Contextual Effects in the Classroom: The Impact of Ability Groups on Student Attention

Contextual Effects in the Classroom: The Impact of Ability Groups on Student AttentionAuthor(s): Diane Felmlee and Donna EderSource: Sociology of Education, Vol. 56, No. 2 (Apr., 1983), pp. 77-87Published by: American Sociological AssociationStable URL: http://www.jstor.org/stable/2112656 .

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CONTEXTUAL EFFECTS IN THE CLASSROOM: THE IMPACT OF ABILITY GROUPS ON STUDENT ATTENTION

DIANE FELMLEE AND DONNA EDER

Indiana University

Sociology of Education 1983, Vol. 56 (April):77-87

This study looks at contextual effects within the elementary classroom by examining the extent to which students' ability group assignments affect their rate of becoming inattentive. The data include behavioral measures of attentiveness obtained from 16 video-taped lessons of first grade reading groups. These data were analyzed using a continuous-time, stochastic model in which the dependent variable is an instantaneous rate of change from the state of attention to the state of inattention. Assignment to a low ability group was found to have a strong negative effect on student attentiveness, controlling for individual characteristics and previous individual inattention.

INTRODUCTION

After reading the literature on contextual effects in school settings, one might easily conclude that students are only minimally affected by their learning environment. Several prob- lems, however, have been raised with these studies, such as their focus on school-level analyses, failure to specify the processes by which effects occur and use of cross-sectional designs (Sewell and Armer, 1966; Hauser, 1970; Hauser, Sewell and Alwin, 1976; S0ren- sen and Hallinan, 1977). Thus, it would be safer to conclude that the extent to which students are influenced by their learning environment is still unknown.

This paper takes a new approach to the study of contextual effects, one which attempts to address the above criticisms. The focus of this study will be the social context in which learning occurs, i.e., within-classroom ability grouping. It will be argued that the ability group to which students are assigned has a significant effect on their attentiveness when controlling for individual characteristics such

as ability and maturity level. More generally, it will be argued that micro-level analyses of group processes are needed to adequately examine the complex phenomena of contextual effects.

BACKGROUND

Much of the research on contextual effects has consisted of between-school comparisons. The results of these studies indicate that school contexts (generally operationalized as school socioeconomic background level) have statistically significant but small effects on students' aspirations and/or attainments when controlling for students' individual ability and background characteristics (Alwin, 1976; Alwin and Otto, 1977; Alexander et al., 1979). However, since the amount of variance in achievement within schools has been shown to far exceed the amount of -variance between schools (Cole- man et al., 1966; Jencks et al., 1972), more recent studies have identified important group environments within schools. At the high school level, curriculum track placement has been found to affect students' educational plans (Hauser, Sewell and Alwin, 1976; Alex- ander and McDill, 1976; Alexander, Cook and McDill, 1978). There as yet have been few attempts, however, to examine the effect of within-school contexts at the elementary level, even though contextual effects are likely to be greater when children are first beginning to learn (Alwin and Otto, 1977). Contextual effects at the classroom level are of particular importance. While students who attend the same school or who are even assigned to the same curriculum track do not necessarily interact with each other, students who. are assigned to the same ability-based instructional groups within classrooms are together whenever they are being taught. Thus. con-

We thank Robert Hauser and Nancy Tuma for their helpful comments and suggestions. We also thank Fred Jones, Rick Monroe and Wai-ying Tsui for computer assistance; Coleen McCracken, Re- becca Cooper and Jim McDonough for their help in preparing and coding the data; and Cathy Evans for typing the manuscript. This research was supported by Spencer Grants No. 44-329-01, No. 44-329-03 and National Science Foundation Grant No. 82-L 08328. An earlier version of this paper was presented at the meetings of the American Sociological Associ- ation, Toronto, 1981. Both authors contributed equally to this paper. Order of authorship was determined randomly. Address all correspondence to the authors at the Department of Sociology, Ballan- tine Hall, Indiana University, Bloomington, IN 47405.

77



78 FELMLEE AND EDER

textual effects on learning are likely to be strongest at the elementary classroom level.

A second problem with early studies of contextual effects is their reliance on aggregate background variables, such as socioeconomic or ability composition of schools. While these variables are viewed as representing underlying processes, the processes themselves are seldom measured (Hauser, 1970). Later studies have examined some of the intervening variables. For example, Alwin and Otto (1977) found that the higher the average ability level of the school, the less likely it is that an individual will be in a college preparatory curriculum. In turn, curriculum placement appears to affect students' aspirations by increasing the likelihood of association with high status, high ability and college-oriented peers (Alexander and McDill, 1976; Hauser, Sewell and Alwin, 1976).

These studies have focused mainly on one process by which students are influenced by peers, i.e., knowing that one's friends are planning to attend college increases one's own educational aspirations. It is likely that association with higher ability peers influences students' behaviors and attitudes in a variety of other ways. For example, students are likely to be directly influenced by the academic and social behavior of other group members during classroom lessons. Students assigned to a high ability group will generally have more positive models to imitate than will students assigned to low groups.

A final problem with early contextual research is the predominance of cross-sectional designs. This type of design is generally not adequate for determining the direction of influence with complex phenomena like group effects (Hauser, Sewell and Alwin, 1976; Alwin and Otto, 1977). One could argue, for example, that students' association with college- oriented peers influences their curriculum track assignment rather than vice versa as Al- exander and McDill (1976) point out. Two recent studies have used longitudinal designs (Hauser, Sewell and Alwin, 1976; Alexander, Cook and McDill, 1978), designs which represent a clear improvement over cross-sectional research. Nevertheless, the designs are based on panel data, which do not completely capture the complex change relationships involved in continuous-time processes (Hannan and Tuma, 1979). Event history data, data recording all changes and timing of phenomena, are needed to do this. For example, information about changes that occur between panels is included in event history data but not in panel data. A related criticism is the use of static models (S0rensen and Hallinan, 1977). Even when research has used longitudinal data, it

has not coupled these data with dynamic models, models that are most appropriate for the study of change.

These criticisms are not independent of one another. In order to address one, we need to address the others. In order to solve-the crucial problem of theoretical inadequacy we need to look at the underlying processes that produce contextual effects. This leads to focusing on the question as to how group environments influence, or change, the behavior of individuals. Addressing this question requires: (a) a micro level of analysis, (b) the use of specific behavioral variables rather than aggregate measures, (c) data recording changes in the behavior of students over time, and (d) a model to fit the change process.

RESEARCH PROBLEM

This study will focus on a particular form of grouping within schools, i.e., ability grouping within classrooms. This practice of assigning students to ability-based,groups for instruction in reading and/or math is extremely common in American elementary schools. For example, ability grouping for readinig instruction was found to occur in 74 to 80 percent of all classrooms (Austin and Morrison, 1963; Wil- son and Schmits, 1978). Assignments are based on teachers' estimations of students' aptitude for learning as determined by recom- mendations from previous teachers, students' performance on achievement tests or their own observation of students. Consequently, there is typically no selection by students. This con- trasts with the case of high school curriculum tracks where students, at least theoretically, have a choice of tracks. A second form of ability grouping in elementary schools is across-classroom grouping where entire classrooms represent different levels of-ability. While this form of grouping is common in British elementary schools, it is currently less common in American schools.

Studies of both across-classroom grouping and within-classroom grouping have found a significant effect of group assignment on ability and/or achievement, controlling for initial ability (Douglas, 1964; Rosenbaum, 1976; Al- exander and McDill, 1976; Weinstein, 1976). More recent studies suggest that students' group assignments may also have an important influence on their behavior. Specifically, students in lower groups have been found to spend much less time paying attention and less time reading than students in higher groups (McDermott and Gospodinoff, 1978; Metz, 1978; Eder, 1981). While these differences could be due solely to differences in students' characteristics, it is likely that the environment



CONTEXTUAL EFFECTS IN THE CLASSROOM 79

in which children learn could also influence student attentiveness.

Knowing the extent to which different group environments affect student attentiveness is important for a number of reasons. First, students are likely to learn more when they are paying attention than when they are inattentive. Lahaderne (1967) found a significant, but small, -correlation between students' attention in class and their performance on achievement tests, controlling for their performance on in- telligence tests. Second, students are evaluated by teachers in terms of their social as well as academic behavior. There is increasing evidence that teachers' perceptions of normative behavior influence their cognitive expectations as well as their evaluations of students' academic performances (Williams, 1976; Entwistle and Hayduck, 1981). Normative behavior has also been found to influence ability group assignments thrioughout the elementary grades (Leiter, 1974; Eder, 1981; Haller and Davis, 1981). Thus students who are more inattentive one year are likely to be assigned to lower ability groups the next year even though their greater inattention may be due to their initial group assignment.

Research Design

Model. The unit of analysis in this study is a student's shift in attention. Attention shifts represent changes in qualitative states, i.e., changes from attentiveness to inattentiveness, which can occur with some probability at any point in time. The appropriate model for such a process is a discrete-state, continuous-time, stochastic model. The purpose of this research is to specify sources of heterogeneity in this process, i.e., the individual and situational influences on inattention. Therefore a multivariate form of a stochastic model will be used in the analysis.

Tuma et al. (1979) have recently developed an estimation procedure for analyzing a mnul- tivariate, discrete-state, continuous-time, stochastic model. This is a very general model, one which has been applied to several social phenomena such as job mobility (Tuma, 1976; S0rensen and Tuma, 1981;, Felmlee, 1982), marital disruption (Hannan et al., 1977) and change in city , government (Knoke, 1982). The model has several distinct advantages over other classes of models that are used in the behavioral and social sciences (Tuma et al., 1979; Hannan and Tuma, 1979), the most important being its ability to capture dynamic causal processes.

The dependent variable is an instantaneous rate of change from one state, j, to another state, k. It is defined as follows:

PJk(t, t + At) (1) rJk(t) = lim I j ok

At--O At

where Pjk(t, t + A t) is the probability of a change from state j at time t to state k at time t + A t. The specific dependent variable in this research will be the rate of change from the state of attention to the state of inattention. The estimation equation will be of the follow- ing form:

rjk(t) = exp(caJk X + (PjkY)t), (2)

where c 3k and Bjk are vectors of parameters to be estimated, and X and Y are vectors in independent variables, with Y accounting for time-dependence.

This dependent variable is not a common one in multivariate analyses. A more usual approach to the study of factors influencing student attentiveness might be to analyze correlates of a dummy dependent variable measuring whether or not an individual was attentive at a particular point in time or a variable measuring the proportion of time an individual was attentive in a reading lesson. Such measures, however, obscure the fact that two processes produce the observed distribution of attentiveness at any one point in time, the process of shifting from attention to inattention and the process of shifting from inattention to attention. These processes may not have the same determinants, and only by focusing on the two types of shifts separately can a clear causal picture be obtained. In this analysis we chose to study one of these two shifts, the process of becoming inattentive, because the concern of most related research has been with the question of what makes students inattentive.

The exponential form of the model was cho- sen rather than, say, a linear form, because the resulting loglinear relationship has the advan- tage of constraining every rate of change to be positive for every individual. By definition, a fundamental assumption is that instantaneous transition rates are positive. This exponential form of the model also usually fits data better than does a linear specification.

A positive value of a parameter in this model indicates the amount by which a unit increase of the independent variable increases the logged rate at which individuals shift from attention to inattention per unit- of time. A negative value, on the other hand, shows that the independent variable reduces the transition rate. The antilogs of the parameters of the model can be interpreted as "multipliers" of the rate of change, and these can be calculated to determine the effect of a variable on the absolute,



80 FELMLEE AND EDER

rather than the logged, transition rates. A unit increase in an exogenous variable multiplies the transition rate by the value of the antilog of the corresponding parameter.

Maximum likelihood estimation is used to estimate the parameters of the model with a computer program called RATE, developed by Tuma and colleagues (Tuma et al., 1979). Among other advantages, using maximum likelihood estimation allows estimation of the parameters with censored events included in the analysis. This leads to estimates that are asymptotically unbiased and which also have very good small sample properties with moderate degrees of censoring (Tuma and Hannan, 1978). Censored events are observations that are interrupted before a change in state has occurred. In these data censored events occur when attentive episodes are ended artificially by the completion of the reading lesson, a temporary change to a non- reading activity occurs and a new reading turn is assigned. Leaving such censored events out of the analysis has been shown to result in serious bias (S0rensen, 1977; Tuma and Han- nan, 1978).

The estimation procedure produces standard errors of estimates, allowing tests of hypotheses about individual coefficients. Also, the likelihood ratio chi-square can be used to assess the significance of the set of variables in a mnodel, and to compare the improvement when additional independent variables are added to the model. Let L1 represent the likelihood for a model with no constraints on parameters and Lo represent the likelihood for a model in which the coefficients of the independent variables are constrained to be zero. The likelihood ratio X is defined as the maximum of Lo divided by the maximum of L1. It can be shown that for large samples -2 ln X has a chi-square distribution with degrees of freedom equal to the number of independent variables.

Description of Classroom and Ability Groups

The classroom which was studied was a first grade classroom with a middle-aged, female teacher and 23 students. The classroom was located in a medium-size community in California and the students were primarily from middle-class backgrounds. Students were assigned to four, relatively equal--size ability groups during the first week of school. These assignments were based mainly on kindergarten teacher perceptions of reading aptitude, although the teacher also relied on her own observation of the students. They were not based on reading readiness test scores which were not generally used for group assignments by the teacher and were not avail-

able when assignments were made. Altogether, there were 13 students in the two high groups (eight males and five females) and ten students in the two low groups (five males and five females).

These groups met each day for 15 to 20 min- utes of reading instruction. The primary activity for these lessons was individual oral reading during which the teacher assigned turns at reading to one student at a time until all students had at least one chance to read. This was found to be the main activity of most ability-based reading groups (Austin and Mor- rison, 1963).

Data Collection

Sixteen video-taped reading lessons were coded for this analysis, four lessons from each of the four groups. One-half of the lessons took place during the second month of school and one-half took place during the seventh month. All students had prior experience with being video-taped and their behavior on other days when they were not taped indicated that the video-taped lessons were typical of lessons in this classroom.

Each students' attentive behavior during oral reading by other members was coded. Atten- tive behavior was defined as looking at what was being read or taught. All other behavior during reading turns was considered to be inattentive behavior (e.g., looking away from the group, watching other group members, playing with objects such as book markers, talking about something other than the activity of reading).I Intercoder coder agreement based on four of the 16 lessons was 89 percent.

Hypotheses

The conceptual model consists of three classes of variables: individual characteristics, group characteristics and time-dependence. The variables in each of these categories are defined in Table 1 and descriptive statistics are in Table 2.

The main variables of interest are the group factors. The first group variable, group ability level, is a dummy variable coded 1 for high reading ability groups and 0 for low groups. It is designed to tap the general group effect and

'It is, of course, also possible that students may be attentive when they appear to be inattentive or inattentive when they appear to be attentive. However, since obvious behavioral inattention is more likely to negatively influence teachers' perceptions and evaluations, it is of interest even though it may not reflect the total amount of inattention during classroom lessons.




Table 1. Independent Variables and Their Indicators

Variable Indicator Individual Characteristics

SEX 0-male, 1-female READING APTITUDE individual's total score on standardized reading readiness

achievement tests taken at the end of kindergarten MATURITY LEVEL teacher's perception of individual's maturity: 1-immature,

2-average maturity, 3-mature, 4-very mature SES father's occupational status as measured by Duncan's SEI

Scale PAST INDIVIDUAL INATTENTION proportion of time an individual has been inattentive during

the class lesson prior to the shift Group Characteristics

GROUP ABILITY LEVEL 0-low ability level reading group 1-high ability level reading group

READING LENGTH length of the reading turn during which the shift occurred in seconds

READING ERRORS average number of reading errors-made during the reading turn when the shift occurred

Time Dependence ATTENTION DURATION length of the attention period prior to the shift in seconds

is expected to have a negative effect on rates of attention shifts. That is, students in high ability reading groups are expected to have lower rates of becoming inattentive than students in groups of low reading ability.

The ability group effect is seen as developing through two processes. The first process is due to reading differences between the groups. Reading turns in high ability groups are shorter and have fewer reading errors than turns in low ability groups, as is shown in Table 2. Long, error-laden reading turns may be difficult to attend to. Therefore, students in low ability groups could be inattentive simply because they have less enjoyable tasks to attend to than

students in high ability groups. The second process is due to peer influence or modeling. When one student is inattentive, this increases the likelihood that another student will be inattentive. For example, one inattentive student may physically disturb or simply distract another. Therefore, a student in a low ability group may be more inattentive than one in a high group because his or her peers are more inattentive. The same student placed in an on-task, high ability group may be quite attentive.

Two group variabl;es, reading length and reading errors, measure specific aspects of the first ability group effect-that due to dif-

Table 2. Descriptive Statistics for Independent Variables for the High and Low Ability Groups and for all the Cases.a

Variable High Group Low Group All SEX .50 .49 .50

(.50) (.50) (.50) READING APTITUDE 250.7 200.7 221.0

(15.14) (17.48) (29.63) MATURITY LEVEL 3.04 2.18 2.53

(.70) (1.08) (1.04) SES 65.77 69.14. 67.77

(20.61) (20.06) (20.34) PAST INDIVIDUAL INATTENTION .18 .33 .28

(.18) (.21) (.22) GROUP ABILITY LEVEL 1.00 0.00 .41

(0.00) (0.00) (.49) READING LENGTH 37.39 57.13 49.12

(19.34) (33.49) (30.18) READING ERRORS 1.17 2.54 1.98

(1.12) (2.42) (2.11) ATTENTION DURATION 21.79 13.02 16.58

(21.23) (13.56) (17.61) N OF CASES 226 331 557 a Standard deviations are in parentheses.



82 FELMLEE AND EDER

ferences in reading ability between the two groups.2 Reading length (duration of the reading turn during which the attention shift occurred) is expected to be positively related to rates of attention shifts. Reading errors could have two opposite effects. Many errors in reading turns may make it difficult for a pupil to concentrate during the lesson. On the other hand, a reading mistake means that a change has occurred in the reading lesson and this may keep listeners' attention from wandering. Also, teachers often ask other students to help when one student makes an error which would increase students' attentiveness. This practice is especially common in low ability groups (Eder, 1981).

We want to test for group effects while controlling for individual characteristics and behaviors. Therefore several individual level variables are included in the model. Tentative hypotheses can be outlined for these factors. Sex of the pupil is the first individual characteristic. Observational studies frequently find that boys are more disruptive than girls in school (Serbin et al., 1973). This implies that boys are less attentive during lessons and that girls will have lower rates of attention shifts than boys. Another individual level variable is maturity level. Since immature children may find it difficult to attend to ieading lessons for long periods of time, maturity level is expected to have a negative effect on rates of changing from attention to inattention. The third individual variable is reading aptitude. Those who have more aptitude for reading are likely to enjoy reading tasks and find listening to some- one else read relatively easy. Attention shift rates, therefore, are expected to vary negatively with the individual's reading aptitude. SES (father's occupational socioeconomic status) is the fourth individual variable. It is included to control for any differences in attentiveness due to socioeconomic background. Past individual inattention is the remaining individual measure. It is the percent of time in the lesson that a student has previously been inattentive. Some individual's may be more inattentive than others. This variable is meant to control for such basic differences and is expected to have a positive coefficient in the estimated model.

A time factor, attention duration, is also included in the model. It will test for duration- dependence. Duration-dependence occurs when rates of change vary as a function of time in the origin state. Negative duration-

dependence, where rates of change decrease monotonically with time spent in the origin state, is commonly observed for rates of change for states such as working at a job (S0rensen and Tuma, 1981; Sandefur, 1981; Felmlee, 1982) and marital status (Hannan et al., 1977). The presence of duration- dependence often reflects unmeasured heterogeneity (Ginsberg, 1971), although it can imply the operation of a substantive process. The variable attention duration is used here largely as a control for either type of effect.

RESULTS

In the first step of the analysis we examine the effect of characteristics of individuals on their inattentiveness. A model with only individual level variables and the time- dependence variable is estimated, and the findings are shown in the first column of Table 3. The chi-square for the model (40.81 with 6 degrees of freedom) is significant at less than the .001 level which means that the model represents a significant improvement over a constant rate model, a model with no independent variables.

The individual level variables have effects that are in the hypothesized directions. Most of the effects, however, are not statistically significant-those for sex, maturity level and past individual inattention. The one variable having a significant coefficient (-.007) is reading aptitude. The higher the reading aptitude of a student, the lower the rate of becoming inattentive during reading lessons.

In the next step of the analysis, group level variables are added to the individual variables in the model. Several interesting findings, de- picted in the second column of Table 3, emerge. First, the individual characteristics variables have no independent statistically significant effects.3 Even the individual's reading aptitude does not significantly influence rates of attention shifts when group characteristics are included in the analysis. The second finding is that the group level variables have a highly significant impact on the dependent variable. The three group factors increase the chi-square for the model from 40.81 with 6 degrees of freedom to 65.14 with 9 degrees of freedom. This is a highly significant increase of 24.33 for the three degrees of freedom.

2 Reading errors and reading length are not measured as group averages. They do vary by ability group, as can be seen in Table 2 and that is why they are labeled as group variables.

3 The total lack of individual effects is quite sur- prising. Of course it is possible that small sample size or measurement error are contributing to this result. Nevertheless, one of the independent variables, past individual inattention, is a behavioral measure with as much accuracy as the dependent variable. This variable also does not have a statistically significant effect in the model.




Table 3. Estimates of the Effects of Individual and Group Characteristics on Rates of Attention Shifts (N = 557)a

Variable MODEL I MODEL II

Constant - 1.581*** -3.532*** (.467) (.654)

SEX -.119 -.023 (.105) (.108)

READING APTITUDE -.007** .003 (.002) (.003)

MATURITY LEVEL -.087 -.082 (.059) (.059)

SES .004 .003 (.003) (.003)

PAST INDIVIDUAL INATTENTION .140 -.067 (.246) (.256)

GROUP ABILITY LEVEL -.715*** (.188)

READING LENGTH .008** (.002)

READING ERRORS -.097** (.036)

ATTENTION DURATION -.008* -.006 (.003) (.003)

Chi-Square 40.81*** 65.14*** df 6 9 a Standard errors are in parentheses.

*.05 2p>.01. **.01 Bp>.00l.

*** p - .001.

A further finding is that each of the three group level variables has an independent effect that is significant and substantial. The largest of these effects is the group ability level measure. It has a negative coefficient, -.718, which is significant at the .001 level. Students in high ability groups have a lower rate of becoming inattentive than those in low groups, even when controlling for factors such as the individual's reading aptitude. In addition, the antilog of the coefficient is .489. This says that a unit increase in the group ability level variable increases the rate of shifting attention for an individual by 48.9 percent. In other words, high ability group students become inattentive at less than one-half the rate of students in low ability groups, net of other effects.

Other group variables, reading length and reading errors, also have significant effects. Reading length has the anticipated positive effect on attention shifts, with longer reading turns producing more inattention.4 Presum-

ably, as reading turns lengthen they become less interesting and therefore more difficult to follow. Reading errors, the final group factor, has a negative significant effect on attention shift rates. It appears that reading errors are not distracting, but act as reminders of the task at hand-the reading lesson-and because of this, actually reduce inattention.

The remaining finding is that although there is significant negative duration-dependence in the individual level variable model, there is none in the more complete model. In other words, rates of changing from attention to inattention do not vary significantly with the amount of time spent in the attentive state in the final model. The duration-dependence in the first model was capturing unmeasured heterogeneity.

In the third step of the analysis we divide the observations by season, into those from the fall of the school year and those from the spring. As can be seen in Table 4, there are definite differences in the results for fall and spring. In the fall, the model does little to explain rates of attention shifts. The chi-square is relatively small (32.85) and the only independent variable that has a significant coefficient is attention duration. The negative effect of attention duration indicates that the longer an individual is at-

4 The reading length variable was measured as the length of the reading turn during which an attention shift occurred. Ideally, the reading length variable would be measured as the reading turn length up to the time of the attention shift. Then coefficients in the model could be determined by simple arithmetic manipulations. This method, however, does not produce standard errors for all the variables in the model. In future work it may be possible to estimate

the standard errors and therefore utilize the more complete reading length measure in the model.



84 FELMLEE AND EDER

Table 4. Estimates of the Determinants of Rates of Attention Shifts for Fall and Springa

Variable Fall (N = 256) Spring (N = 301)

Constant -2.948** -4.151*** (.978) (.963)

SEX -.210 .085 (.167) (.152)

READING APTITUDE -.001 .007 (.005) (.005)

MATURITY LEVEL -.020 -.124 (.098) (.076)

SES .005 - .003 (.004) (.004)

PAST INDIVIDUAL INATTENTION .202 -.328 (.360) (.372)

GROUP ABILITY LEVEL - .286 - 1.152*** (.250) (.319)

READING LENGTH .007 .012*** (.004) (.003)

READING ERRORS -.019 -. 125* (.081) (.043)

ATTENTION DURATION .016** .006 (.005) (.004)

Chi-Square 32.85*** 54.14 df 9 9 a Standard errors are in parentheses.

* .05 p > .01. ** .01 p > .001.

*** p s .001.

tentive, the lower is his/her rate of becoming inattentive. This duration-dependence could indicate a "cumulative inertia" type of effect or it could simply be reflecting unmeasured heterogeneity. Neither individual nor group processes appears to account for inattention in the classroom in the fall.5

The findings in the spring indicate that group effects on inattention develop over time. The individual level variables still have no effect on attention shift rates in the spring and neither is there significant duration-dependence. The group variables, however, have effects that are highly significant. The group effects are larger, but in the same direction, as those obtained in the aggregated model in Table 3. The group ability variable has a particularly strong effect on rates of attention shifts in the spring (- 1.152). The antilog of the coefficient is .32, indicating that students in high groups become inattentive at approximately one-third the rate of low group members, when other factors are controlled. 6

DISCUSSION

In summary, group effects on attentiveness are much larger than individual effects. In fact, once group characteristics are added to a model consisting of individual level variables, none of the variables at the individual level has a significant effect on attentiveness. This em- phasizes the strong impact of learning environments on students' behavior.

Both reading length and reading errors have significant effects on attentiveness. Students have higher rates of becoming inattentive during longer turns. Since long turns are more common in lower groups, this is one group characteristic which contributes to more inattention in lower groups. However, reading errors, which were more common in lower groups, were found to decrease attention shift rates.

Group ability level has the greatest effect on student attentiveness. In the spring, students in low groups become inattentive at more than three times the rate of high group students.

s We have also done separate analyses for each of the four days. The findings for the two fall days and the two spring days were very similar. The group effects do not appear until the spring.

6 There are several possible sources of interdependence among these data. We attempt to control for these statistically by various means-the inclu- sion of the "past individual inattention" variable, separate analyses for fall and spring, and separate

analyses by day (not presented here). Yet in the discussion we acknowledge that one student's attention shift may be influenced by other students' inattention. Not completely controlling for this source of interdependence could affect our estimates. We are attempting to explicitly model this effect in future work. Documentation of this effect would simply strengthen our main argument that group contexts influence students' inattention.




Previous research found that low group members in this class were inattentive 40 percent of the time as compared to high group members who were inattentive only 20 percent of the time (Eder, 1981).7 It now appears that this considerable difference in attentiveness is due primarily to their group assignment rather than to individual characteristics.

Another important finding is the development of group effects over time. While the effect of ability group is in the expected direction in the fall, it does not become significant until the spring. This indicates that the longer the students are exposed to a group environment, the stronger its effects become. This provides additional evidence that the ability group effect is a function of the classroom environment and not individual differences.

The fact that group ability level had the greatest effect on attentiveness implies that substantial group processes are operating- processes in addition to the ones reflected in the reading turn variables. One such process is peer influence. Students who be'came inattentive due to boredom or other reasons often engaged in certain disruptive or distracting behaviors. For example, they would play with their bookmarkers or- talk to the teacher or other group members. Solomon and Wahler (1973) found that students paid more attention to such disruptive and distracting behaviors. In-depth, observations of interaction in this classroom indicate that students were dis- tracted by classmates who talked during reading turns and frequently imitated the nonverbal play of disruptive students. In future research we plan to code and analyze the effects of different types of inattentive acts on other members' attentiveness to determine the extent of peer influence.

Another process which could explain the higher rates of inattentiveness in low groups is the development of different ability group norms concerning attentiveness. Other research suggests that teachers may react dif- ferently to students interrupting a reading turn depending on their group level, thereby developing different group norms. Students' communicative styles were found to reflect these different norms over the course of the year, with high group members making many fewer interruptions in the spring and low group members making many more interruptions (Eder, 1982). Similarly, teachers may respond dif- ferently to student inattention across groups, expecting that students in lower groups will be

less attentive and more disruptive. Comments by the teacher in this classroom indicate that she did view low group members as less attentive and had lower expectations for their con- duct during reading lessons.

Yet another process could develop from the previous one. Once different norms are estab- lished across groups, students could also help to maintain these norms. In previous research students have been found to actively monitor the behavior of other members in line with group norms. Specifically, high group members often reprimanded each other for turn- taking violations (Eder, 1982). Students may also monitor their own behavior in line with group norms, thus becoming more attentive in groups where such behavior is expected. We plan to further examine both of these normative processes in future studies.

Of course this research, as well as any design that does not randomly assign cases to groups, is subject to the objection that the findings are attributable to initial selectivity into the groups. Two features of our analysis address the selectivity argument. First, we compare models for the fall and spring and find that the group effect does not appear in the fall but emerges in the spring, a finding which is incon- sistent with the claim that the group differences in attention rates existed at the beginning of tfie school year. Second, in our models we use the standard statistical technique of controlling for initial differences in student characteristics, in- cluding sex, reading aptitude, maturity level, SES, and past individual inattention. The measure of past individual inattention is a particularly critical control, becau-se it measures the level of inattentiveness just prior to the attention shift for the current case. The significant coefficient for the group ability level measure in the spring, therefore, represents the effect of being in a particular ability group at a point in time, controlling for the individual's prior inattentiveness in the group. This finding provides even stronger evidence that individual differences in inattentiveness do not account for the group effect.

These results are in line with several recent studies of grouping and student attentiveness. When comparing levels of attentiveness in classrooms which used ability groups for reading instruction and those which did not, Filby et al. (1982) found that high ability students were more attentive in all classrooms but that the variance in attentiveness was greater in grouped classrooms than in non-grouped ones. Metz (1978) looked at tracking at the junior high level and found greater levels of inattention in the lower level classrooms. An- other study which looked at tracking in junior high and senior high schools found that stu-

7This result indicates that students in low groups not only have higher rates of shifting to inattention, but that they in general spend more time being inattentive.



86 FELMLEE AND EDER

dents in lower tracks were more often engaged in "off-task" behavior than were students in higher tracks. (Oakes, 1982). They also found that these differences in attentiveness were greater at the senior high level, again suggest- ing that differential learning environments do influence students' behavior.

These findings have important implications for research on the effects of schooling. The fact that students who were assigned to low ability groups were more likely to become inattentive than students assigned to high ability groups suggests that students are not being exposed to equally positive learning environments within schools. Since students from lower socioeconomic backgrounds and students who have difficulty with standard En- glish are often initially assigned to lower ability groups within classrooms (Rist, 1970), the very students most in need of a positive learning environment would be exposed to less desir- able learning conditions. Furthermore, this relationship between family background and ability group assignment has been found throughout elementary and high school (Baker-Lunn, 1970;' Alexander and McDill, 1975; Hauser et al., 1976; Rosenbaum, 1976). Thus rather than making up for the initial dis- advantages which many children have upon entering school, practices such as ability grouping may further compound these disad- vantages.

More generally, the fact that group environments were found to have a greater effect on students' behavior than individual characteristics suggests that educational practices can have important implications for students. Cur- rently it is believed -that individual and family influences are very strong, and that the role of school and classroom factors on students' behavior is minimal. Our results imply classroom factors can have a considerable effect on students' behavior.

It is important to keep in mind that these findings are based on analyses of lessons from one elementary classroom. More research is needed to see if similar differences occur in other classrooms. The fact that qualitative studies have found similar group differences in attentiveness at the elementary and junior high level (McDermott and Gospodinoff, 1978; Filby et al., 1982; Metz, 1978; Oakes, 1982) suggests that these findings are not unique and that further research is warranted.

In conclusion, our research provides evidence for the existence of strong contextual effects in schools at the classroom level. The unique combination of dynamic modeling, in- depth, qualitative data and a micro-level focus has enabled the uncovering of these group effects. Future research needs to continue to

center attention on the underlying processes by which group contexts influence individual behavior.

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Contextual Effects in the Classroom: The Impact of Ability Groups on Student Attention