The first, second, and third sections of the chapter focus, respectively, on English language arts, mathematics, and science and engineering. For each discipline we

  • discuss how “deeper learning” has been characterized in the discipline, including issues and controversies that have played out over time;
  • describe the relevant parts of the Common Core State Standards or the NRC science framework (along with selected other reports outlining expectations for student learning) in light of the historical context; and
  • analyze how the new standards and framework map to our characterization of deeper learning and to the clusters of 21st century skills defined in Chapter 2.

In the final section of the chapter, we present conclusions and recommendations based on a broad look across all three disciplines. In this broad look, we compare the expectations included in the Common Core State Standards and the NRC science framework with deeper learning (as characterized within each discipline) and 21st century skills.

ENGLISH LANGUAGE ARTS

The Context: A History of Controversy

Discussions of how to teach reading and writing in the United States have a reputation for contentiousness, reflected in the military metaphors used to describe them, such as “the reading wars” or “a curricular battleground.” The public debates surrounding the fairly regular pendulum swings of the curriculum reveal fundamental differences in philosophy and widely variant interpretations of a very large but sometimes inconsistent research base.

Divergent Positions on Reading for Understanding

Beliefs about how to develop reading for understanding diverge greatly, with the spectrum of opinions defined by two extreme positions. One position, which we will refer to as the simple view of reading, holds that reading comprehension is the product of listening comprehension and decoding. Proponents of this position argue that students in the early grades should learn all of the letters of the alphabet and their corresponding sounds to a high degree of accuracy and automaticity. Agile decoding combined with a strong oral language (i.e., listening vocabulary) base will lead to fluent



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