Meaning Of Socio-Cultural Factors Involved In Education - Research Paper Example

Meaning Of Socio-Cultural Factors Involved In Education I. Introduction A student scored two standard deviations below the mean for children in her grade (7th grade) in Reading and received a standard score of 115 in Mathematics where the mean is 100 with a standard deviation of 15 points for the test that follows the bell curve. How can we interpret the results of a daughter’s tests to her mother? How can we use these to develop a successful academic curriculum? Related to the matter, what are the socio-cultural dimensions of education that are relevant to the situation? What insights can we get from current research on the cultural factors involved in academic testing, particularly for African-Americans and Latinos? II. Meaning of Test Scores In summary, the meaning of the test scores in our case is that the student is behind the rest of grade sevens in Reading but is relatively way ahead of the grade sevens in Mathematics. A reading score that is two standard deviation below the mean implies that the student belongs to the bottom 3% of the grade sevens, implying that close to 97% of the grade sevens are ahead of her in the test. In what may be clearer terms, this means that if there are only 20 grade seven students, the student who scored two standard deviations from the mean in Reading means that she is about only as good as the slowest academic performer in 20 students. Make the students 100 instead of 20, a two standard deviation score below the mean score in reading means that she is about as good as third slowest academic performer in a reading if there only 100 grade seven students. These discussions are not very accurate, however, and were meant to impress important mental images of the situation. Table 1. Select Portions of the Table of the Normal Curve Source: Walpole & others (751) Table 1 refers to select portions of the normal curve. A test score that is two standard deviation from the mean imply a z-Score of -2.0 that is associated with -2.00. The numeral -2.00 follows through the intersection of the row of -2.0 in the z column and the column associated with .00 of the table. In turn, following protocols followed for the table of the normal curve, the numerals mentioned earlier are associated with the value of 0.0228 that gives area under the normal curve. The area of normal curve 0.0228 is associated with the probability associated with the lowest scorer with the score associated with two standard deviations below the mean. This means that more precisely the student we are discussing belong to bottom 2.28% of the grade sevens, following the association of 0.0228 with 2.28%. Meanwhile, a score of 115 given of 100 and standard deviation of 15 imply a z value of 1.15 following standard transformation formation protocols. In turn, the z-score of 1.15 is associated with the area 0.8749 under the normal curve based on Table 1. This means that the student we are discussing belong to the bottom 87.49% of the grade sevens. Described in another way, only 12.51% of the students of grade seven are ahead of our student. We can also say that our student belong close to the top 13% of the grade sevens. In other words, if there are only 100 grade sevens, our student closely belong to the best 13 students of the grade sevens. In summary, we can say that our student is one of the “laggards” with regard to performance in reading but one of the best with regard to performance in mathematics. The academic profile is probably related to cultural factors but we suspend discussions on this for the meantime. We proceed first to the matter of developing an appropriate academic curriculum for our student. III. Developing Successful Academic Curriculum In developing an appropriate academic curriculum for our student, three data deserve close attention. Firstly, our particular student is one of the lagging performers in Reading. Secondly, our particular student is one of the best academic performers in Mathematics. And, finally or thirdly, cultural factors may be a possible explanation why our student seems to be a laggard with regard to academic performance in Mathematics. The third characteristics of our student NEED NOT play a factor in the design of the curriculum appropriate for our specific student although it is serving well in UNDERSTANDING better the nature of the problems involved in the academic success of our student. The student may be a laggard in reading and, as indicated by data, belong to close to the bottom 3% in academic performance with regard to reading. Most likely, the student’s data on Reading does not indicate that our student belong to the less bright among the grade sevens. Although statistical studies may have to be obtained to validate this view (poor performance in Reading but good performance in Mathematics does not imply an overall academic laggard), the safe and just approach is to assume that we have a good student. To a certain extent, this is equivalent to assuming innocence unless proven guilty. Thus, only a few tweaks may be necessary: we subject the student to special catch-up classes in Reading. The catch-up classes can be held immediately after class hours or on Saturdays. The catch-up classes will involve reading exercise in school as well as reading assignments that must be taken up at home. We must involve the parent in supervising the catch-up reading program and ask them to help us supplement their reading exercises at home. Related to this, we must provide the student access to home study materials. In addition, we must use video and audio educational materials as well as tap extra books for our students so long as we are sure that our student can use the materials at home or her family has the appropriate players or equipment. With regard to our student’s performance in mathematics, we have two options. Leave the situation as is or expose the student to advance materials in mathematics. It probably will not harm a bit if we let the student perform undisturbed in her mathematics class, especially because she has a lot of catching up to do in her Reading. Probably the situation will just be fine if we let the student perform good or excellently in mathematics to compensate for her deficiencies in Reading. Counseling support is, of course, a must and a school counselor must invite this student regularly maybe weekly at first then monthly to monitor how she is coping with and looking at her situation: a laggard in Reading but a good enough performance in Mathematics. There can be peer pressure at work and how the student handles the pressure should be an area for monitoring by a school counselor. Another option for student above average performance in Mathematics would be to encourage her teachers to give advance materials, providing her extra points for handling advance materials. However, this need not be an option in the early stage but can be one of the options in the later stages. It may be important to study the student’s socialization or integration patterns with students her age. Socialization not only academic performance would make her a better person, especially now that she must undergo a phase of catching up in her Reading. Thus, in summary, the appropriate academic program for success for the student is for her to catch up in Reading and perform normally or excel in her Mathematics subjects without extra effort. Side by side with this program, the student should be monitored for integration with her peers and whether she is able to cope up with her situation and the attitudes she is taking in coping with her situation. Only after several months and based on overall academic performance should we consider giving this student a special mathematics curriculum that would take into account her above average performance in mathematics. IV. Socio-cultural Factors Involved in Education Socio-cultural factors are likely involved in academic performance. This is indicated by several basic data in US education. In a March 2009 report, the National Center for Education Statistics reported that US education data for 2007 reveals the following (20): The percentage of students enrolled between ages 10 to 24 is almost consistently higher for White Americans than for Blacks, followed by Hispanics. Among the 10 to 13 years old, the percentage of young people enrolled in school is 98.6%. However, this is 98.6% for Whites, 97.8% for Blacks, and 99.0% for Hispanics. Among the 14 to 15 years old, the percentage of young people enrolled in school is 98.7%. This is 98.8% for Whites, 99.0% for blacks, and 98.4% for Hispanics. Among the 16 and 17 years old, the percentage of young people enrolled in school is 94.3%. This is 95.6% for Whites, 93.7% for Blacks, and 90.6% for Hispanics. Among the 18 and 19 years old, the percentage of young people enrolled in school is 66.8%. This is 69.7% for Whites, 61.8% for Blacks, and 57.2% for Hispanics. Among the 20 and 21 years old, the percentage of young people enrolled in school is 48.4%. This is 54.5% for Whites, 38.1% for Blacks, and 32.3% for Hispanics. Among the 22 to 24 years old, the percentage of young people enrolled in school is 27.3%. This is 28.4% for Whites, 27.8% for Blacks, and 18.8% for Hispanics. Thus, based on the latest available data, we can conclude that except, for ages 14 to 15, the percentage of young people in school are higher for Whites than for Blacks and Hispanics have the least the lowest percentage of young people in school among the Whites, Blacks, and Hispanics. Disparity in education is also reflected in disparities as they enter the labor force. According to the same publication of the National Center for Education Statistics: out of those who completed high school in 2005 in the United States, the unemployment rate is 10.3% for Whites, 26.2% for Blacks and 18.5% for Hispanics (567). Most revealing are the data in the National Center for Education Statistics Compendium Report on the dropout rate for 2007 for grades 10-12 in US schools (24): the school dropout rate for White is 2.2% while this is 4.5% for Blacks/Non-Hispanics and 6.0% for Hispanics. For 16 to 24 years old Americans, the dropout rate is 5.3% for Whites (non-Hispanics), 8.4% for Blacks (non-Hispanics), and 21.4% for Hispanics (31). Meanwhile, as indicated by Figure 1 next page, although college enrollment rates for Whites has been improving from 1973 to 2006, college enrollment rates among Whites are consistently and substantially higher among college enrollment rates among Blacks. College enrollment rates between Blacks and Hispanics are competing against each other to become the second highest enrollment rate among ethnic groups in the US. However, there are periods in the eighties in which college enrollment among blacks have been too low: hovering close to only 40% versus the current consolidated college enrollment rate of around 70% College enrollment rates may have been increasing since 1973 but college enrollment rates among non-Whites, especially among Blacks, have been consistently lower compared to Whites. Figure 1. College Enrollment Rate by Ethnicity Source: Gates Foundation Post Secondary Strategy (5) V. Socio-cultural Factors in Tests and Academic Performance Studying 30 years of research on race differences and cognitive ability, Rushton and Jensen reached a disturbing finding: they argue that that there is a genetic component in the racial differences in IQ (235). Based on their finding, Rushton and Jensen concluded that differences in IQ among races that are interpreted based on a notion of discrimination must be tempered by a notion of underlying differences between races that explain for differences in IQ, academic achievement, and cognitive ability (235). Stricker and Ward assessed the impact of inquiring on a student’s ethnicity in their academic performance. They reported that in an earlier test in 1995, asking students about their ethnicity affect their performance on tests. However, in their 1998 study, they found that inquiry has no effect on their academic performance in tests (5, 8-14). Joireman and Abbott investigated the effects of several variables on educational achievement and found that there is a “reasonable support” for the view that ethnicity affects student performance (26). However, they also found that “low income mediates the relationship between ethnicity and math achievement” and “ethnicity exert a strong effect on reading over and above low income” (16). Getinet and Nguyen investigated several possible determinants on high school students’ academic attainment in various subjects, focusing especially on the possible effects of ethnicity on academic attainment in the US. They found that Blacks and Hispanics tend to fare “worse” at higher quantiles, especially in science. However, they did not conclude the difference in performance is due to ethnicity itself but to other variables associated with ethnicity. Thus, their recommendations were “aimed at closing the gap in attainment between Whites and minorities who need to target higher levels of the test score distribution” (2). Finally, the research of Dickens and Flynn, using data from nine standardization samples from four major tests of cognitive ability, found that blacks have gained 5 or 6 IQ points against non-Hispanic nonwhites between 1972 and 2002 (913). The data can be interpreted in several ways but a possible way to interpret the Dickens and Flynn data is that the environment in which homo sapiens have grown into affect the intelligence quotient. Further, the Dickens and Flynn study can be interpreted to mean that the differences in academic performance among ethnic groups are not genetic but cultural: cognitive ability is a function of the social environment in which homo sapiens are in. Related to the foregoing, the book of David Livermore says that it is CQ or cultural quotient rather than IQ that should be the focus. Unfortunately, Livermore’s book is more on the romantic and theological rather than academic. VI. Conclusion The conclusion that is asserted in this work is that cultural factors affect the results of tests and influence both academic performance and achievement. Ethnic differences also reflect cultural differences that influence results of tests, performance, and achievements. It helps to attribute the differences to cultural rather than genetic factors because doing this will lead to correct academic policy. Recognizing the differences as an effect of socio-cultural rather than genetic differences imply that the differences can be moderated and gaps among Blacks, Whites, and Latinos can be eliminated. The term socio-cultural factors is meant to highlight the role of social variables like income in the manifestation of cultural differences that in turn affect test outcomes, academic performance, and achievement. Work Cited Bill & Melinda Gates Foundation. Postsecondary Success. Washington: Bill & Melinda Gates Foundation, 2009. Dickens, William and James Flynn. Black Americans Reduce the Racial IQ Gap: Evidence from Standardization Samples. Psychological Science, 2006, 17 (10), 913-920. Getinet, Haile and Ngoc Anh Nguyen. Determinant of Academic Attainment in the US: A quantile regression analysis of test scores. Munich Personal RePEc Archive MPRA Paper No. 4626. Munich: MPRA, 2007. Joireman, Jeff and Martin Abbott. Structural Equation Models Assessing Relationships Among Student Activities, Ethnicity, Poverty, Parents’ Education, and Academic Achievement. Technical Report 6. Washington: Washington School Research Center, 2004. Livermore, David. Cultural Intelligence: Improving Your CQ to Engage our Multicultural World. Michigan: Baker Academic. National Center for Education Statistics. Digest for Education Statistics 2008. Institute of Education Sciences: National Center for Education Statistics, 2009. National Center for Education Statistics. High School Dropout and Completion Rates in the United States: 2007. Institute of Education Sciences National Center for Education Statistics, 2009. Rushton, J. Philippe and Arthur Jensen. Thirty Years of Research on Race Differences in Cognitive Ability. Psychology, Public Policy, and Law, 2005, 11 (2), 235-294. Stricker, L. and William Ward. Examinees’ Ethnicity and Sex: Effects on Computerized Placements Tests Performance. New York, College Entrance Board, 1998. Walpole, Ronald, Raymond Myers, Sharon Myers, and Keying Ye. Probability and Statistics for Engineers and Scientists. 8th ed. London: Pearson Education Ltd., 2007. Read More