Promoting the Development of Talent in Technical Areas: Obstacles to ... Technical Careers in Europe, Scandinavia Countries, Asia, and the United States.
Promoting the Development of Talent in Technical Areas: Obstacles to Females Pursuing Technical Careers in Europe, Scandinavia Countries, Asia, and the United States. Gender Inequity Among Academic Olympians Across the Globe: Theoretical Paradigms James Reed Campbell St. John's University This article introduces the theme for this issue. The articles that follow examine how parents and teachers nurture or hinder the development of talent. Figure 1 illustrates our conceptualization of the interacting forces that contribute to the development of talent. Certainly, parents occupy a central position, but the schools and teachers share in the process. Our framework also adds gender as an overlapping sphere of influence. We see overlapping spheres of influence among all of these sources. Nations are especially concerned about the development of technical talent because it is fundamental to both economic and military concerns. In the United State such talent is carefully monitored by the National Science Foundation because the nation needs a steady supply of scientists and mathematicians in the Science and Engineering (S & E) pipeline for continued economic growth. Our focus for these articles is on the development of talent in the technical areas (mathematics, chemistry and physics). To secure samples of individuals that possess these talents, we conducted retrospective studies of academic Olympians. Many people around the world are familiar with the winter or summer Olympic competitions, but there are also academic Olympic competitions in mathematics, chemistry, and physics for exceptional high school students. Each participating nation administers a series of demanding national tests to identify the best 20 students in these technical areas. Once these selections have been made, the Olympians are sent to a summer training camp that prepares them for the international competitions. At the completion of these training sessions, the top four or six students are selected and then sent to the International Olympics where they compete with the best students from each participating nation. Currently, there are more than 75 nations that participate in these Academic Olympics. The Russians were the first to realize the potential of academic competitions and initiated the academic Olympics. The first program involved mathematics and was started in Leningrad in 1934 (Kukushkin, 1996). This mathematics Olympic competition was extended to city programs in Moscow and Kiev in 1935 and eventually spread to the entire USSR and beyond. The Soviets used these competitions to funnel talent into areas where they were needed. A student scoring exceptionally high on one of the academic Olympic exams was given automatic admission to the best universities. This admission placed the exceptional student in the Soviet S & E pipeline. In socialized countries national testing programs were conducted that assured the identification of a steady stream of gifted individuals. Once identified, these talented students could be funneled into areas where development was needed. Our studies of academic Olympians have been underway since 1994 in China, Taiwan, Korea, Finland, Germany, Romania, and the United States. In each of these countries the different Olympiad cohorts in mathematics, chemistry, and physics have been tracked down for our studies. In some countries interviews are used to secure information; in other cases, mail surveys are used. Our studies accumulated a substantial amount of quantitative and qualitative
data. These parallel studies collected data in a number of areas: socio-economic information; psychological dimensions of family; school influences (negative & positive); school grades; scores on standardized tests; high school graduation ranks; extracurricular activities (including musical interests & abilities); awards; college and university degrees; computer literacy; career histories; academic productivity (including publications, patents, software products); and evaluation of Olympiad programs. The objectives of these studies are: 1. To determine if these academic Olympians fulfill their high potential; 2. To isolate the factors that help or hinder the development of extraordinary talent. Members of our research team have published the results of a number of studies dealing with these academic Olympians ( see Campbell, 1996a, 1996b; Campbell & Wu, 1996; Shoho, 1996; Subotnik, Miserandino, & Olszewski-Kubilius, 1996; Wu, 1996; Wu & Chen, 2001; Zixiu, Pengzhi, & Xiaoyong, 1996). Methodology for the Olympiad Studies Since the different national Olympiad research teams used the same methods and instruments, a brief description of them is provided. To secure the samples we followed four steps: 1. Secure the names and addresses of Olympians from the organizations that are responsible for running these competitions. 2. Verify the validity of these addresses. (Mobility in some countries makes finding adult Olympians very difficult.) 3. Mail packets of surveys and instruments to the Olympians where we have valid addresses. 4. Refinement of the database: Many packets were never delivered because Olympians have moved without leaving any forwarding address. (One national team used telephone interviews to secure survey information.) After utilizing these steps, stable sets of addresses were established, in effect, finalizing the size of each sample. To obtain responses from the Olympians we used repeated mailings, telephone calls, and e-mail messages. With all of these contacts we uncovered no bias for the nonrespondents. Some of them replied after the 2nd or 3rd mailing. Some replied to a follow-up mailing that was done two years later. Some are overseas and cannot respond. The only complaint we got from them was that the packets are too long and require too much time to complete. Still, most of them responded eventually (95% of the U.S. Math Olympians responded). Our research teams were especially concerned with validity issues. Our strategy was to ask for much of the same information from the Olympians and their parents as a means of checking the accuracy of the information. However, a few Olympians did not give us the addresses of their parents, and in a small number of cases we had addresses of parents who felt that their Olympian was too busy to reply. Consequently, we have a few cases where we have data from only Olympians or only from parents, but the overlap strategy in these cases supplied us with complete data. Overall, for over 90% of the Olympians we have data from both sources and after careful analysis concluded that both sets of information are virtually identical. For the two instruments that were used in these studies (Inventory of Parental Influence, IPI; Self-concepts attitudes attribution Scales, SaaS), extensive factor analyses were done to establish validity. Further factor analyses were done with items within the surveys to isolate valid scales. In all cases reliabilities were derived for the scales used with these studies. For example, for the IPI, the alpha reliabilities for the following factors are: pressure α =.83; support α=.63; help α=.92; books and intellectual stimulation α=.80; tight supervision α=.87. For the
SaaS attribution scales, the reliabilities ranged from α=.64 to α=.68. As an example of scales constructed from items within the surveys, we derived two hindrance scales: shortcomings of the schools α=.90; negative affect in the schools α=.76. One common finding emerges in all the cohorts in the different countries -- the scarcity of female mathematics, chemistry, and physics Olympians. The gender gaps are summarized in Table 1. Why are there so few female Olympians? These gender gaps are surprising because there are so many different cultures involved (including samples in Asia, Europe, Scandinavia, and the United States). All of the articles contained in this theme issue of JRE address this question. For the most part, the different national teams conducted in-depth qualitative studies to answer this question. Some articles also contain other studies that were related to this fundamental question. Overview of Paradigms that Offer Explanations for Gender Gaps Some researchers attribute the gender differences to overall male/female differences (Benbow & Lubinski, 1997; Benbow & Stanley, 1983a, 1983b; Page, 1976). Feingold (1992a, 1992b) attributes the gaps to more variability among males. Other researchers (Campbell & Beaudry, 1998; Eccels, 1982, Eccles, Kaczala, & Meece,1982; Chipman & Thomas, 1987; Chipman & Wilson, 1985; Fennema, 1983, Linn, 1986) believe that differential socialization factors are responsible. Plomin (1997) argues for analyzing the interactions between nature (genes) (44%) and nurture (46%) when analyzing such complex questions. The following brief summaries represent a primer of the paradigms that have been presented to explain gender gaps. Explanations Depending Largely on the Biological Differences Between Males and Females (Deficit Theory)(Including Genetic & Hormonal Differences) Deficit Theory In 1971 the Study of Mathematically Precocious Youth (SMPY) uncovered large gender gaps (Benbow & Stanley 1980, 1982, 1983a; 1983b, 1983c; Lubinski, Benbow, & Morelock, 2000). The following gender gaps were found: SAT-M 500+2:1 (2 boys: 1 girl); SAT-M 600+ 4:1(4 boys: 1 girl); SAT-M 700+ 13:1 (13 boys: 1 girl). Page (1976) believes that these data “brilliantly satisfies [sic] all requirements” for a sex-linked recessive model of inheritance where genes on the X and Y chromosomes were responsible for some of the important traits involved with exceptional math achievement. This deficit would explain why males did better in this one academic area (p304). Explanations Involving some Balance Between Physical Gender Differences and Socialization Forces Combination of Nature & Nurture According to this paradigm (Plomin, 1990, 1997), genes account for no more than 50% of variance for most traits. For example, for IQ, 44% of the variance is due to genes (nature), 23% can be attributed to common family factors (environment -- nurture), and 23% can be traced to environmental factors outside the family (nurture). Home environment is important early in life but fades later; some genes are turned on later in life. For gender differences there is a complexity of factors.
Males Exhibit Greater Variability Feingold (1992a, 1992b) and Hedges and Nowell, (1995) attribute gender gaps to the fact that males exhibit more variability than females; more males are found at the extreme ends of the normal curve on many traits. For example, there are more feeble-minded males at one extreme and more genius males at the other extreme. This phenomenon occurs for several psychological and cognitive traits. Explanations Depending Largely on Socialization Factors Interaction of Numerous Factors Effecting Choice The Eccles paradigm (Eccels, 1982a, 1982b, 1983, 1984, Eccles, Adler & Meece,1984; Eccles, Barber, Updegraff & O’Brien, 1995; Eccles & Jacobs, 1986) is the most comprehensive theoretical model yet to be developed. It represents a synthesis of other promising theories and frameworks. Much of this model is based on the perceptions of students as they make important decisions. Decisions are the product of complex interactions among 11 sets of factors. Within each set many variables operate. Each specific decision is not dependent on any one variable but is the result of numerous interactions, and gender stereotypes and gender typing occurs over time. Females Camouflage Talent Kerr (2000) believes that during preschool and primary school years gifted females are encouraged to develop their talents. However, during early adolescence and adulthood many gifted females learn to camouflage their talents in an effort to gain acceptance by other females and by males for dating and marriage. Furthermore, the families in many cultures encourage gifted girls to comply with traditions, and by doing so limit their career development. Balance between Development & Nurturing Job Environment According to this theoretical prospective (Theory of Work Adjustment --TWA) (Lubinski, Benbow & Morelock, 2000), there is a correspondence between each individual’s abilities and the abilities inherent in the job (satisfactoriness). The correspondence between each individual’s interests and preferences must be matched with the degree to which the job permits the nurturing of these personal qualities (satisfaction). Many gifted females have more balanced goals than gifted males -- wanting to divide their time more equitably among marriage, family, and their career. This orientation undercuts their job satisfaction and limits their career growth. Micro-Inequities/Macro-Inequities Campbell and Beaudry (1998) break down gender differences into micro-inequities and macro-inequities. This approach emphasizes that there are hundreds or even thousands of sociopsychological variables where gender inequities occur. Many different social agents reinforce these inequities over time. Such socialization occurs time and time again at the micro level where it is subtle, easily missed, or overlooked. A good many of these micro-inequities arise when parents emphasize “masculine” behavior for their sons and “feminine” behavior for their daughters. They want their sons to exhibit masculine ways of acting and their daughters to be “lady-like.” This emphasis is responsible for starting boys and girls down very divergent roads. Eventually these subtle micro-inequities accumulate over time to produce observable gender
gaps and gender stereotypes. This conceptualization views socialization as a strong outgoing ocean tide that is very difficult to overcome. People get caught up in such a tide and “learn to go with the flow.” The articles that follow summarize qualitative data from Finland, the United States, Germany, and Korea. These articles use the research questions listed below to address the gender inequities that were uncovered among the Olympians. 1. Does an aversion from competition hamper talented females in pursuing technical careers? 2. Are parents somewhat responsible for this lack of encouragement? 3. Do parents discourage the development of their daughter’s extraordinary talent in the technical areas? 4. Does family socialization hamper a female’s development? 5. Are teachers responsible for not recognizing talented females? The final article in this theme issue deals with how well the findings from these international Olympiad studies support or refute the eight theoretical paradigms that have been derived to explain the existence of the gender differences in the technical areas. How well do any of these theoretical frameworks fit the Olympiad data? References Benbow, C., & Stanley, J. (1980). Sex differences in mathematical ability: Fact or artifact? Science, 1262-1264. Benbow, C., & Stanley, J. (1983a). Differential course-taking hypothesis revisited. American Educational Research Journal, 20(4), 469-473. Benbow, C., & Stanley, J. (1983b). Sex differences in mathematical reasoning ability: More facts. Science, 222(4627), 1029-1031. Benbow, C., & Stanley, J. (Eds.). (1983). Academic precocity: Aspects of its development. Baltimore, MD: The Johns Hopkins University Press. Benbow, C., & Stanley, J. C. (1982). Consequences in high school and college of sex differences in mathematical reasoning ability: A longitudinal respective. American Educational Research Journal, 19(4), 598-622. Campbell, J. (1996). Early identification of mathematics talent has long-term positive consequences for career contributions. Journal of Educational Research, 25(6), 497-521. Campbell, J., & Wu, W. (1996). Development of exceptional academic talent: International research studies. Journal of Educational Research, 25(6), 479-483. Chipman, S., & Thomas, V. (1987). The participation of women and minorities in mathematical, scientific, and technical fields. Review of Educational Research in Education, 14, 387430. Eccles. (1983). Expectancies, values and academic behavior. In J. Spence (Ed.), Achievement and achievement motivation (pp. 75-146). San Francisco, CA: W.H. Freeman. Eccles, J. (1982). Sex differences in achievement patterns. Paper presented at the Annual Meeting of the American Educational Research Association. Eccles, J. (1984). Do students turn off to math in junior high? Paper presented at the Annual Meeting of the American Educational Research Association.
Eccles, J., Adler, T., & Meece, J. L. (1984). Sex differences in achievement: A test of alternate theories. Journal of Personality and Social Psychology, 46(1), 26-43. Eccles, J., Barber, B., Updergraff, K., & O'Brien, K. (1995). An expectancy-value model of achievement choices: The role of ability self-concepts, perceived task utility and interest in predicting activity choice and course enrollment. Paper presented at the Annual Meeting of the American Educational Research Association. Feingold, A. (1992). The greater male variability controversy. Review of Educational Research, 62(1), 89-90. Plomin, R. (1997). Genetics and intelligence. In N. Colangelo & G. Davis (Eds.), Handbook of gifted education (2nd ed.) (pp. 67-74). Boston, MA: Allyn and Bacon. Wu, W., & Chen, J. (2001). A follow-up of Taiwan Physics and Chemistry Olympians: The role of environmental influences in talent development. Gifted and Talented International, 17(1), 16-26. Zixiu, Z., Pengzhi, L., & Xiaoyong, T. (1996). Nurturing factors that promote Mathematics achievement in Mainland China. Journal of Educational Research, 25(6), 535-543. James Reed Campbell is a Professor at St. John’s University, Jamaica, New York. He has authored over 200 publications and research papers including two popular books for parents, Raising Your Child to Be Gifted: Successful Parents Speak (Brookine Books) and Secrets of Productive Parents: Recipes That Work (Psychological Corp.)(in Mandarin).
Table 1 Gender gaps for academic Olympians Country
Ratio (males:females)
Finland math
10:1
USA math physics chemistry
44:1 4:1 6:1
Germany math physics chemistry
35:1 95:0 10:1
Taiwan math physics/chemistry
17:1 11:1
China math
17:1
math
15:1
Korea
