The achievement gap in science education refers to often broad disparities between black and Hispanic minority students and white and Asian students. Many subjective and external factors affect variations in performance standards and test norms. However, given the tilted performance spectrum accusations of racist and classist preferences or standards are not surprising as much as they are incorrect and reactionary. Such guarded factors as class, economic strata, and race have all exerted an impact on the discussion and assessment of standardized science performance and testing and the controversy of disproportionate outcomes. An adequate example of the provocative opinions that surround this issue can be cited with the introduction of Charles Murray’s book The Bell Curve, which many people attacked as racism masquerading as sociology. The achievement gap reflects social and economic complexity as much as it reflects surface expectations. It seems that there are several ways to assess test norms: one way is to simply accept the standards as a very limited gauging of academic data that disregards important variables that determine scores. Another way to evaluate test norms is to conclude that they are indeed biased by race, class, and economics. If this is the case the testing methods need to be adjusted for these variables, and take into consideration access to quality education, a number of important social factors, and economic privilege contrasted with wide patterns of disadvantage (Geisinger, 1994 ). To simply evaluate performance norms on the surface and ignore these underlying causes is somewhat myopic.
Underlying factors in divergent performance in the advance placement science curriculum for minority students are the formative structures of preparatory environments that often impact not only student motivation, but the cumulative performance standards that follow (Holcomb-McCoy 2007). Recent NAEP (National Assessment of Educational Progress) test results show an improvement over time in the science and math performance of minority students however they continue to lag behind white and Asian students. Qualitative and quantitative data analysis from the National Center for Education Statistics revealed that on average test scores in science and math revealed a gap of more than 20 test-score points between White and Asian and African-American and Hispanic students (NCES 2012). These disparities and score-differentials follow through graduation and college completion rates. A common complaint among educators is that standardized testing doesn’t reflect significant cultural mediators. Both reliability and validity in testing are important considerations. To produce reliable test properties all evidence of bias and distortion must be excluded (Barton et al 2000). For instance, it is important to analyze how a particular test model was established and applied? What are the cultural components that went into the template and how are these factors weighed in the variation in scores? In this context, it is understood that reliability translates to consistency. It refers to the perceived quality of the instrument being used. A reliable test should work towards reliable results (Brown 2004). Validity would be the perceived accuracy of the instrument and taken as a whole, reliability and validity play a necessary role in generating reasonably accurate data. Generally, reliability coefficients range from 0 to 1.0, with the coefficient of 0 signaling no reliability and 1.0 registering as optimal reliability (Payne 2009). By applying this measure to standardized testing the expectations for improved reliability and validity are satisfied.
One type of validity should ideally be content validity: this simply means that the design should measure content knowledge to the most accurate degree, and avoid less relevant criteria. By focusing the emphasis on the core content the test is presumed valid. Another category is criterion-related validity, which entails specific behavioral predictors based upon previous assessments. A third category is construct validity, this consideration is an evidence-based formulation worked out over a period of time. Factors that impact, testing validity are reliability and the degree of systematic error calculated. There is also the consideration of a test’s internal consistency, which means that individual test items should correlate with total test scores. Construct validity predicts developmental changes, where this equation fails the construct validity of the test is called into question (Denson & Chang 2010). Reliability is a prerequisite to validity; if the test is unreliable it is not valid. Norm-based testing simply compares scores without taking into account other relevant factors that may impact score variables. The model for norm-based testing is flawed in that it is not designed to factor in complex social and economic factors for variations in test scores. By standardizing tests irrespective of these considerations the results do a disservice to many students. These are not minor or cosmetic flaws, they are structural flaws that generate widely inaccurate data, and conclusions.
Other significant conclusions in disproportionate performance are directly related to cultural compatibility. Cultural environments that contribute to academic reinforcement through participatory language and embedded cues often place certain students in a better position to acquire skills and motivational dynamics that positively impact performance. Because socialization is often environment-specific, the process entails widely diverging academic influences that impact performance (Davenport 2002). At the lower end of the economic strata, minority demographics are not adequately prepped for a science curriculum or the demands and disciplines necessary to navigate it. In this particular respect, the qualitative resources necessary for an understanding of basic science and the ability to translate this understanding into compatible performance are missing (Kanter 2010). What begins as a process of wide economic disparities invariably translates to the uneven cumulative performance and lag-factors that largely determine success.
Assessment and testing differ in important ways: testing is a sub-category of assessment, and therefore limited and limiting in its conclusions. Test data can improve or decline under many factors that do not necessarily reflect the particular student’s abilities or disabilities (Jackson 2006). To this extent testing can be inconclusive and even damaging in certain respects because of flawed prescriptions it might entail. Testing can also be constructive when the results are tempered by the overall assessment. Spot testing may identify weak areas in student performance whereas assessment identifies strong patterns in academic behavior that can be evaluated for improvement or intervention (Boykin & Noquera 2011). Depending upon the needs of certain students comprehensive assessment is an important diagnostic instrument for directing attention and corrective measures, assessment isolates broad patterns in academic performance, where testing can be misleading.
Among the positives of assessment and testing are a more informed profile of individual student needs and strategies to meet them; however, testing can also be confining in its conclusions. Where individual students may commonly underperform in testing, aptitude results may differ sharply in a more comprehensive assessment. The current assessment model is reliable to the extent that it can fairly predict performance patterns over a period of time, and allow for calculated intervention where it is required. Because of the unreliable nature of testing, some disadvantages can be overcome by informal assessment and later a more comprehensive assessment designed to identify consistent patterns over time. Assessment data can then inform the approach of teachers and parents, and permit the student to better understand their strengths and weaknesses (Drew 1996). Until there are productive assessments and action-plans to compensate for the achievement gap between minority students, as well as a comprehensive understanding of the components of this gap a resolution will be difficult.
The experience of the classroom also plays an irreducible role in determining the assimilation of students to the disciplines of science. Evaluating the impact of teachers, classroom dynamics and key aspects of student socialization relevant to culture cannot be ignored in the assessment of the achievement gap (Adams 2012). Current research has endeavored to isolate the reasons why race and class are such strong predictors of student performance and educational objectives. The subject raises critical questions of social preference and inequality at the national educational levels and demands a coherent administrative response. In a larger and more substantive evaluation the achievement gaps are tied into what is described as an ‘opportunity gap’, a broad divergence in resources and collective opportunity that is skewed toward wealthier demographics, and conversely works against the less advantaged segment of the minority population (Barton 2000).
With each new generation, the challenges of teaching change slightly, demanding both a new set of adaptations for the teachers and additional requirements for students. Today these challenges are difficult to ignore because of the advance in technology and other influences that accompany it, children are more exposed to the trappings of adult culture and the behavioral problems that arise from this exposure. (Howard 2010). Even at the introductory stage of education, there are notable changes in children, ranging from aggression, image-behavior learned from television or other children, and problems with attention that also have associations with the pop-culture. The academic experience is fraught with several variables all of which can impact the quality of education and its essential utility for equipping children with the set of skills and disciplines required to compete in a complex culture. The institutional failure to comprehensively probe and address factors that impact minority students in a national education system is a tragedy.
Adams, C. (2012) Eligible Students Missing Out on Advanced Placement Courses, Education Week, 31 (21), 18
Barton, Angela C. and Kimberley Yang. (2000). \The Culture of Power and Science Education: Learning from Miguel". Journal of Research in Science Teaching, 37(8): 871-89. Boykin, W. A. & Noquera, P. (2011) Creating Opportunity to Learn: Moving from Research to
Practice to Close the Achievement Gap, 1st edition. Association for Supervision and Curriculum Development.
Brown, B. (2004) Discursive Identity: Assimilation into the Culture of Science and its Implications for Minority Students. Journal of Research in Science, 46 (2) 810-834
Davenport, P. (2002) Closing the Achievement Gap: No Excuses, American Productivity Center.
Denson, N., & Chang, M. J. (2010) Racial Diversity Matters: The Impact of Diversity-Related Student Engagement and Institutional Context. American Educational Research Journal 46 (2), 322-353.
Geisinger, K. F. (1994) Cross-Cultural Normative Assessment: Translation and Adaptation Issues influencing the Normative Interpretation of Assessment Instruments. Psychological Assessments, 6 (6) 304-312.
Holcomb-McCoy, C. (2007) School Counseling to Close the Achievement Gap: A Social Justice Framework for Success, 1st edition. Crown Publishers.
Howard, T. (2010) Why Race and Culture Matter in Schools: Closing the Achievement Gap in America’s Classrooms. Teachers College Press.
Jackson, A. (2006) Academic Achievement and Minority Individuals, http://www.sagepub.com/upm-data/11753_Jackson__A_Entries.pdf
Kanter, D. E. (2010) The Impact of a Project-Based Science Curriculum on Minority Student Achievement, Attitudes and Careers. Wiley Periodicals.
Noquera, P. A. & Wing, J. Y. (2008) Unfinished Business: Closing the Achievement Gap in Our Schools, 1st edition. Jossey-Bass.
Olszewski-Kubelius, P., Lee, S. Y., Ngoi, M. & Ngoi, D. (2004) Addressing the Achievement Gap Between Minority and Nonminority Children by Increasing Access to Gifted Programs. Journal for the Education of the Gifted, 28, No 2 pp. 127-158.
Payne, R. K. (2009) Research-Based Strategies for Narrowing the Achievement Gap in Under-Resourced Students, 1st edition. Process Inc.