Rhetoric and reality in science performance assessments: An update

This article addresses the rhetoric of performance assessment with research on important claims about science performance assessments. We found the following: (a) Concepts and terminology used to refer to performance assessments often were not consistent within and across researchers, educators, and policy-makers. (b) Performance assessments are highly sensitive not only to the tasks and the occasions sampled, but also to the method (e.g., hands-on, computer simulation) used to measure performance. (c) Performance assessments do not necessarily tap higher-order thinking, especially when they are poorly designed. (d) Performance assessments are expensive to develop and use: technology is needed for developing these assessments in an efficient way. (e) Performance assessments do not necessarily have the expected positive impact on teachers' teaching and students' understanding. (f) If teachers are to use performance assessments in their classrooms, they need professional development to help them construct the necessary knowledge and skills. This article attempts to address some of these realities by presenting a conceptual framework that might guide the development and the evaluation of performance assessments, as well as steps that might be taken to create a performance assessment technology and develop teacher inservice programs. © 1996 John Wiley & Sons, Inc.     

K-ABC content bias: Comparisons between Mexican American and White children

Content (item) bias on the Mental Processing Scales and the Achievement Scale of the Kaufman Assessment Battery for Children (K-ABC) was investigated with 100 Mexican American and 100 White fifth- and sixth-grade boys and girls. All children were English-speaking and from similar socioeconomic status (SES) backgrounds. An item-group (partial correlation) method that controlled for age, sex, and ability was used to examine bias. On the Mental Processing Scales, 17 (14%) of 120 items were found to be biased—the strong majority against the Mexican American children. On the Achievement Scale, it was observed that 58 (63%) of 92 K-ABC items were biased—all against the Mexican American sample. This latter finding is discussed in the context of possible differences in learning opportunities (language based, SES based, and school segregation) experienced by the two ethnic groups.     

Cognitive and Social Structure of the Elite Collaboration Network of Astrophysics: A Case Study on Shifting Network Structures

Scientific collaboration can only be understood along the epistemic and cognitive grounding of scientific disciplines. New scientific discoveries in astrophysics led to a major restructuring of the elite network of astrophysics. To study the interplay of the epistemic grounding and the social network structure of a discipline, a mixed-methods approach is necessary. It combines scientometrics, quantitative network analysis and visualization tools with a qualitative network analysis approach. The centre of the international collaboration network of astrophysics is demarcated by identifying the 225 most productive astrophysicists. For the years 1998?C1999 and 2001?C2006 four co-authorship networks are constructed comprehending each a two year period. A visualization of the longitudinal network data gives first hints on the structural development of the network. The network of 2005?C2006 is analyzed in depth. Based on cohesion analysis tools for network analysis, two main cores and three smaller ones are identified. Scientists in each core and additionally in structurally interesting positions are identified and 17 qualitative expert interviews are conducted with them. The visualization of the network of 2005?C2006 is used in the interviews as a stimulus for the interviewees. An analysis of the three most often used keywords of the 225 astrophysicists is included and combined with the other data. The triangulation of these approaches shows that major epistemic changes in astrophysics, e.g. the discovery of the accelerating expansion of the universe, together with technical and organizational innovations, leads to a restructuring of the network of the discipline. The importance of a combination of qualitative and quantitative network analysis tools for the understanding of the interplay of cognitive and social structure in the sociology of science is substantiated.