K12阶段的计算机科学框架_丁丁猫亲子创客

The concepts and practices of the K–12 Computer Science Framework are not specific, measurable performance expectations in the form of standards, nor are they detailed lesson plans and activities in the form of curriculum. Instead, the K–12 Computer Science Framework is a high-level guide that states, districts, and organizations can use to inform the development of their own standards and curricula.

As illustrated in Figure 1.1, the framework provides building blocks of concepts (that students should know) and practices (that students should do) which can be used to create standards (performance expectations of what students should know and do).

框架是对于不同背景的观众,包括教师在学习计算机科学教授。这个观众包括

•州/地区政策制定者和管理者;

•标准和课程开发人员(计算机科学有足够经验);

•当前计算机科学和新教师,包括教师与其他学科领域和教育工作者在非正式的场合,和

•支持组织(非营利组织、行业合作伙伴和非正式教育)。

The framework was written for an audience with diverse backgrounds, including educators who are learning to teach computer science. This audience includes

• state/district policymakers and administrators;

• standards and curriculum developers (with sufficient computer science experience);

• current and new computer science teachers, including teachers from other subject areas and educators in informal settings; and

• supporting organizations (nonprofits, industry partners, and informal education).

实践包括计算思维

前言

计算机科学的七个核心实践描述行为和思维方式,计算有文化的学生使用充分参与今天的数据丰富的和相互联系的世界。实践自然彼此整合,包含语言,故意重叠照亮它们之间的连接。他们显示在订单表明过程发展中计算工件。这个过程是周期性的,可以按照许多路径;框架,它始于识别不同用户和重视别人的观点和结尾与广泛的受众沟通的结果(见图5.1)。

与核心概念不同,实践没有划定等级。相反,实践使用叙事描述学生应该如何展示每个实践与日益成熟从幼儿园到12年级。除了描述进展,这些故事也提供一些实践的相互关系的例子语句,这些语句的方式建立。

Practices Including Computational Thinking

Preface

The seven core practices of computer science describe the behaviors and ways of thinking that computationally literate students use to fully engage in today’s data-rich and interconnected world. The practices naturally integrate with one another and contain language that intentionally overlaps to illuminate the connections among them. They are displayed in an order that suggests a process for developing computational artifacts. This process is cyclical and can follow many paths; in the framework, it begins with recognizing diverse users and valuing others’ perspectives and ends with communicating the results to broad audiences (see Figure 5.1).

Unlike the core concepts, the practices are not delineated by grade bands. Rather, the practices use a narrative to describe how students should exhibit each practice with increasing sophistication from kindergarten to Grade 12. In addition to describing the progression, these narratives also provide some examples of the interrelatedness of the practice statements and the ways in which these statements build upon one another.

Computational Thinking

Computational thinking is at the heart of the computer science practices and is delineated by practices 3–6. Practices 1, 2, and 7 are independent, general practices in computer science that complement computational thinking.

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定义计算思维

计算思维是指思维过程表达解决方案涉及到计算步骤和算法可以由一台计算机(城市大学,斯奈德和翅膀,

2010;哦,2011;李,2016)。这个定义利用的想法制定问题和解决方案的形式,可以由一个信息处理代理(城市大学,斯奈德,&翼,2010)和的解决方案应该采取的具体形式的计算步骤和算法由计算机执行(哦,2011;李,2011)。计算思维需要理解

实践包括计算思维

电脑的功能,制定电脑,需要解决的问题和设计算法,计算机可以执行。最有效的上下文和方法开发计算思维是学习计算机科学;它们在本质上是相连的。

计算思维本质上是一个解决问题的过程,涉及设计解决方案,利用电脑的力量,这个过程开始之前写一行代码。电脑提供福利的内存,执行的速度和精度。电脑还需要人们表达他们的想法在一个正式的结构,如编程语言。类似于记录在一张纸上写“马上把你的想法写下来,”创建一个程序允许人们外部化的想法以一种可以操纵和审查。编程允许学生考虑他们的想法,通过调试一个程序,学生调试自己的思考(Papert,1980)。

尽管其名称所暗示的,计算思维基本上是一个人的能力。换句话说,“[h]乌曼过程信息;人类计算”(翅膀,2008年,p . 2008)。这个细微差别的基础计算机科学的方法(即“不插电”。计算机科学,教学没有电脑)和解释了计算思维可以应用计算机科学的边界以外的各种学科,比如科学,技术,工程和数学(干),而且艺术和人文学科(邦迪,2007)。

区分计算思维

计算思维在k - 12计算机科学的描述框架超出一般使用电脑或技术教育包括特殊技能,如设计算法,分解问题,建模的现象。如果计算思维能发生没有电脑,相反,在课堂上使用电脑并不一定构成计算思维。例如,一个学生并不一定使用计算的思想

当他或她在电子表格中输入数据并创建一个图表。然而,这一行动可以包括计算思维如果学生创建算法自动化数据的转换或交互式数据可视化。

计算构件必须区分评估过程(即创建它。计算思维),除了产品本身的特点。例如,相同的数字动画可能是精心构造算法的结果,控制人物移动,以及他们如何相互作用或简单地从预定tem-plate选择人物和动作。在本例中,它是用于创建过程动画定义它是否可以被认为是一个计算工件。计算思维的评估可以通过提高学生解释他们的决定和开发过程(布伦南&雷斯尼克,2012)。

Defining Computational Thinking

Computational thinking refers to the thought processes involved in expressing solutions as computational steps or algorithms that can be carried out by a computer (Cuny, Snyder, & Wing,

2010; Aho, 2011; Lee, 2016). This definition draws on the idea of formulating problems and solutions in a form that can be carried out by an information-processing agent (Cuny, Snyder, & Wing, 2010) and the idea that the solutions should take the specific form of computational steps and algorithms to be executed by a computer (Aho, 2011; Lee, 2016). Computational thinking requires understanding

Practices Including Computational Thinking

the capabilities of computers, formulating problems to be addressed by a computer, and designing algorithms that a computer can execute. The most effective context and approach for developing computational thinking is learning computer science; they are intrinsically connected.

Computational thinking is essentially a problem-solving process that involves designing solutions that capitalize on the power of computers; this process begins before a single line of code is written. Computers provide benefits in terms of memory, speed, and accuracy of execution. Computers also require people to express their thinking in a formal structure, such as a programming language. Similar to writing notes on a piece of paper to “get your thoughts down,” creating a program allows people to externalize their thoughts in a form that can be manipulated and scrutinized. Programming allows students to think about their thinking; by debugging a program, students debug their own thinking (Papert, 1980).

Despite what the name implies, computational thinking is fundamentally a human ability. In other words, “[h]umans process information; humans compute” (Wing, 2008, p. 3718). This nuance is the basis for “unplugged” approaches to computer science (i.e., teaching computer science without computers) and explains how computational thinking can apply beyond the borders of computer science to a variety of disciplines, such as science, technology, engineering, and mathematics (STEM), but also the arts and humanities (Bundy, 2007).

Distinguishing Computational Thinking

The description of computational thinking in the K–12 Computer Science Framework extends beyond the general use of computers or technology in education to include specific skills such as designing algorithms, decomposing problems, and modeling phenomena. If computational thinking can take place without a computer, conversely, using a computer in class does not necessarily constitute computational thinking. For example, a student is not necessarily using computational thinking

when he or she enters data into a spreadsheet and creates a chart. However, this action can include computational thinking if the student creates algorithms to automate the transformation of the data or to power an interactive data visualization.

A computational artifact must be distinguished by evaluating the process used to create it (i.e., computational thinking), in addition to the characteristics of the product itself. For example, the same digital animation may be the result of carefully constructing algorithms that control when characters move and how they interact or simply selecting characters and actions from a predesignated tem-plate. In this example, it is the process used to create the animation that defines whether it can be considered a computational artifact. The assessment of computational thinking can be improved by having students explain their decisions and development process (Brennan & Resnick, 2012).

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