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• W153660134 abstract "ABSTRACT Despite a decade of advocacy and advances in technology, data driven decision making remains an elusive vision for most high schools. This article identifies key data systems design needs and presents methods for monitoring, managing, and improving programs. Because of its continuing salience, we focus on the issue of tracking (ability grouping). We provide specific examples of useful analyses for measuring the conditions and consequences of tracking. We also discuss how to make transcript data useful for analyzing inter-track mobility, for monitoring course placement decisions, and for drilling down to plan interventions for subsets of students. INTRODUCTION Most high schools organize courses in ability levels, although the prevalence of rigid tracking programs has declined (Hallinan, 2004; Lucas, 1999). About 80 - 85% of U.S. high schools have ability grouping in courses, but these estimates are approximate and ability grouping policies vary greatly among schools (Hallinan, 2004, Johnson, 2002). Ability grouping is common in all industrialized countries (Ireson & Hallam, 2001; LeTendre, Hofer, & Shimizu 2003; Trautwein, Ludtke, Marsh, Koller, & Baumert, 2006). Though tracking remains common, the practice has many detractors. Critics contend tracking exacerbates racial and class segregation and disadvantages those students most in need of exposure to rigorous curriculum and high standards (Burris & Welner, 2005; Lucas, 2001; Oakes, 1985; Rosenbaum, 1976). Studies generally find low-achieving students achieve more in heterogeneous classrooms (Hallinan, 2003; Oakes, 1985; Rosenbaum, 1976; Slavin, 1990). initiatives have been implemented in many schools, though with mixed success (Hallinan, 2004; Loveless, 1999; Rubin, 2003; Welner, 2003). Detracking is not without its critics. Its implementation frequently engenders controversy; teachers and parents who oppose detracking argue that the rigor and standards in upper level (formerly tracked) courses diminishes (Loveless, 1999; Singal, 1991; Stanley & Baines, 2002). Just as there is research against tracking, there are also studies questioning heterogeneous grouping - studies showing high-achieving students perform better in homogenous classrooms (Argys, Rees, & Brewer, 1996; Gamoran & Mare, 1989; Kulik, 1992; Loveless, 2003). This paper does not seek to provide more research findings. We seek; rather, to enlighten readers about better ways to measure conditions and consequences of tracking and about enabling features of school/district data systems. While much literature extols the virtues of data-driven decision making, the reality in many high schools is more like a black box. Students enter in 9th grade and leave in 12th grade, but there is limited measurement and monitoring of student outcomes. Most high schools can produce a transcript for each student showing his/her accumulated courses, credits, and grades. But how well can high school decisionmakers pose and answer questions specifically about conditions and consequences of tracking? School leaders and analysts should have ready access to information showing the prevalence of: * students stuck in lower level tracks despite high test scores and good grades * minority or other categories of students under-represented in honors courses * girls under-represented in upper level math or science courses * students with poor grades and/or test scores being placed in upper level courses Other forms of information would be evidence of successful courses and positive value added: * diminishing percentages over time of minority students in lower level tracks * diminishing enrollments over time in remedial courses * equivalent levels of measured academic growth irrespective of track placement Data-informed decision making requires more than just transcripts, more than disaggregated annual test scores, and more than listings of students in courses. …" @default.
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• W153660134 title "Measuring Conditions and Consequences of Tracking in the High School Curriculum" @default.
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