Memo from Lado to Counsel with Equity and Computers in the Schools Article

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sHart 

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organizational perspective. Educational Administration Quarterly, 26, 235-259. 

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JAIL 
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Author 

ANN WEAVER HART is Associate Dean, Graduate School of Education and Associate 

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Salt Lake City, UT 84112. She specializes in leader socialization and work redesign in schools. 

474 |   
   



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sociate 

MBH, 

chools. 

  

  

Review of Educational Research 
Winter 1991, Vol. 61, No. 4, pp. 475-503 

Equity and Computers in the Schools: 
A Decade of Research 

Rosemary E. Sutton 

Cleveland State University 

In this review, the research conducted during the 1980s on racelethnicity, gender, and 
social class differences in K-12 educational uses of computers is summarized in terms 
of access, processes, and outcomes. First, gender, social class, and racial inequalities in 
access to computers are documented. Second, equity in four aspects of process is 
considered: type of use, teachers’ attitudes towards equity and equality, curriculum 
content, and interactions among students. Third, the literature on three outcome 
variables is considered: student attitudes, computer-related competence (literacy and 
programming), and traditional achievement measures using computer-aided instruc- 
tion. Finally, implications from the decade of research are drawn. These include the 
conclusions that the use of computers maintained and exaggerated inequities, that 
equity issues are complex and future research should reflect this, that between-school 
differences in equality should be examined, and that much more research on poor and 
minority children is a priority. 

Microcomputers moved into schools in the U.S.A. at an unprecedented rate for a 
new technology. In 1975, the first microcomputer was developed. By 1981, the 
majority of secondary schools owned at least one microcomputer (Becker, 1983a), 
and, by 1985, more than 90% of all public schools owned at least one microcomputer 
(Office of Technology Assessment, 1987). The number of computers in use in schools 
by the end of the 1980s was estimated to be 2.4 million (Becker, 1990). 

Along with the rapid increase of microcomputer use in schools came a concern for 
inequalities in access and use of microcomputers. Quickly, it became apparent that 
richer schools bought more equipment and more expensive equipment for instruc- 
tion, African-American students had lower access to computers than did White 
students, and girls used computers in and out of schools less than boys (Lockheed, 
1985). 

This concern for equity lead to extensive activity in the research and practitioner 
computer education-related communities by the mid-1980s. For example, in 1985, 
the journal, Sex Roles, devoted an entire issue to gender equity issues in computer 
use, and Becker, who conducted two national surveys during the 1980s, devoted two 
of his newsletters to equity issues in K-12 school ownership and use (1983, Oct.; 
1986). In addition, numerous other research articles were published on this topic. 
The practitioner-oriented literature also responded quickly. A large portion of the 
April issues of The Computing Teacher in 1984 and in 1985 was devoted to equity 
issues. Materials and workshops were developed for teachers to help overcome 
  

My thanks to Patricia Campbell, Joanne Rossi Becker, and four anonymous reviewers for 
their comments on earlier drafts of this article. An earlier version was presented at the Annual 
Meeting of the American Educational Research Association, Boston, 1990. 

475



    

Sutton 

inequities in the classroom. “The Neuter Computer” (Sanders & Stone, 1986) and 

“Off and Running” (Erikson, 1986) are probably the best known examples. These 

curriculum materials were aimed primarily at gender differences in the classroom. 

However, by the end of the 1980s, the applied literature was focusing on minority 

concerns. In 1988, Electronic Learning magazine sponsored a conference entitled, 

“Technology and the At-Risk Student,” publishing a partial transcript of this confer- 

ence in its November/December, 1988, issue. In the same month, Teaching and 

Computers published an interview with four of the conference participants. 

The Importance of Equity in Computer Use 

During this period, the rationale for the attention to equity issues in school 

computer use typically focused on economic and global competitiveness concerns 

arising from several current but distinct beliefs. First, a plethora of reports during the 

1980s documented the inferior state of education in the U.S.A. when compared to 

other countries. Perhaps the best known, the “Nation at Risk” (U.S. Department of 

Education, 1983), a report on education, began: 

Our nation is at risk. Our once unchallenged preeminence in commerce, industry, 

science, and technological innovation is being overtaken by competitors throughout 

the world. (p. 5) 

Second, the predictions of the demographic changes that the U.S.A. was undergo- 

ing became well known because of the work of Hodgkinson (1985) and other 

researchers. The work force in the U.S.A. increasingly is consisting of women and 

people of color (Pallas, Natriello, & McDill, 1989). Thus, if the U.S.A. is to maintain 

a competitive place in the world economy, it must educate female and minority 

children because they are needed as future workers. Finally, the belief that computer 

competence was the fourth basic skill (U.S. Department of Education, 1983) and 

necessary for future workers was well promulgated (Nobel, 1984). Hence, so the 

argument went, for the country to remain competitive and retain the quality of life 

enjoyed by the predominately White middle class, the education system had to be 

reformed, and this reform had to focus on technological skills for all students. Thus, 

for reasons of self-interest, attention had to be paid to the school computer use of 

poor, minority, and female students. A concern for equity can be argued as part of 

any democratic system (Dewey, 1946), but rarely was this moral argument made in 

discussions about technology, because during the 1980s this was not believed to be 

persuasive. 
The widespread belief that computer competence was essential for students, 

future workers, and an educated citizenry and thus should be taught in schools has 

been challenged (Nobel, 1984). It can be argued that students do not need to learn 

about, and with, computers in schools because by the time they graduate the software 

and hardware they used will be obsolete and, in addition, they will receive all the 

training they need on the job. Whereas it is true that the field changes rapidly, a 

confident attitude towards technology has been found to be related to experience 

(Chen, 1986), and a confident attitude helps in mastering newer software and 

hardware. Much on-the-job training does occur, but a brief examination of the 

employment ads will indicate that some computer experience is a prerequisite for 

many jobs. 

476 
  

   



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Equity and Computers in the Schools 

Research Design Issues 

In this review, I summarize the research literature on race/ethnicity, gender, and 
social class differences in K-12 educational uses of computers. Research on educa- 
tional equity related to race/ethnicity and social class typically requires different 
research designs from research on gender inequities. Research on class and race 
frequently involves a comparison of different schools because of housing segregation 
and funding formulas for U.S.A. schools. Thus, one asks questions such as: Do 
schools serving mostly rich children have more computers than schools serving 
mostly poor children? Do students in predominantly African-American schools use 
computers in the same manner as students in predominantly White schools? These 
between-school analyses are hard to conduct on a limited budget; thus, most of the 
information comes from several national surveys. 

Few schools in the U.S.A. are segregated by gender, and gender of the student is 
obviously not associated with family income. Thus, when considering inequitics 
between girls and boys, one often asks questions about differences within schools and 
classrooms, such as: Are as many girls enrolled in advanced computer-programming 
classes as boys? What percentage of before- and after-school computer users are 
girls? Sources of information on this topic come from national surveys as well as 
numerous, small, local research studies. The limited sample size of some local studies 
raises questions of generalizability. 

Unfortunately, few studies on equity and school computer use have examined 
simultaneously race/ethnicity, social class, and gender. This is due to the difficultics 
of research design mentioned above as well as the lack of integration of these 
concepts found in all areas of educational research. This is a serious limitation of 
the rescarch, because it can lead to oversimplification or inaccurate understanding of 
what actually occurs in schools (Grant & Sleeter, 1986). 

The research is largely atheoretical, and much of it is descriptive in nature. This is 
not unexpected in a new field, but it does mean that one knows much more about 
what differences exist rather than why they exist. In this review, my intent is to report 
on the existing empirical literature; I do not attempt to interpret the data through a 
specific theoretical perspective, although I do use a framework for organization. 

Equity and Equality 

Writers concerned with equity issues in education have distinguished between the 
terms equity and equality (e.g., Brookover & Lezotte, 1981; Green, 1983; Secada, 
1989). Secada (1989) argues that equity is a qualitative property and refers to 
judgments concerning justice whereas, he argucs, equality is a quantitative property 
that describes parity among groups along some index (e.g., access to computers, 
attitudes towards computers). Using these definitions, most of the data summarized 
in this article are measures of equality or inequality. At times, [ do consider and 
discuss equity because the concepts are not synonymous. Considerations of cquity in 
curriculum content must involve a qualitative judgment (Secada, 1989). Also, it can 
be argued that equity may require inequality. For example, the evidence indicates 
that poor children have less access to computers at home and that at-home access is 
related to attitudes and competence (Martinez & Mead, 1988). Thus, if schools wish 
to provide equity for poor children with respect to their confidence and competence 
concerning computer learning, poor children need to be provided more in-school 
access—that is, incquality of access. 

A777



   Sutton 
Organization of This Review 

Equality in schools can be assessed in terms of inputs, processes, and outputs 

(Good & Brophy, 1986; Harvey & Klein, 1989). Schooling inputs consist of what a 
school starts with when educating its students and include financial, physical, and 

human resources. For the purposes of this review, access to computers is the most 
important input variable. Educational processes include what happens within the 

school when students are educated—for example, how students are treated by 
teachers and what courses they take. Outcomes include academic achievement, rate of 

dropping out, and attitudes (Secada, 1989). I use the following tripartite distinction as an 

organizer for this review of the research conducted in the 1980s. First, I describe equality 

in terms of access to computers at school and at home. Second, I summarize the data on 
process and consider type of computer use, teacher attitudes towards equity, curriculum 

content, and interactions among students. Third, I review the data related to outcome 

measures including attitudes, competence in computer learning, and the effectiveness of 

computer-aided instruction. In the final section of this article, I consider implications of, 
and make suggestions for, future research directions. 

Inequalities in Access 

During the early and mid-1980s, when microcomputers in schools were relatively 
new and individual units were expensive, much attention was paid to access. Many 
people believed that children needed exposure to computers to get ahead in the 

information society (Nobel, 1984). Middle-class parents raised money for the pur- 

chase of computers and pressured districts to provide access for their students 
(Cuban, 1986). Print and TV commercials suggested that students would not com- 
plete college without ownership of a personal computer. It was as if sitting at a 
terminal and learning to program would exercise the mind and enhance critical 

thinking (Salomon & Perkins, 1987). Whereas this now appears simplistic, the 

concern for access is legitimate, because equity in computer use must take into 
account access. In this section, I discuss the data on racial/ethnic, socioeconomic 

status (SES), and gender differences in computer access at school and at home. 

Access to computers has been assessed by two methods. In the first approach, 
teachers or administrators are asked the number of computers and students in the 
building, and the ratio of students/computers is calculated. This gives an assessment 

of available school resources and is an important between-schools variable. However, 
this ratio does not indicate which students within the school use the computer, and 
certain groups of students (e.g., females and low-achieving students) may not gain as 
much computer time as other groups. For example, in some high schools, Algebra I 
was a prerequisite for computer programming and literacy classes, thus assuring that 
high-achieving students had greater access (Arias, 1990). In the second approach, 

students are asked whether they have used a computer, or teachers are asked to 
estimate what percentage of computer use is by a specific group. This does address 
the issue of differential access within a school, but, if the school has voluntary 

computer time (e.g., before- or after-school use), this is not a pure assessment of 
access, because access is confounded with interest. 

SES and Ethnic Origin Differences 

Four national surveys conducted during the 1980s comparing access to computers 
by pupils of different social class backgrounds and different ethnic origins showed 

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Equity and Computers in the Schools 

consistent and predictable inequalities: Poor and minority children had less access to computers both at home and at school. It does seem that inequities in school-related access were lower in higher grades and decreased during the 1980s. I now summarize specific details of each of the four surveys. 
Becker, using the first approach to measuring access, conducted two national surveys in the 1980s. He asked computer-using teachers to identify the number of computers in their buildings. In 1982-1983, the mean ratio of elementary school students per microcomputer was 155:1 for students in high SES, predominantly White schools; for students in predominately White, low SES schools, the ratio was 192:1; for students in predominantly minority schools, the ratio was 215:1 (Becker, 1983, Oct.). The 1985 survey indicated similar inequities: 94% of K—6 schools with less than 4% African-American enrollment owned computers compared to only 67% of schools with a majority of African-American enrollment. These inequities were less dramatic for upper grades. In middle schools with less than 4% African-Ameri- can enrollment, 96% owned at least one computer compared to 90% in majority African-American schools (Becker & Sterling, 1987). When only considering the schools which owned computers, African Americans still had less access. The median student computer ratio was 72:1 for Sth-grade African-American students compared to 55:1 for Sth-grade White students. For 11th-grade African-American students, the median ratio was 43:1 compared to 35:1 for White students (Becker & Sterling, 1987). 

The two NAEP surveys conducted assessed access by the second approach. In the 1981-1982 NAEP Science assessment, over 15,500 students were asked if they used 

computer access and competence. They found that 78% of 3rd-grade White students, compared to 65% of African-American and 69% of Hispanic students, had used a computer. Again these differences were lower at the high-school level (89% of White students, 81% of African-American students, and 80% of Hispanic students had used computers) (Martinez & Mead, 1988). One statewide study in North Carolina conducted during 1984-1985 provided results consistent with the national surveys (King, 1987). There was lower access to computers in larger school districts and in school districts with a higher percentage of minority enrollment. Disparities in school access and use among children of different backgrounds were also reflected in at-home use. The 1985-1986 NAEP study found that 32% of White 

graduated from college were three times as likely to own a computer than students whose parents had not completed high school (Martinez & Mead, 1988). Thus, poor and minority children had less access to computers both at home and at school. 

Gender Differences 
Data from three large national surveys and from smaller state and local surveys indicated small gender differences favoring boys in access to computers in school (sce Table 1). Several studies reported no significant gender differences, but no study indicated girls had greater access. This pattern was not restricted to the U.S.A:



   
TABLE 1 
Summary of research on gender differences in access in school and at home 
  

Significance 
  

  

Total School Home 

Study Location N Grade access access 

Anderson, Welch, & Harris, 1984 U.S.A. 15,000 3rd, 7th, 11th 0 

Becker & Sterling, 1987 U.S.A. 265# K-6th <i 

Middle + 

High school + 

Martinez & Mead, 1988 U.S.A. 24,000 3rd + + 

7th + + 

11th oi + 

Chen, 1986 California 1,138 High school 0 : 
Linn, 1983a California 51,481 High school + 

Fetler, 1985 California 7,343 6th i 

4,800 12th * 

Miura, 1986 California 400 6th-8th 3 

Swadener & Jarrett, 1986 Colorado 259 4th-8th % + 

Campbell, 1989 Oklahoma & 1,067 7th-12th ’ 

Kansas 

Arenz & Lee, 1990 Wisconsin 306 Middle i 

Collis, Kass, & Kieren, 1989 Canada 3,000 11th * X 

Colbourn & Light, 1987 Britain 56 Middle : 

Culley, 1988 Britain 984 High school . 

Johnson, 1987 Britain 144 High school + 

Levin & Gordon, 1989 Israel 222 8th-10th * 2» 
  

0 No significant differences 

+ Data favoring boys, no significance reported 
* Significant differences favoring 
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Equity and Computers in the Schools 
Israeli girls (Levin & Gordon, 1989) and Canadian high-school girls (Collis, Kass, Kieren, 1989) also had less access. 

These gender differences are reports of averages. In addition to averages, Becker and Sterling (1987) examined between-school disparities and found large differences. For example, at the average K-6 elementary school, 45% of computer use was by girls. However, a school low in gender equality in computer use (at the 20th percent- ile) had a female participation of only 30% whereas a school high in gender equality in computer use (at the 80th percentile) had female participation of 50%. These between-school data are important because they indicate that schools and teachers play a major role in gender inequalities separate from society and parental influ- ences. 
School-related differences in access for boys and girls were accompanied by larger and more consistent differences in access at home (Table 1). Regional and national studies found that families of male students were more likely to own a computer than families of female students. Also, Hess and Miura (1985) reported that boys were three times as likely as girls to attend a Summer computer camp, and Linn (1985a) reported that only one quarter of the participants in the computer courses conducted at the Lawrence Hall of Science over a 3-year period was female. Again, this pattern was not restricted to the U.S.A: studies in Britain, Israel, and Canada all found gender differences favoring boys. 

Equity in the Process of Computer-Related Learning 
Access is one aspect of inequality and is often predominantly determined by state funding levels and district or building policies. Equity and equality concerns in process are closer to classroom teaching and, thus, to the teachers’ daily behavior. In this section, I discuss the type of use made of computers in schools, teachers’ attitudes towards equity and equality, curriculum content, and interactions among students. 

Type of Use 
Type of computer use changed during the 1980s. There was added sophistication of hardware and software, better student-computer ratios, and additional experience and training of teachers in how to use this new technology in the classroom. In 1983, it was found that schools that had owned computers for 3 or more years were using them more for programming and less for drill and practice than schools in the first year of computer ownership (Becker, 1983a). By the end of the 1980s, a trend away from programming in high schools had been reported (McCarthy, 1988; Yoder, 1989), and an emphasis on using the computer as a tool (e.g., word processing, databases, graphics tools) integrated into the curriculum was evident (Becker, 1990: Brady, 1991; McCarthy, 1988). 
Race and class differences. Whereas type of computer use changed over time, 

of students 

ative to the average income of the local education agency) had taken programming compared with 14% of students in non-Title I schools (cited in Anderson et al., 1984). In Becker’s 1982-1983 national survey, only 13% of high-SES, predominantly White schools reported intensive use of computers for drill and practice compared with 33%



   Sutton 

of predominantly minority schools; 21% of high-SES, predominantly White schools 
reported intensive use in programming compared with only 10% of predominantly 
minority schools (Becker, 1983, Oct.). Similar differences were evident in 1985. For 

low-SES students, 56% of their use was for drill and practice, and only 13% of their 

use was for computer programming. In contrast, for high-SES students, 39% of their 

use was for programming (Office of Technology Assessment, 1987). 
This means that the low-SES children, who are disproportionately African Ameri- 

can and Hispanic, were gaining most of their experience with a computer when it was 

in control, asking questions, expecting a response, and informing the student when 
he or she was correct. In contrast, the high-SES students, who are disproportionately 
White, were gaining considerable experience when they were in control, giving the 
computer a series of instructions, and observing the consequences of these instruc- 

tions. This differential use is consistent with long-standing beliefs that children must 

first master the basics before moving to higher order thinking and that poor and 
minority children lack the basics (Doyle, 1991; Laboratory of Comparative Human 
Cognition, 1989). Thus, the new technology was being used in a manner that 

reflected current practice and ideology, which Cuban (1986) has argued is the fate of 

all technologies unless major school reorganization occurs. 

Gender differences. Whereas school-related gender differences in overall access 
were relatively small, differences in type of use were larger. Becker and Sterling 

(1987) found that girls were underrepresented at the elementary, middle, and high- 
school levels in elective programming, game playing, and before/after school use. 

The only area in which girls were overrepresented was in high-school word processing 

(Becker & Sterling, 1987). Other studies supported these findings. Programming 
classes at junior-high school and high school in the mid-1980s were typically less than 
half female (Chen, 1986; Lockheed, 1986). Similar inequities were also reported in 

Canada (Collis, Kass, & Kieren, 1989), Australia (Clarke & Chambers, 1989), New 
Zealand (Department of Education, 1988), and Great Britain (Culley, 1988). 

Teacher Attitudes 

A number of studies examined teachers’ attitudes about school use of computers, 

anxiety about computers (e.g., Kay, 1989; Koohand, 1987; Marshall & Bannon, 

1986), and gender differences in teachers’ attitudes (e.g., Kay, 1989; Loyd & 

Gressard, 1986), but few researchers examined teachers’ attitudes towards equity 

and school use of microcomputers. It is important to examine teachers’ attitudes and 
not just rely on data on classroom use because while classroom behaviors and teacher 
attitudes may be consistent—for example, one study reported that teachers who 

expressed high levels of enthusiasm towards computers were more likely to be heavy 

computer users (Schofield & Verban, 1988)—this consistency may not always exist. 
Teachers frequently have little influence on school policy decisions. Thus, for exam- 
ple, teachers in a low-income minority school may be using drill and practice 
programs extensively in their classrooms, because that is the only software available 

not because they believe that this type of software is the most appropriate for their 

students. Alternatively, teachers may profess equity in attitudinal surveys but actu- 

ally use practices that are inequitable. Unfortunately, studies that examine both 
attitudes and behaviors related to equity in computer use are exceedingly rare. Thus, 

one has little direct information about how often behaviors and attitudes are consis- 

tent and how often they are inconsistent. 

482   
   



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Equity and Computers in the Schools 

By the late 1980s, teachers were aware of the inequalities in access. A nationwide 
poll of over 1,000 teachers conducted in 1989 found that 90% of the teachers felt that 

not having access to computers could put students from less affluent schools at a 

disadvantage (“Computer Report Card,” 1989). Knupfer (1988), in a survey of 6th- 

grade teachers in Wisconsin, asked teachers to devise criteria for access to a limited 

number of computers in a school. The first choice by the largest number of teachers 
(37%) was equal access. However, these beliefs coexisted with views that, if imple- 

mented, would hinder access. For example, one quarter of the teachers said that 

better behaved students should have a better chance of using a computer. 

Several surveys focusing on attitudes towards type of use for low-achieving and 
high-achieving students reflect the type of use shown in the data presented above. 
Cosden (1988) reported that the overwhelming majority of elementary teachers 

thought that mastery of basic skills was the major benefit of computer use. A 

significantly higher proportion of these same teachers believed that low achievers 
benefited the most from computer use compared to high and average achievers. 
Chan (1989) found in a survey of Canadian elementary school teachers that nearly 
three quarters believed that drill and practice was effective in teaching students of low 

ability, but the responses were mixed on whether bright students were suited to drill 
and practice. 

Many studies have documented that sexism in society is reflected in classroom 
interactions and teachers’ attitudes and behaviors in schools (e.g., Sadker & Sadker, 

1986; Shakeshaft, 1986), particularly in mathematics classrooms (Brophy, 1986). It is 
expected that these attitudes and behaviors continued into classes using computers, 
especially in the early phases of the introduction of computers when they were 
associated predominantly with programming and, therefore, mathematics. Culley 

(1988), in a rare study that included observations and attitude assessment in the 

United Kingdom, reported that 11-12-year-old boys were frequently regarded as 
more interested in computing and more rewarding to teach than girls. Even when it 
was observed that girls did everything that was asked of them and presented good 

work, they were still seen by some teachers as having less of a flair for programming. 

Curriculum 

Whereas many writers concerned with equity and equality have not considered 
curriculum content (e.g., Brookover & Lezotte, 1981; Coleman et al., 1966), Secada 

(1989) argued that it is important to scrutinize the knowledge that is being distributed 
among students. This scrutiny involves some qualitative judgments about what is 
inherently unjust—such as, the biology texts of the 1940s which contained eugenics 
material (Selden, 1989). 

Curriculum concerns related to equity focused on two areas in the 1980s: photo- 

graphs in computer magazines and textbooks and software design. Three studies 
examined the pictures of large-circulation, popular, computing magazines in the 
early to mid-1980s. Ware and Stuck (1985) found that only 31% of the illustrations in 
three different magazines included women or girls. In addition, women’s roles in the 

pictures, compared to men’s were much more likely to be passive than active. They 
frequently appeared in the role of clerical worker or sex object. Sanders (1985) found 

in an analysis of four computer magazines that only 17% of the photographs con- 

tained females actively involved in using a computer. In addition, only 12% of the 

bylined articles during that period were written by women (12% of the articles could 

483



    

Sutton 

not be gender identified). Demetrulias and Rosenthal (1985) examined 175 issues of 

15 general and educational computing magazines for gender and racial bias in 
photographs. They found that the most common photograph contained a lone male 

(59% of all advertisements), and, in 90% of these advertisements, the male was 

White. 

Most elementary and middle-school students would not read these magazines, but 
parents, teachers, and interested high-school students would. An analysis of text- 

books for computer studies in the United Kingdom (Culley, 1988) and Australia 

(Hattie & Fitzgerald, 1987) showed that the majority of the photos depicted males 

making decisions, supervising, controlling robots, and fixing or using computers 

whereas the females were portrayed passively—for example, handling input and 

output. 

This research, while simple in nature, does portray the degree of stereotyping of 

the computer industry in the early and middle 1980s. The new, “intelligent” ma- 

chines were obviously the domain of White males. This was, of course, before the era 

when the typewriter on the secretary’s desk had routinely been replaced with a 

computer workstation which not only contained a word processing package but also 

databased-management and spreadsheet programs. 
A number of authors has discussed the gender stereotyping of software. Most 

software, especially in the early days of development, was game-like with much 

aggression and violence. This violence was part of the task (e.g., the student “shoots” 
the correct answer) or was part of the reward for a series of correct answers. It was 

suggested by many authors, before there was supporting research data, that this 
content was inherently unappealing to girls (e.g., Lockheed, 1985; Rosenthal & 
Demetrulias, 1988; Sanders, 1984; Schubert, DuBois, & Wolman, 1985). This 

assumption was based, in part, on a generalization from the then current situation 

with video games: The games were very violent, written and designed by men, and 

the video arcades were virtually all-male domains (Kiesler, Sproull, & Eccles, 1983; 

Loftus & Loftus, 1984). There was evidence that video games were liked by boys 

more than by girls (Wilder, Mackie, & Cooper, 1985). In addition, one study found 

that, when educators with programming experience were asked to design software 

for boys, girls, or students, programs for boys and students were classified as 
“games” whereas programs designed for girls were classified as “learning tools” 

(Huff & Cooper, 1987). 
There is limited quantitative data to support the belief that software content was 

gender biased. Sherman, Divine, and Johnson (1985) found no gender differences in 
the choice of problem-solving versus drill-and-practice software for preschool chil- 
dren. Sanders (1985) asked junior-high school students which of three programs they 

liked more: an action shooting program (Mastertype), a problem-solving program 
requiring manipulating machines (The Factory), or a language arts cloze program 

(Missing Links). Girls liked the language arts program more than boys, but there 

were no differences in liking between the other two programs. Cooper, Hall, and 
Huff (1990) reported that both boys and girls liked the “male” program (Demolition 

Division) more than the “female” program (American Classroom Fractions). They 

also assessed students’ stress when using the program in a private, versus a public, 
setting. For both boys and girls, stress increased when using the cross-gender pro- 

gram in the public context of the computer center compared to working in more 

privacy. This suggests that, while there are few gender differences in overall liking of 

484   
   



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Equity and Computers in the Schools 

different types of software, it may be fruitful to explore gender-rel 
other domains such as self-awareness, ex 
(Cooper, Hall & Huff, 1990). 

These three studies confounded curriculum content with software design. Malone (1981) controlled content and explored gender differences in eight different kinds of feedback for an educational game called Darts, which was designed to teach students about fractions. He found that Sth-grade girls did not like the fantasy of a dart (actually an arrow) bursting balloons but they did like the option which included music more than boys did. It has frequently been reported that girls like music in software more than boys (Sanders & Stone, 1986). 
Decisions about whether software is equitable involve judgments that cannot be assessed quantitatively. The language should be free from gender, race, and cultural bias; women and minorities should be proportionately represented in text: contribu- tions of all racial and ethnic groups should be presented in a cquitable manner (Rose, 1984); a Eurocentric view should not predominate. 
The concern for the assumed gender bias in software content led some software designers to devise special “girl” software. The intent was software that would tap into girls’ interests and complement the way that girls like to learn. While some titles portrayed females as active problem solvers and were moderately successful {c.g., Jenny of the Prairie), other attempts resulted in software that was less interesting (Brady & Slesnick, 1985) or that reinforced female stereotypes by focusing on such topics as visiting a shopping mall, dating, interior decorating, or housework (Sanders & Stone, 1986). There were some obvious Eurocentric biases in carly software. For example, the early edition of the simulation, Oregon Trail, involved unprovoked attacks on the White settlers from American Indians. It has been argued that the sound, music, and opportunity to express creativity that is part of some software is a good match for an African-American learning style (Lee, 1986), but, unfortunately, there is no research evidence to support this assertion. Even so, at Icast one model African-American program, Visions for Children, uses computers as an integral part of its preschool program (Hale-Benson, 1990). Some Spanish language versions of programs also exist—for example, Voy a Leer Escribiendo is the Spanish adaption of Writing to Read, a literacy program for kindergarten and first-grade students. Cur- rent writing on language minority children using computers stresses the importance of the quantity and type of language interaction among students rather than the relationship between learning styles of the students and software (e.g., DeVillar & Faltis, 1991). 

ated differences in 
pectations of success, and cgo involvement 

Student Interactions 

Several aspects of possible inequalities in student interactions in computer-related learning received attention during the 1980s. First, it was hypothesized that girls spent less time on computers than boys, because boys in their enthusiasm crowded computer rooms and clubs and girls were unwilling to enter an all-male domain (Lockheed, 1985; Sanders, 1985). Sanders (1985) found from a survey of junior-high girls that it was not the presence of boys that they objected to but rather the absence of their friends. Sometimes, the enthusiasm of boys to get to the computer resulted in verbal and physical aggressive behavior, which discouraged girls from gaining access (Culley, 1988; Lipinski, Nida, Shade, & Watson, 1986). These problems are obvi- ously more likely to occur in unstructured or semistructured situations. A teacher 

10C



   
Sutton 

who structures activities well and rewards cooperative behavior can monitor the 

amount of sex-segregated and aggressive interactions that occur around computers 

(Dickson & Vereen, 1983; Sanders & Stone, 1986). 

The second area of interest was the effect of working alone at a computer or ina 

group. Many early visions of microcomputer use in classrooms involved one child 

working at a computer, and the presumed benefits of individualized instruction were 

emphasized (Bork, 1987; Cuban, 1986; Johnson & Johnson, 1986). This led to 

concerns among some educators that interactions among students would be mini- 

mized and that cognitive skills would be emphasized at the expense of social skills 

(Webb & Shavelson, 1985). However, as with many technological innovations, the 

predictions of social and educational impact were not accurate. Because of a shortage 

of hardware, students frequently worked in groups rather than individually at a 

computer, and it was found that more social interaction occurred among students 

when they were working with computers than when working without them (Hawkins, 

Sheingold, Gearhart, & Berger, 1982). Also, new forms of student-to-student and 

student-to-teacher interactions developed with classroom use of this medium (Pease- 

Alvarez & Vasquez, 1990; Sheingold, Hawkins, & Char, 1984). 

Webb (1985) explored whether working alone or in a group differentially affected 

the performance of middle-class, White, junior-high girls and boys when learning 

BASIC. No significant differences in programming skill were found. Guntermann 

and Tovar (1987) found that the size of the group (two vs. three) was not related to the 

performance of fourth-grade Canadian girls and boys of mixed socioeconomic back- 

ground learning to program in LOGO. In contrast, Mevarech, Silber, & Fine (1991) 

reported that low-SES, 6th-grade Israeli students who worked cooperatively in pairs 

on mathematics programs scored higher than those working individually on the same 

program. In addition, the cooperative condition alleviated the math anxiety of the 

low-ability students more than the individual condition. In these three studies, the 

‘criterion for success was individual student achievement. In contrast, Cox and Berger 

(1985) tested whether the number of science problems solved and the time taken to 

solve each problem varied when there were one, two, three, or five middle-class, 

junior-high students working on a computer. They found that students solved signifi- 

cantly fewer science problems when working individually than when working in any- 

sized group, but there was no difference in the time taken to solve problems. 

A third area of interest was group composition. Webb, (1984a) in a study involving 

mathematics rather than microcomputer use, found significant differences in the 

interactions of all-female, all-male, and mixed gender groups. In particular, in 

majority-male groups, males tended to ignore females and showed higher achieve- 

ment than females. In majority-female groups, females directed most of their inter- 

actions to males and showed lower achievement than males. When this study was 

replicated in a computer setting (learning LOGO or BASIC), quite different results 

were found (Webb, 1984b). Group composition had little effect on achievement or 

interactions. In the majority-male groups learning LOGO, females were quite suc- 

cessful in obtaining explanations and time at the keyboard. Webb (1984b) suggested 

the differences could be due to either the microcomputer setting or the small, select 

sample (both studies involved middle- and upper middle-class students, and the 

microcomputer study had a sample size of only 35). The microcomputer setting 

seems the more plausible explanation, because consistent results were reported in 

three studies using subjects of differing ages, ethnic origins, and socioeconomic 

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status. These studies examined 36 fourth-graders of mixed socioeconomic back- 
ground learning LOGO (Guntermann & Tovar, 1987); 82 third- through sixth-grade, 
predominantly Hispanic students using vocabulary and problem-solving software 
(Merino, Legarreta, Coughran, & Hoskins, 1990); and 125 predominantly minority 
(approximately half African-American and half Hispanic) urban high-school stu- 
dents using the computer as a tool in a variety of curriculum areas (Feldman, Fish, 
Friend, & Bastone, 1989). 

A fourth area of research has been the structure of rewards (cooperative, competi- 
tive, and individualistic) within the groups. Johnson, Johnson, and Stanne (1985), 
using a sample of 51 fourth graders from a midwestern inner city, found that in the 
competitive condition, compared to individual or cooperative conditions, girls per- 
formed less well than boys, were less motivated, liked computers less, were less 

confident in their ability to work with computers, and perceived less personal support 
from their teachers. This finding is consistent with noncomputer-related research 
(e.g., Peterson & Fennema, 1985). Unfortunately, 1 did not find any research on 
student interactions while using computers that systematically examined the role of 
race/ethnicity and social class. 

Equality of Outcomes 

Assessments of school-related inequality typically focus on outcome measures 
(Brookover & Lezotte, 1981; Coleman et al., 1966; Winfield, 1986). In this section, 

equality in three types of outcome are summarized: student attitudes, computer- 
related learning, and traditional school achievement measures using computer-aided 
instruction. 

Student Attitudes 

At the rate computer technology changes, attitudes towards the technology may be 
as important as skills with a specific machine. Individuals who are not anxious, but 

rather view computers positively and are confident about their ability to use com- 
puters, will be more likely to learn whatever new skills are required by future 
developments in hardware and software. 

Much research on attitudes towards computers and gender differences was con- 
ducted during the last decade (see Table 2). Following the research on gender role 
stereotyping of mathematics (e.g., Eccles et al., 1985; Fennema & Sherman, 1978; 

Sherman & Fennema, 1977), a number of investigators explored students’ attitudes 
about the computer as a male domain. All the studies, except one with a very small 
sample size, found that males held more stereotyped views than females (sce Table 
2). The students in these studies ranged from first graders to high-school students and 
were living in Canada, Britain, or the U.S.A. Thus, this stercotyping by males is very 
widespread. One study examined differences in gender-stereotyping of White and 
minority students (predominantly African-American) and found that minority high- 
school students tended to more strongly stereotype computers as a male domain than 
did White students (Campbell & Perry, 1989). 
Gender differences favoring males in general attitude, interest in computers, 

liking of computers, utility/necessity of computers, and confidence in using com- 
puters have been reported in some studies but not in others that found no significant 
differences. Again, no study reported more positive attitudes for girls (see Table 2). 
These discrepancies in results may reflect between-school gender differences also 
found in the type of data used (see above). 

487



   
  

  

  

TABLE 2 

Summary of research on gender differences in attitudes 

Attitude Study Grade N Significance 

Computer as a male domain Arenz & Lee, 1990 High school 328 X 

Campbell, 1990 High school 171 4 

Chen, 1986 High school 1,138 * 

Colbourn & Light, 1987 6th, 7th 56 * 

Collis, Ollila, & Ollila, 1990 1st 91 ’ 

Eastman & Krendl, 1987 8th 80 y; 

Hattie & Fitzgerald, 1987 Sth, 6th, 8th 1,868 * 

Johnson, Johnson, & Stanne, 1985 8th 71 x 

Martinez & Mead, 1988 3rd 24,0008 RE 

7th + 

Richards, Johnson, & Johnson, 1986 7th 370 * 

11th 95 * 

Smith, 1987 1st—12th 318 * 

Swadener & Hannafin, 1987 6th 3 

General attitude Collis & Williams, 1987 8th, 12th 1,818 * 

Eastman & Krendl, 1987 8th 80 0 
Griffin, Gillis, & Brown, 1986 4th, Sth 401 4 

Levin & Gordon, 1989 8th-10th 222 ’ 

Swadener & Hannafin, 1987 6th 32 0 

Interest in computers Chen, 1986 High school 1,138 * 

Colbourn & Light, 1987 6th, 7th 56 0 

Krendl, Broihier, & Fleetwood, 1989 4th—10th 317 x 

Miura, 1986 6th—8th 400 y 

Pulos & Fisher, 1985 7th 185 0 

Richards, Johnson, & Johnson, 1986 7th 370 0 
11th 95 0 

Liking of computers Loyd & Loyd, 1988 7th-11th 777 0 

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Liking of computers Loyd & Loyd, 1988 7th-11th 777 0 
Johnson, Johnson, & Stanne, 1985 8th 71 X 
Richards, Johnson, & Johnson, 1986 7th 370 0 

11th 95 0 
Utility/necessity Campbell, 1990 High school 171 yi 

Griffin, Gillis, & Brown, 1986 4th, Sth 401 + 
Hattie & Fitzgerald, 1987 Sth, 6th, 8th 1,868 + 
Krendl, Broihier, & Fleetwood, 1989 4th-10th 317 0 
Richards, Johnson, & Johnson, 1986 7th 370 * 

11th 95 0 
Swadener & Hannafin, 1987 6th 32 0 

Confidence in using Chen, 1986 High school 1,138 » computers Collis & Williams, 1987 8th, 12th 1,818 : 
Johnson, Johnson, & Stanne, 1985 8th 71 0 
Krendl, Broihier, & Fleetwood, 1989 4th-10th 317 * 
Loyd & Loyd, 1988 7th-11th 277 0 
Smith, 1987# 1st-12th 318 0 
Swadener & Hannafin, 1987 6th 32 0 

Anxiety Campbell, 1990 High school 171 * 
Chen, 1986 High school 1,138 * 
Hattie & Fitzgerald, 1987 Sth, 6th, 8th 1,868 + 
Loyd & Lovd, 1988 7th—11th 777 ¥ 
  0 No gender differences 

Males higher on scale but not significantly different 
* Males significantly higher on the scale 

Confidence and attitudes towards society impact combined 
Sample size for grades combined 

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Few studies examined ethnic differences in attitudes. Griffin, Gillis, and Brown 

(1986) found White children had more positive attitudes than minority children 

(predominately Hispanic). Collis and Williams (1987) found that Chinese high- 

school students were more positive in their attitudes towards computers when 

compared to Canadian students. Also, there were fewer gender differences among 

Chinese students in attitudes towards their computers. 
The gender differences in attitudes may be caused by many factors (Lockheed, 

1985). However, differences in experience appear to be a major cause. When expo- 

sure to computers is controlled for, gender differences in attitudes reduce or com- 

pletely disappear (Chen, 1986; Levin & Gordon, 1989). Gressard and Loyd (1987) 

found that gender did not explain any additional variance in attitudes towards 

computers after computer experience and mathematics anxiety had been entered 

into the regression equation. Campbell (1989) found that there were no gender 

differences in computer anxiety of rural students when effects due to computer access 

were controlled. Other studies on attitudes and experience have also found that more 

experience is associated with better attitudes (Arenz & Lee, 1990; Lever, Sherrod, 

Bransford, 1989; Loyd & Loyd, 1988) and less anxiety (Loyd & Gressard, 1984). 

A study of Spanish-speaking migrant children, which compared a group who had 

received supplementary computer-aided instruction (CAI) to a control group, found 

a conflicting result: Students who were in the non-CAl program had more favorable 

attitudes towards CAI than did those students in the CAI program (Saracho, 1982). 

Saracho suggested several possible explanations: Learning style differences of the 

migrant children may be different from those of the middle-class Anglo subjects who 

have been found to be more favorable after exposure to computers; students within 

the CAI program may have felt stigmatized as remedial and thus rejected the 

‘program; the non-CAI group may have wished to participate in the innovative 

program and thus glorified it. This is an important study because of the scarcity of 

research on computer use with language minority children. Other, more recent 

research with Hispanic children using computers has reported, in contrast, great 

enthusiasm (Chavez, 1990; Merino et al., 1990). However, in these studies, compari- 

sons were not made between the attitudes of those using computers and those not 

using computers. : 

Computer-Related Competence 

Two areas of computer-related competence have been studied extensively: com- 

puter literacy and computer programming. 

Computer literacy. Gender differences were found in computer competence in 

7th-grade and 11th-grade students (Lockheed, Thorpe, Brooks-Gunn, Casserly, & 

McAloon, 1985; Martinez & Mead, 1988) and in computer literacy in 12th-grade 

students (Fetler, 1985). Not surprisingly, students who had more experience using 

computers were more competent in their knowledge about computers (Martinez & 

Mead, 1988) and in their use of computers (Kersteen, Linn, Clancy, & Hardyck, 

1988; Martinez & Mead, 1988). This experience may be gained at home or at school, 

and one study reported that pupils who had computers at home believed that more of 

their learning about computers was done at home than at school (Martinez & Mead, 

1988). These data on gender differences in literacy should be interpreted cautiously 

because the differences in the two studies (Fetler, 1985; Martinez & Mead, 1988), 

while statistically significant, were not large. In one instance, the authors themselves 

questioned the practical importance of the findings (Martinez & Mead, 1988). 

Computer programming. Several studies examining gender differences in pro- 

gramming in BASIC were conducted. No gender differences were found in program- 

490 
  

   



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Equity and Computers in the Schools 

ming competence in middle-school/junior-high school students (Linn, 1985b; Linn & Dalbey, 1985; Webb, 1985) and high-school students (McCormick & Ross, 1990). However, access to computers, both in and out of school, influenced performance (Linn & Dalbey, 1985). Also, quality of instruction affected different groups of students differentially. M. C. Linn (1985c) found that the difference in programming achievement between high-ability students and their less able peers was smaller for students who had received “exemplary” instruction compared to students who had received “typical” instruction. That is, general ability was a much stronger predictor of programming success under typical instruction than under exemplary instruction. Many research studies were conducted which examined the process of learning LOGO (e.g., Fay & Mayer, 1987; Kurland & Pea, 1985; Turkle, 1984) and the cognitive consequences of learning LOGO (e.g., Clements, 1987, 1990; Mayer & Fay, 1987; Pea & Kurland, 1984), but few of these examined equity issues. Most of these studies had very small sample sizes so a consideration of gender, race, and/or social class differences would have been very difficult. The literature on LOGO and gender differences considered differences in programming achievement as wel] as differences in programming style. 
Several studies found no gender differences in m 

Webb (1985) examined gender differences in plan 
in group processes and achievement among junior-high school students learning in small-group and individual settings. No gender differences were found in achieve- ment in either setting. Two studies used preschool samples. Schaefer and Sprigle (1988) examined LOGO-programming mastery of preschool children, and Camp- bell, Fein, Scholnick, Schwartz, and Frank (1986) studied kindergarten children’s coding of Instant LOGO commands. Both studies found no significant gender differences. In contrast, Block, Simpson, and Reid (1987) reported gender differ- ences favoring boys in a sample of kindergarten, first-grade, and second-grade children. Unfortunately, they did not report if these gender differences occurred at each grade level. In addition, a long-term study at Bank Street in New York City found boys outperforming girls in programming commands, program composition skills, and debugging (Hawkins, 1987). 
One chapter in the book, The Second Self: Computers and the Human Spirit (Turkle, 1984), was devoted to describing middle-school children learning LOGO. Turkle identified two programming styles she called soft mastery and hard mastery. Hard mastery, more typical of boys, involves the imposition of will over the m through the implementation of a plan. A computer program is the instrument of premeditated control. Soft mastery, more typical of girls, is more interactive. The programmer tries something, waits for a response, then tries something else. Soft mastery is the mastery of an artist whereas hard mastery is the mastery of an engineer. Both approaches can result in children’s discovering new ide creative programs; it is the approach that is different, not over 

Campbell et al. (1986) also found gender differences in progr 
programming mastery of kindergarten children’s understa 
Instant LOGO. 

At least two studies examined ethnic differences in learning LOGO. Emihovich and Miller (1988) compared African-American and White first-grade children who were assigned either to a LOGO, CAL, or control group. A signific 
group by ethnic origin was found for the dependent variable of ma 

astery of programming in LOGO. 
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491



     Sutton 

While the White children in the CAI and control group outperformed the African- 

American children, this trend was reversed for the LOGO group with African- 

American children outperforming White children. This result is particularly interest- 

ing, because the design was confounded by social class: The White children came 

from middle-income homes, and the African-American children came from low- 

income homes. The authors suggested that LOGO builds on strengths of African- 
American students ‘such as high responsiveness to visual and auditory stimuli and 

desire to collaborate and share their ideas” (p. 478). Replication of this study is 

important because the sample size was small (36). 
One study conducted in Zimbabwe (Mundy-Castle, Wilson, Sibanda, & Sibanda, 

89) examined gender and ethnic differences of 11-12-year-old middle-class chil- 
dren after 11 months of LOGO. A significant interaction of gender by ethnic origin 
by experimental condition was found for the dependent variable of spatial reasoning. 

Black girls who had been in the LOGO group showed particularly large gains on the 

WISC-R Block Design scale. 

Computer-Aided Instruction 

The purpose of much of school computer use during the second half of the 1980s 

was not to learn about computers or programming but to enhance writing, reading, 
math, or problem-solving skills (Becker, 1986, 1990; Brady, 1991). Many studies 
were conducted to determine the effectiveness of computer-aided instruction by 

comparing two groups of students: one that received CAI and one that received 

traditional instruction (for meta-analyses of this research see Bangert-Drowns, 
Kulik, & Kulik, 1985; Niemiec & Walberg, 1985; Samson, Niemiec, Weinstein, & 

Walberg, 1986). These findings suggested that CAl is particularly beneficial to low- 
achieving pupils and to males (Bangert-Drowns et al., 1985; Niemiec & Walberg, 

1985; Samson et al., 1986). 

However, this type of research design was severely criticized on several grounds 

(Clark, 1985a, 1985b; Salomon & Gardner, 1986). When the same teacher taught the 

CAI and the traditional group, the difference between the two groups was reduced, 

and studies of longer duration showed less advantage to CAl, suggesting the impor- 

tance of the novelty effect (Clark, 1985a). The most important criticism, however, is 

that the medium of instruction is being compared—not the design features of the 

instruction. That is, this research cannot not tell one what it is about CAI that is 

effective (or ineffective), and any effectiveness may be due to aspects of the instruc- 

tion that are not intrinsic to the medium (Clark, 1985a). For example, perhaps the 

software sequences instruction better than the teacher, provides feedback more 

, promptly, or allows for more student interaction. These features are not unique to 

computer software: A teacher could change his or her instruction to be more effective 

in any of these ways. In addition, students react differently to software and gain 

different information from software packages (or any kind of instruction). It is 

important, therefore, to study how learners actually handle a program (Salomon & 

Gardner, 1986), and, for the purposes of this review, itis particularly important to see 

if different reactions to programs are related to gender, race, and social class. 

Hativa (1988), interested in the effectiveness of CAI for low and high achievers, 

observed in detail the strategies high-and low-achieving elementary students used to 

effectively work through a tutorial containing material not yet studied in class. Low 

achievers, more than high achievers, were prone to make mistakes associated with 

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Equity and Computers in the Schools 
hardware and software and were less able to adjust to the special environment of computer work and derive great benefits from it. Other rescarch has indicated that many students need teacher intervention when students are using complex software (Stein & Linn, 1985; Sutton, 1987). Mandinach and Corno (1985) studied the cognitive engagement processes used by males and females and high and low achievers in a computer problem-solving game. High-ability students were more successful because they used sclf-regulated learning rather than recipient learning. Females were more likely than males to adopt and maintain only one type of engagement throughout the entire problem solving. Much more research on the features that make a software package and classroom interaction effective for poor, minority, and female students is needed. It has been found that, in traditional classrooms, low-SES students necd more active instruction, more feedback, more warmth, support, and encouragement (Brophy, 1986). Do these same characteristics apply to computer-aided classrooms? If yes, can they be incorporated effectively when using the computer as an instructional tool? For example, can tutorials be devised that display empathy (Lepper & Chambray, 1988)? Can teachers provide active instruction when much of the instructional timing, content, and sequence is controlled by the computer? 

L5 years). The goals were to develop computer literacy and critical thinking skills and to create interest in further classes in science and technology. Teachers were given extensive training and technical sup- port. Students in the project gained significantly in computer literacy and problem solving compared with those students not in the project (Edwards, 1989, 1990). PALS (Principles of the Alphabet Literacy System), a structured CA]J program, was developed to alleviate adolescent illiteracy. Using videodiscs, touch screens, and headphones, students learned the written alphabet and associated sounds and word processing and touch typing in preparation for the final phase of personal writing. peal of the program, and an evaluation of a pilot program found students progressed three grade levels in 20 weeks (Karlstein, 1988; Stone, 1987). 
Both of these 

In-service training based on 
Stone, 1986). The girls in 

ategies used the computer 
true in the control schools. 

significantly more than the boys in these schools. This was not this study did not examine achievement measures, 
Unfortunately, 

Implications From a Decade of Research 
Practices Maintained and Exaggerated Inequities in Education Coleman (1977) argued that a reasonable appro ach to the concept of “an equal educational opportunity” js:



   Sutton 

  

public schooling that leans in the direction of equal adult opportunities. Such a 

formulation implies that public schooling is to reduce handicaps that children face as 

a function of their early environments. . . . (p. 28) 

The evidence indicates that computer use during the 1980s did not bring education 
closer to equal educational opportunity. Rather, it maintained and exaggerated 
existing inequities in education in input, processes of computer learning, and output. 
Poor, minority, and female students had less access to computers at home and, in 
addition, less access to computers at school. While the inequalities appeared to be 

larger at home, especially for females, school inequalities exacerbated the experien- 
tial differences children brought to school. Poor and minority students were more 
likely to use computers for drill and practice than were middle-class and White 

students, and females outnumbered males in word processing but were underrepre- 

sented in programming. Teachers, while concerned about equity, held attitudes 

which hindered access: They believed that better behaved students deserved com- 

puter time and that the primary benefit of computers for low-achieving students was 
mastery of basic skills (i.e., the use of drill and practice). The photographs in 

computing magazines and textbooks indicated that active computer use was a pre- 

dominantly White male activity. Output measures also reflected inequalities: Com- 

puters were perceived as a male domain (especially by boys); computer literacy skills 

and positive attitudes towards computers were associated with experience, and 

middle-class White boys had more experience. Thus, children who were minority, 
poor, female, or low achieving were likely to be further behind after the introduction 
of computers into schools (Chambers & Clarke, 1987; Sutton, 1989). These inequal- 
ities were found in the U.S. A., Great Britain, Australia, Canada, and New Zealand. 

Equity Issues Are Complex 

The decade of research has demonstrated the complexity of equity and equality 
issues in school computing. In the early 1980s, the primary focus of equity was 

equality of access. For example, Mathews and Winkle (1982) defined equity as 

“equal access to computer literacy for each person” (p. 1). Now, one knows that 

equality assessments can be made in output measures including attitudes, computer 

literacy, computer programming, and achievement measures when using the com- 
puter as an instructional aid. In addition, process assessments including type of 
computer use, teachers’ attitudes, students’ interaction and curriculum content must 
also be considered. There are related topics not discussed in this review because of 
the lack of research. For example, research on gender differences in mathematics has 
provided a wide range of variables to consider, including: novelty versus familiarity 
(Kimble, 1989), autonomous learning behaviors (Fennema & Peterson, 1985), suc- 

cess and failure attributions (Stipek, 1984), and parental expectations (Eccles & 
Jacobs, 1986). A recent article by Kimble (1989), on women’s mathematics achieve- 
ment, addresses the complexity of the problem in a field with a longer history than 
school computer use. 

More sophisticated understanding of the problems, after a decade of research, 
should lead to a change in research emphasis. Descriptive research which only 
examines gender or ethnic/racial differences in access, type of use, or competence is 
of dubious value in the 1990s and beyond. The inequalities are known. The future 

task should be to understand the complexities of the problems better and, therefore, 

to be able to devise better intervention strategies. This may involve qualitative 

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Equity and Computers in the Schools 
research, which would provide detailed descriptions of classroom interactions, or quantitative multivariate research, which would include simultaneously the status variables: race, class, and gender as well as input, process, and output variables. Much more of the research should be theoretically based so one can understand the causes rather than just describing the problems. Research from mathematics will help inform, but there are important differences, and these differences may be growing over time. The increasing use of word processors, graphics tools, and databases is making the computer less associated with mathematics. 

because the funding is primarily for the intervention and because the program organizers are not researchers (Sutton & Fleming, in press). With the current and anticipated funding problems in education, it is essential that money is not wasted on ineffectual programs and that one understands which components in successful programs are beneficial and which are not (Campbell, 1990). 

ularly intriguing results. Emihovich and Miller (1988) reported that low-income African-American students outperformed middle-class, White students when learn- ing LOGO, and Mundy-Castle et al. (1989) found that middle-class, Zimbabwean 

warranted. Another interesting area of research may be African-American learning styles and software design. 
One should also be concerned about some issues of practice. Much current attention is being paid to reforming urban education, and technology is often sug- gested as one of the panaceas. The high ievels of use for drill-and-practice software for minority and poor students, the widespread availability of relatively cheap drill- and-practice software, and the ease of integrating this type of software into the curriculum make this an easy “solution.” An overemphasis on drills will not help



  

   Sutton 
inequities in school computer use. The meta-analyses suggest that CAl is particularly 

beneficial for underachieving and male students, but this should not be interpreted to 

mean that the majority of computer-related activities should be drill and practice. 

Recent theory and research suggest that children who learn with difficulty may 

particularly benefit from instruction that focuses on conceptual understanding and 
solving novel problems (Doyle, 1991; Porter, 1991). 

The use of technology in urban schools has potential. Some students are inspired 
by the use of technology. A study conducted in 1970 with poor, middle-school, 

predominantly Mexican-American students reported that the computer was per- 

ceived to be fairer, easier, clearer, more likeable, and better than the teacher (Hess & 

Tenezakis, 1970). More recent anecdotal reports confirm this: Students state that 
they like the computer because “it never gets angry” and “it is fair.” In addition, 
research has found that using computers can provide a collaborative environment 

where students of different levels of language proficiency interact meaningfully 
(Gonzilez-Edfelt, 1990). However, strong existing stereotypes that urban children 
need drill on the basics before they are capable of getting to higher order thinking 

skills or problem solving suggest the potential danger in computer use in urban 

schools (Laboratory of Comparative Human Cognition, 1989). 
As researchers, there is much to be done. We need much deeper understanding 

about the complexities of inequities in computer use in schools. This is a much harder 
task than descriptive research on existing problems. We need to conduct more 

research which examines simultaneously race, class, and gender, and this is also more 

difficult. We need to examine which intervention programs work and which elements 
of these successful programs make them work. The task is compounded by the 
changing use of the technology over time, but the decade of the 1980s provided a 

large research base on which to build the next decade of research. 

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Author 
ROSEMARY E. SUTTON is 

University, Cleveland, OH 
teachers in training. 

Associate Professor, College of Education, Cleveland State 44115. She specializes in equity and technology and urban 

nn