For many students, a solid foundation in learning is established in the early stages of education, whether it be at home or in school. Unfortunately, access to computing is also at its greatest disparities in these stages. Children from poor families often have less access to computing technologies than their counterparts from more affluent families. In most of these situations, computer equipment is not only absent but parents themselves do not know or understand how to operate them.

Fortunately, once a child begins school the prospects of gaining computer access typically increase. Approximately ninety-eight percent of all schools in the United States own computers, eighty-five percent of which have multimedia capabilities and sixty-four percent have access to the Internet. It is no wonder that the average student-to-computer ratio these days is at an all time low of 10-to-1.

However, students attending poor and high-minority schools still have less access to computer technology than students attending other schools because school districts in these areas often have smaller budgets and less community sponsorship. Hence, less money is spent on updating classes with the latest technology, fewer qualified teachers are present, and course offerings in computer literacy may be sparse.

Of course many students can go through elementary and secondary schools without learning a considerable amount about computers. Even those who have taken classes in computers usually find that their skills are hardly substantial, especially for those aspiring to become computer scientists. For these people, what ultimately determines success in higher education is the strength and caliber of the student's training in disciplines adjacent to computer science, such as mathematics and the sciences. Afterall, these subjects can establish foundations in logic and problem solving skills that all computer scientists will need in the course of their study.

But studies indicate that racial differences in science/math course taking patterns are well pronounced. Black and Hispanic students, especially, are far less likely than white students to have taken advanced courses in science and math, let alone computer science. These disparities eventually manifest themselves in math/science achievement. Data collected by the National Assessment of Educational Progress (NAEP) has shown that median test scores for black and Hispanic students at three age levels--9, 13, and 17--are lower than the 25th percentile scores of white students. Such a statistic can easily translate to predicting potential achievement in computer science, putting minorities and lower-income class members at a tremendous disadvantage when they finally do get the chance to study the field.

The teacher expectations imposed upon a student can also have profound effects on a student's achievement in these areas. Researchers have found that teachers tend to have different expectations of students in various socieconomic groups. For instance, teachers in "high-ability" classes are more likely to emphasize the development of reasoning and analytical skills than those in "low-ability" classes. Teachers in these "high-ability" classes are more liekly to concentrate preparing students for further study in science and mathematics. On the other hand, teachers in "low-ability" classes are more likely to read directly from textbooks and prepare students for standardized tests, and are less likely to involve their students in hands-on scientific activities. Statistically, ability groupings are inversely proportional to the number of minorities enrolled. Only 9 percent of all classes with at least 40 percent minority enrollment are labeled "high ability". On the other hand, 42 percent of all classes with high proportions of minorities are labeled "low-ability" classes. What this means is that many minorities are not receiving the kind of education that will prepare them for careers in computing.

An indirect consequence of ability grouping in schools is that often times minority students have less access to qualified teachers, as many able teachers choose to teach in "high-ability" classes predominantly composed of middle-income to higher income students. In fact, mathematics classes with a high proportion of minorities are less likely than those with a low proportion of minorities to have mathematics teachers with degrees in the field. Again, students in these categories will be seriously affected when it comes time to compete with students who had better educators. Of course, these problems may affect students equally regardless of academic interests, but the situation is likely to be more pronounced in especially rigorous fields such as computer science.

The conclusion that can be made from all these characteristics is that schools are doing very little to destroy the barriers to computing minorities and lower-class students face. Rather than reinforcing cerebral skills and supplementing deficiencies in computer training, they seem to only perpetuate the conditions which will later present challenges to students in these minority groups attempting to become computer scientists. Elaine Seymour, a sociologist at the University of Colorado, Boulder, says that many underrepresented minorities simply do not get an "adequate basic education, let alone have access to computers." Furthermore, they do not get a chance to develop a high sense of self-esteem in highly rigorous scientific areas where the knowledgeable have extremely overpowering behaviors, a problem which can significantly contribute to decreased participation rates. So in computer science, as in other scientific fields and mathematics, minorities tend to "drop out as quickly as they come in."

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