant interaction and curvilinear associations among the variables. At low social status values, the number of children in families has a significant curvilinear association with parents’ aspirations. In contrast, at high social status levels there is no significant relationship between parents’ aspirations and the number of children in families. These findings suggest that the sibling resource dilution hypothesis is supported for girls from low social status families but not for girls from high social status families.

Although the results from the other regression models provided some support for the

sibling resource dilution hypothesis, the investigation suggests that the non-rejection of the hypothesis must be treated with caution. The hypothesis appeared to be supported,

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for example, at some social status levels but not at others. Also, for some of the measures of family educational resources, particularly press for independence and parents’ interaction with children, there was no support for the theoretical position. The analysis suggests, therefore, that there may be a particular set of family educational resources that fits the predictions of the resource dilution hypothesis.

Due to the complexity of family learning environments it is not surprising that findings from sibling research are inconclusive and quite often contradictory. One factor that makes the interpretation of sibling research difficult is the effect that age spacing between siblings has on children’s learning environments. Powell and Steelman (1993, p. 380) suggest, for example, that “research should more clearly delineate the dimensions of resource allocation and the link between resource allocation and spacing of siblings. ”

The difficulties in attempting to account for sibling differences in children’s learning outcomes and the significance of age spacing between siblings led, in part, to the development of the confluence model of sibling effects.

The co@uence model

One of the most significant social-psychological models used to examine relationships between sibling variables and cognitive growth is the confluence model developed by Zajonc and Markus (1975). They propose that the cognitive development of individuals in any given period is related to the intellectual quality of family environments where quality is determined by influences such as the number of siblings in families, the age spacing among siblings, and whether individuals are only or last children in families (also see Zajonc, 1976; Markus & Zajonc, 1977; Zajonc & Bargh, 1980). As Berbaum and Moreland (1985, p. 207) indicate, the model implies that the “intellectual environment is determined by the average intellectual ability of all family members, including the child who is being studied. A family’s intellectual environment is never stable, but instead changes are produced by alterations in family structure. The arrival of infants and the departure of adults, for example, can worsen a family’s intellectual development by lowering the average intellectual ability of its members. Similarly, the arrival of adults can improve a family’s intellectual environment by raising the average intellectual ability of its members. The maturation of children within a family can also produce changes in its intellectual environment. As children mature, their intellectual abilities improve and they contribute more to their family’s intellectual environment.”

The confluence model has generated vigorous controversy. McCall (1985, p. 217) suggests, for example, that “Further research, I feel, should distinguish between validating confluence theory - the psychological assumptions and principles proposed (e.g., the birth of a child dilutes the intellectual climate of the home and retards the intellectual development of an older sibling) - and validating the mathematical model.” It is proposed further by McCall that “the confluence model should predict accurately and efficiently, where efficiently means that the level of prediction is higher to a worthwhile extent than can be achieved with simpler procedures or with other models. In this respect, I think the confluence model is in trouble” (p. 218). He concludes, “It is my feeling that too much attention has been paid to validating the model and not

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enough effort has been devoted to testing specific theoretical propositions directly, within individuals, within families, and with longitudinal data” (p. 218).

In one test, Retherford and Sewell (1991) examined the mathematical form of the confluence model using aggregate data. between-family data, and within-family data from the Wisconsin Longitudinal Study. From their analysis they conclude that the confluence theory “despite its ingenuity and intuitive appeal to many social scientists, does not hold up under careful scrutiny. It may even be a theory that attempts to explain a social phenomenon that does not exist” (p. 156). Zajonc et al. (1991, p. 159) respond, however. that “These criticisms are shown to be either irrelevant, erroneous, or both. Retherford and Sewell further conclude that birth-order effects may be ‘a social phenomenon that does not exist’. We disagree. Far from contradicting the confluence model. the Wisconsin data lend further support to it.” Zajonc, Markus, Berbaum, Bargh and Moreland (1991) repeat an earlier conclusion from the confluence model

that:

Any apparent birth order effects found in a set of data are hypothesized to be artifactual in that they may be explained solely by family size and the spacing of births. With short birth intervals, increasing order of birth will be associated with lower intelligence levels. With sufficiently long gaps, however. this pattern may be mitigated or even reversed, provided the new child is born at a time when the average intellectual level of the family is greater than that when the earlier siblings were born

(p. 164).

Retherford and Sewell (1992) react to the reply from Zajonc ef ul. (1991) suggesting that their assertions are seriously in error. Such debates about the confluence theory and model will no doubt continue. The criticisms of the confluence explanation have led to an alternative sibling model being proposed which has been labeled as the admixture

hypothesis of sibling relationships.

The rrdmixture hypothesis

The admixture hypothesis suggests that sibling variables may not be as powerful predictors of outcome measures as suggested by the confluence model. Instead, it is proposed that the significant associations found between sibling variables and cognitive outcomes in many investigations may merely be an artifact of the pooling of subpopulations within those studies. If relationships between sibling variables and

outcome measures are examined in samples that are divided, for example, into social status and ethnic groups, then the admixture hypothesis suggests that the size of the sibling associations is reduced substantially.

In a test of the admixture model, I used longitudinal data to examine relations between sibling variables and measures of educational and occupational aspirations at different levels of parents’ aspirations, family social status and children’s intellectual ability, for adolescents from different ethnic groups (Marjoribanks, 1988b). Aspirations were chosen as the outcome measure as they have been shown in many social-arithmetic studies to mediate, substantially, the impact of cognitive and environmental variables on adolescents’ eventual educational and occupational attainment.

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Data were collected initially from 11-year-old Australian children and their parents and then collected in a follow-up study, 5 years later. Families were either Anglo- Australian, Greek, or Southern Italian. In the initial survey, measures were obtained on family social status, parents’ aspirations, sibling indicators, and the children’s ability. The aspirations of the adolescents were assessed in the follow-up investigation.

Generally, the study indicated that sibling variables had modest associations with adolescents’ educational and occupational aspirations, even after taking into account relationships involving family social status, parents’ aspirations, and children’s intellectual ability. The findings also suggested, however, that there were ethnic group variations in the relations between the sibling variables and adolescents’ aspirations, which provided tentative support for the admixture hypothesis of sibling relationships.

A direction for future sibling constellation research may be to combine analyses of the resource dilution and admixture hypotheses. That is, to test the relations between sibling variables and family resource characteristics for boys and girls from different social status and ethnic groups.

Sibling Variables and Social-Arithmetic Studies

If our understanding of the complexities of children’s learning environments is to be advanced then it is important that we begin to integrate the various approaches to environmental research. In one of the most extensive set of studies of sibling associations with outcome measures, Blake (1989, p. 93) has introduced a consideration of sibsize into social-arithmetic models. Blake notes that “although the explanatory model is very similar to the one used by the classic status-attainment studies, this research focuses on the influence of a family structure variable whose independent effects have been largely ignored in research on educational opportunity to date - the number of siblings in the respondent’s family of orientation.” A number of national U.S. samples are used by Blake to investigate status-attainment models that include sibsize and she concludes:

young people from higher-status families and small sibsizes appear to have higher educational goals primarily because these influences operate on ability and grades and thence through parental expectations. Regardless of how the data on parents’ expectations are obtained, or whether these data pertain to seniors or younger children, parents’ expectations are the most important influence on youngsters’ expectations. These results tend to confirm past research using the Wisconsin model, except that they add considerable detail concerning the relative importance of sibsize compared with other family background variables (p. 222).

Overall, Blake (1989) suggests:

the analysis has uncovered numerous informational gaps in our knowledge of the causes and consequences of sibsize outcomes. Very few data are available on cognitively relevant settings and treatments of children in different-size families - time-use information on the children, measures of parental interaction with children (such as time spent reading to children or playing cognitively relevant games). It would also be helpful to know whether there are sibsize differences in the perception of education as an investment or as an end in itself and how such differences as may

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exist relate to the child’s tolerance for long periods of schooling. This list could be multiplied many times. The fact is that we have not begun to study systematically how intrafamilial dynamics influence cognitive and educational outcomes in young people (p. 304).

Future Sibling Constellation Research

If our interpretations of the associations between family structural characteristics and learning outcomes is to be enriched then further analyses of sibling relationships are required that use alternative theoretical orientations, include other measures of family influences, examine relationships within different social groups, and test for the possible complexity of the relationships between sibling variables and different measures of students’ school-related outcomes. Such research should also consider sibling relationships in cross-cultural contexts. As Cicirelli (1994, p. 17) indicates “In industrialized societies, sibling structure has an influence on sibling relationships, but in non-industrialized societies sibling structure variables are the criteria for establishing normative relationships between siblings, and roles established by birth order and gender.” He proposes that industrialized societies have much to learn from non- industrialized societies about the processes of sibling teaching and caretaking as part of family socialization.

The research that has been presented in this section of the chapter indicates that sibling variables are indicators of significant interactions that take place in children’s family learning environments. A challenge for future sibling research is to develop means of capturing more clearly how such variables constrain or promote life chances for children from different social groups.

Sibling variables are, however, only one measure of family structure. Increasingly, relationships are being examined between the presence of parents in families, family disruption, and children’s learning outcomes. In the following section some of that

research is reviewed.

Parental Presence and Family Disruption

In an investigation involving one-parent households, Mulkey, Crain and Harrington (1992, p. 48) indicate that “Research shows that students who live in one-parent households are disadvantaged on many counts. However, there is not much agreement as to why they are. Some researchers have argued that the effects of one-parent families are largely explained by educational disadvantage or by the low incomes of father-absent families - in other words, there is nothing wrong with a one-parent household that a woman making a decent wage could not cure.” They go on to suggest that “Others claim the opposite - that children are harmed by psychological stress and incomplete socialization, even in affluent one-parent families. They view the difficulties as more emotional than cognitive, resulting less from the family’s lack of educational resources and more from its difficulty in managing the child’s behavior” (p. 48).

Similarly, Astone and McLanahan (1991, p. 309) indicate that “Numerous studies have shown that children who grow up in single-parent families are less likely to

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complete high school or to attend college than children who grow up with both parents. One reason children from single-parent families are less likely to finish high school is the precarious economic position of their families.” In an investigation of family structure they conclude that “Children who live with single parents or stepparents during adolescence receive less encouragement and less help with school work than children who live with both natural parents, and parental involvement has positive effects on children’s school achievement. Differences in parental behavior, however, account for little of the difference in educational attainment between children from intact and nonintact families” (p. 309). They observe that “A key finding is that growing up in a stepparent family has similar negative consequences on the educational attainment process as growing up in a single-parent family” (p. 319). Astone and McLanahan link their findings about stepparents to Coleman’s theory of social capital and they propose that “a child may live with two parents, but her or his access to social capital depends on (1) the willingness and ability of the parents to provide the child with time and attention, and (2) the receptivity of the child to parental and stepparental overtures” (p. 319).

In a further analysis of stepparents, Furstenberg (1990, p. 396) suggests:

The ambiguity of family norms may help explain why bonds between stepparents and their children are weaker and sometimes fraught with conflict. Few children come to view their stepparents as indistinguishable from a biological parent, and few parents treat their stepchildren in ways identical to those with their biological offspring. Consequently it is not surprising that ties to stepparents are described as less close than ties to biological parents, and relations in stepfamilies are generally somewhat less harmonious and gratifying.

Furstenberg (1990, p. 396) goes on to propose that “While studies on the long-term effects of remarriage on children are sparse, there is little evidence to suggest that remarriage enhances the psychological well-being of children even when it improves their economic circumstances.” Similarly, Biblarz and Raferty (1993, p. 99) suggest that in traditional social mobility research it is assumed that children receive “whatever the household has to offer - if not from the father, then from whomever is the household head. However, when the custodial household head is the mother, stepfather, or other adult, we would expect a more problematic and less efficient intergenerational transmission process compared with that in traditional families.”

While marital disruption may have an impact on children’s learning, research suggests also that sibling structure is related to marital disruption. Morgan, Lye and Condran (1988, p. 123) propose, for example, that “women who have daughters are more likely to experience marital disruption than those who have sons. Likewise, daughters are more likely than sons to experience the disruption of their parents’ marriages.” They conclude that “Our finding of differential rates of disruption by sex of children provides indirect support for the overall theory that children provide a new basis for marital stability built on parents’ involvement with and investments in children. All children increase stability, but sons promote greater stability than daughters because they elicit a greater investment and involvement from fathers” (p. 124). Also, Waite and Lillard (1991, p. 930) indicate that “Children hold a unique position in a marital relationship: they belong to the partnership rather than to either of the individuals. For this reason, children, constitute the prime example of ‘marital-specific capital’, a resource worth

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substantially less outside a particular relationship than in it.” Such analyses indicate the need to examine the reciprocal effects between family structure and marital disruption.

The stability of intact families is related also to an often replicated finding that women who grow up in nonintact families are more likely to bear children before marrying. Wu and Martison (1993, p. 210) suggest that there are three common explanations for the findings: (a) a childhood socialization hypothesis which suggests “that women who grow up in a mother-only family during early childhood are socialized in ways that produce a

high risk of premarital birth,” (b) a social control explanation which proposes that the supervision of adolescents is more difficult in single-parent families than in two-parent families, and (c) an instability and change hypothesis which proposes that “a premarital birth is a response by a woman to the stresses accompanying instability in her family situation. ” In their own analysis of the relationships between family structure and the risk of premarital birth, Wu and Martison conclude that their findings were consistent with the instability and change explanation but provided little support for the social control and socialization hypotheses.

Refined Model of Family Learning Environments

The discussions of family structural characteristics, whether they relate to sibling constellation variables, the presence of parents, or family disruption, suggest that the model of family environments which was presented in Chapter 2 might be refined as

shown in Figure 3.2. In the model, families are defined by a socialization dimension which might be assessed by family social capital and the activation of intellectual capital. The second defining characteristic is labeled as an allocation dimension which is measured by how family structures provide opportunities or present barriers for children’s learning. Sometimes the characteristics that define the two dimensions may be reinforcing and supportive, whereas in other families the characteristics might be in opposition. In studies of children’s environments, researchers often examine either the socialization or allocation dimensions of families. However, as McClelland (1990, p. 103) suggests “Rather than attempt to choose between these two interpretations, one could choose an alternative path to unify them theoretically, which would show how both culture and structure interact to produce and modify occupational goals.”

The socialization and allocation functions of families need to be investigated within the economic resources available to the families. Also, the socialization, allocation, and economic resources of families will be influenced by a family’s membership in particular ethnic, racial, and social status groups. Thus, it is important to consider

the model which is being presented, as being embedded in those wider constraining social contexts. As Halsey (1975, p. 17) claims, in much family environment research, the concept of social group membership “is trivialised to the point where differences of parental attitude are conceived of as separate factors rather than as an integral part of the work and community situation of children.”

If the inclusion of structural characteristics helps to refine the definition of family environments then it is appropriate to consider to what extent the inclusion of structural variables assists in defining school learning environments. In the following section, school structural characteristics such as ability grouping, instruction and curriculum, and teachers’ knowledge are examined.

Family and School Environments

Family economic capital

r sibling variables

Socialization characteristics

social capital intellectual capital

Figure 3.2. Environmental dimensions defining family contexts.

School Structural Characteristics

Ability Grouping in Schools

Numerous investigations have examined the relationships between tracking or streaming in schools and children’s learning outcomes. In an analysis of streaming in British schools, for example, Kerckhoff (1986, p. 856) concludes:

The evidence presented leaves little doubt that separation of students into ability groups has an effect on achievement test performance in both reading and mathematics. Students in remedial classes lose a great deal of ground (at least in reading), students in low ability groups lose ground, and those in high ability groups increase their average performance level beyond that exhibited by comparable students in ungrouped school settings. The losses by students in low ability groups, combined with the gains by students in high ability groups, make the overall effects of ability grouping very striking. Whatever the advantages educators may expect from

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ability grouping, there is no evidence here that grouping increases achievement test performance - except among students in high ability groups.

Kerckhoff’s investigation provides support for the divergence hypothesis of grouping which states that relative to what would have occurred if students had not been placed in ability groups, those students in high ability groups gain in academic performance while those in low ability groups lose in performance.

In an anatysis of academic tracking in American schools, Gamoran and Mare (1989, p. 1147) propose that while tracking has been examined extensively, “its role in promoting excellence or maintaining inequality has not been fully clarified.” They suggest that to assess the impact of tracking it is necessary to distinguish between the effects on educational productivity and on educational inequality. reproductivity refers to raising or lowering average outcomes, such as achievement or graduation rates. Inequality refers to the increase or reduction in the dispersion of outcomes, both overall and among subgroups, such as whites and blacks or high and low socioeconomic groups” (also see Gamoran, 1992). Their own investigation indicates that academic tracking does widen the gap in mathematics achievement and in the probability of graduating between students of high and low socioeconomic background.

Gamoran and Mare conclude:

Contrary to the expectations of some educators, noncollege programs do not do a better job of holding their students in school. Although our models are explicitly designed to reveal differences among students that would show that some are more suited to benefit from one track or the other, this hypothesis was not borne out. Instead, all students would be more likely to graduate if they enrolled in the college track. Even more than for achievement. the analyses of graduation indicated that students are tracked in accordance with their expected likelihood of finishing high school (p. 1177).

Gamoran (1993) also suggests that the inconsistent findings from investigations on grouping and tracking might relate to how instructional resources are allocated within schools. He proposes that “If the quality of instruction were invariant, ability grouping might benefit (or harm) all students equally, but if the quality of instruction varied along with the levels of the grouping system. ability grouping would serve some students well but do ill to others. Hence, the impact of ability grouping may depend, at least in part, on how it is used to distribute instruction to students in different ciasses.”

The inconclusive interpretations of findings from investigations of ability grouping is highlighted in an exchange between Slavin (199Oa, 1990b) and Hallinan (1990). In a review of research, Slavin (1990a, p. 494) proposes that there are several conclusions that can be advanced with some confidence:

1. Comprehensive between-class ability grouping plans have little or no effect on the achievement of secondary students, at least as measured by standardized tests. This conclusion is most strongly supported in Grades 7-9, but the more limited evidence that does exist from studies in Grades 10-12 also fails to support any effect of ability grouping.

2. Different forms of ability grouping are equally ineffective. 3. Ability grouping is equally ineffective in all subjects, except that there may be a

negative effect of ability grouping in social studies.

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4. Assigning students to different levels of the same course has no consistent positive or negative effects on students of high, average, or low ability.

In a response to the Slavin review, Hallinan (1990, p. 503) suggests that “Slavin’s conclusions, at least with respect to the impact of ability grouping on students assigned to different level groups, rest on shaky ground. Careful systematic research is currently underway by several social scientists examining the interaction between the organiza- tional practice of ability grouping and the instructional process as it affects student opportunities to learn and academic achievement. This research promises to be far more fruitful in addressing the complex issues related to the effectiveness and equity of ability grouping than previous studies that were limited in conceptualization, scope, and methodology.”

In a further reaction, Slavin (1990b, p. 506) comments that the “whole discussion is in many ways beside the point. Educators are not looking for research on whether students should be assigned to the high-, middle-, or low-ability group. As long as the system exists, students will be assigned to these groups by some standard. What educators want to know is the effect of the system, compared to a plausible alternative. The results [of the review] were clear. Comparing ability-grouped to ungrouped situations, there were no differences for high, average, or low achievers.” Slavin emphasizes that “educators must not expect that eliminating ability grouping will in itself accelerate the achievement of low-achievers (and they must not fear that doing so will decelerate the achievement of their high achievers). There is simply no evidence to support such expectations. While there is more we’d like to know about the instructional and curricular consequences of ability grouping (among other things), we do not know enough about the comparison between ability grouping and heterogeneous grouping to say this much, and this much is worth saying” (p. 507). But what must end, claim Slavin and Braddock (1993, p. 17), “is the kind of ability grouping that sorts students into categories that have long-lasting consequences; that is the between-class grouping strategies often called tracking.”

In an analysis of other aspects of grouping Hallinan (1992) suggests that “Surprisingly little empirical research has examined how ability groups are formed and how students are assigned to them. The paucity of research on the assignment process is particularly curious, given the potential consequences of placement” (p. 115). From an examination of tracking in middle schools, Hallinan concludes:

the number of tracks in a tracking system is determined independently of characteristics of the student population. Organizational factors, such as the schools’ resources, policies governing the size of classes and teachers’ work loads, and teachers’ and students’ activity schedules, seemed to determine how many tracks were established. Organizational constraints result in the failure of schools to adapt their track structures to changes in the student population. While a few changes in track assignments do occur, most students remain in their assigned tracks for the duration of the year and usually across years as well. This stability ignores the fact that students mature and learn at different rates and hence that the learning needs of some students may be served better if the students are reassigned to a different track. When an initial track-placement decision is inappropriate, the negative consequences of an inflexible tracking system are all the more serious (p. 126).

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Similarly, Pallas, Entwisle, Alexander and Stluka (1994) in an analysis of reading groups found that:

children in higher-ranked instructional groups learned more and received higher grades than did children in lower-ranked groups. In this sense, instructional grouping may have the unintended effect of increasing inequalities in educational outcomes, largely by creating inequalities in educational resources and rewards (p. 43).

Importantly, they conclude:

the symbolic meaning of instructional-group placements may be an important mechanism for increasing educational stratification. Children in higher-ranked read- ing groups were perceived by their parents and teachers as more competent than were similar children in low-ranked groups, often independent of their actual performance. These perceptions may structure the educational opportunities that parents and teachers subsequently make available to children, as we11 as the social-psychological resources they extend to such children (p. 43).

Indeed, the influence that parents can have on children’s placement in the tracking system was explored by Useem (1992), who indicates that better educated parents were more likely to intervene in direct ways to improve their children’s experiences at school. Useem notes that typical kinds of interventions by such parents include “requesting a certain teacher or the avoidance of a particular teacher; requesting a placement in a certain ‘team’, ‘cluster’, or ‘house’ at the middle school or secondary school; calling meetings with teachers, principals, counselors, or higher school administrators to ask for a change in a teacher’s behavior or to gain access to certain resources; seeking an override or waiver of a teacher’s recommendation for placement in a course; or removing a child from a classroom or school” (p. 272). Such findings provide support for the claim of Bowe, Ball and Gewirtz (1994, p. 38) that “education markets can be exploited by the middle classes as a strategy of reproduction in their search for relative advantage, social advancement and social mobility.”

This review indicates the need for further investigations of how ability groups are formed, how students and resources are assigned to those groups, how families influence the assignment of children to ability groups, what the effects are of ability grouping on educational productivity and inequality, what the effects are on learning of the alternatives to ability grouping, and how different school organizational forms influence instruction and curriculum and thus affect learning. In the following section some of the issues related to instruction, curriculum, and teachers’ knowledge in classrooms are considered.

Instruction, Curriculum and Teachers Knowledge

Perhaps it is trite to say, but if teachers are to be successful in creating stimulating learning environments and in promoting students’ opportunities and learning then they need to understand the nature of curriculum development and differences in instructional approaches. To assist teachers, it is important that researchers begin to examine with greater sensitivity the relationship between teachers’ knowledge and

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students’ learning. Shulman (1986) notes, for example, that research on teaching has tended to concentrate on how teachers: manage their classrooms, allocate time, structure assignments, organize activities, formulate the levels of their questions, plan lessons, and ascribe praise and blame. He cautions that research has not satisfactorily asked questions about “the content of the lessons taught, the questions asked, and the explanations offered” (p. 8). Shulman challenges investigators of children’s learning to start examining questions of the form, “Where do teacher explanations come from? How

do teachers decide what to teach, how to represent it, how to question students about it and how to deal with problems of misunderstanding? What are the sources of teacher knowledge? What does a teacher know and when did he or she come to know it? How is new knowledge acquired, old knowledge retrieved, and both combined to form a new knowledge base?” (p. 8).

If such questions are to be answered and provide evidence for the construction of more effective classroom and school learning environments, then Shulman (1987) suggests that “A major portion of the research agenda for the next decade will be to collect, collate and interpret the practical knowledge of teachers for the purpose of establishing a case literature and codifying its principles, precedents and parables” (p. 12). He goes on to say, however, that much, if not most of the knowledge base of teaching “remains to be discovered invented and refined. As more is learned about teaching. we will come to recognize new categories of performance and understanding that are characteristic of good teachers, and will have to reconsider and redefine other domains” (p. 12). Anderson (1989a, 1989b) suggests further that part of that research into classroom curriculum and instruction should adopt a cognitive-mediational conception of learning and learners. Such a conception assumes that “learning occurs when learners actively transform incoming information and construct meaning in terms of their prior knowledge. Teachers and others influence learning not through mere transmittal of information but through their influence on the students’ cognitive processing of that information” (1989b, p. 102). An implication of the approach is that, if teachers are to make links among ideas then they must gain information about how children are constructing knowledge as a lesson proceeds. That is, teachers must “attend to cues received during the lesson about how students are making sense of the information; and stand ready to adjust the presentation of new information according to student responses” (1989b, p. 104). But as Anderson (1989b) indicates “a teacher’s capacity to respond to students with appropriate linking ideas may depend in large part on the teacher’s own knowledge of the content and store of representations” (p. 104).

Such theoretical orientations about curriculum and instruction indicate that our understanding of children’s learning environments will be enhanced if we consider teachers as being thoughtful professionals. Indeed, Peterson (1988) suggests that there are several important qualities of the thoughtful professional teacher:

First, the thoughtful professional is engaged continuously in the process of learning. Not only is the thoughtful professional teacher engaged in “learning to learn” and “in higher order learning,” but she or he also inspires and facilitates this kind of higher order learning in students. Second, teachers’ thoughts, cognitions, judgement, thinking and learning processes become important dimensions in studying the teacher and teaching and in determining what constitutes “effective teaching.” Finally, teachers’ thoughts, knowledge, judgements, and decisions will have a profound

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effect on the way teachers as well as on the way students learn and achieve in

their classrooms (pp. 5-6).

The need to examine with greater sensitivity the role of teachers as caring professionals in classrooms is stressed also by Alexander, Entwisle and Thompson (1987). They indicate that “The environment of the classroom is intensely interpersonal, and good teaching is not simply a matter of using time wisely, of selecting the right reading series, of adopting a particular classroom management technique. Nor is it reducible to matters of professional development. All the perspectives neglect differences of teacher background and/or personality that determine to a considerable extent what actually happens in the classroom. Teachers implement the curriculum, regulate time usage, and structure classroom process. Whether they are sympathetic

or hostile, faithful or lax, skilful or inept surely matters. Our results emphasize the social-psychological dynamics that underlie classroom process; pupil performance is driven down where teachers are distant and disaffected” (pp. 680-l).

Conclusion

This discussion of the structural characteristics of family and school environments suggests that children’s learning outcomes will be related to sets of socialization, allocation, and economic influences and that the relationships will vary for boys and girls from different ethnic and social status groups. So far, in this presentation, learning environments have been defined by rather global and distal measures. In the following chapter relationships are examined between more refined measures of children’s environments and their learning outcomes.