Abstract

Reform efforts in education have increasingly emphasized standards and accountability as the pathway to achieve educational outcomes. The Obama Administration released a blueprint for the reauthorization of the Elementary and Secondary School Act (also known as the No Child Left Behind Act, also known as NCLB) as one of his first acts after assuming the presidency in 2008. The NCLB policy seeks to reform education using a standards-based model. It is based on the belief that setting uniformly high standards for all students will improve their performance. NCLB established the requirement for all states to create assessments aligned to challenging state standards in order to receive federal funding. This chapter focuses on the impact of NCLB accountability mechanisms on middle school mathematics teaching and learning. The ultimate purpose of NCLB is to promote improved student achievement and to reduce the achievement gap among student groups. The reform aims to achieve this goal through aligning curriculum and instructional practices with standards and assessments. A review of current academic research includes the impact of NCLB accountability mechanisms on student scores in middle school mathematics, on teaching, and on the alignment of curriculum content with the established standards. The findings show that it is difficult to determine the impact of NCLB on student learning, given the general rise in National Assessment of Educational Progress scores over time and the limited studies available. Studies surveyed indicate that there have been some changes in teaching practices; however, more specific evidence is presented on the importance of teacher knowledge and related proxies of teacher quality in improved student outcomes. Recommendations are provided for policy and further research on the influence of NCLB accountability mechanisms.KeywordsMiddle SchoolFormative AssessmentStudent PerformanceTeacher KnowledgeAccountability MechanismThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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