Reading Like a Historian: A Document-Based History Curriculum Intervention in Urban High Schools

课程 热情 阅读(过程) 数学教育 读写能力 历史思维 干预(咨询) 阅读理解 教育学 理解力 纪律 心理学 教学方法 社会学 社会科学 计算机科学 语言学 社会心理学 程序设计语言 哲学 精神科
作者
Abby Reisman
出处
期刊:Cognition and Instruction [Informa]
卷期号:30 (1): 86-112 被引量:326
标识
DOI:10.1080/07370008.2011.634081
摘要

Abstract Enthusiasm about the instructional potential of primary sources dates to the late nineteenth century and has been echoed recently in the work of literacy experts, historians, and educational psychologists. Yet, no extended intervention study has been undertaken to test the effectiveness of primary source instruction in real history classrooms. This study, with 236 11th-grade students in five San Francisco high schools, represented the first extended curriculum intervention in disciplinary reading in an urban district. The Reading Like a Historian (RLH) curriculum constituted a radical departure from traditional textbook-driven instruction by using a new activity structure, the "Document-Based Lesson," in which students used background knowledge to interrogate, and then reconcile, historical accounts from multiple texts. A quasi-experiment control design measured the effects of a 6-month intervention on four dimensions: (a) students' historical thinking; (b) their ability to transfer historical thinking strategies to contemporary issues; (c) their mastery of factual knowledge; and (d) their growth in general reading comprehension. MANCOVA analysis yielded significant main effects for the treatment condition on all four outcome measures. This study has implications for both adolescent literacy instruction and history teaching at the middle- and high-school levels. ACKNOWLEDGMENTS This research is part of the Reading Like a Historian Project, funded by the Stanford K–12 Initiative, Sam Wineburg, Principal Investigator. I was also supported by the National Academy of Education Predoctoral Fellowship in Adolescent Literacy. I thank the students and teachers who so generously gave of their time in the project, as well as members of my dissertation committee—Sam Wineburg, Pam Grossman, and Dan Schwartz—for helpful comments on earlier versions. I am indebted to Rich Shavelson for his astute statistical advice, as well as to Brad Fogo for his ongoing collaboration. This support is gratefully acknowledged; however, I alone am responsible for the contents of this article.
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