Assessing Computational Thinking

Assessing Computational Thinking

Roxana Hadad (Northeastern Illinois University, USA & University of Illinois at Chicago, USA) and Kimberly A. Lawless (University of Illinois at Chicago, USA)
Copyright: © 2015 |Pages: 11
DOI: 10.4018/978-1-4666-5888-2.ch150
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Introduction

Despite the fact that computer science and information technologies have redefined nearly every discipline and are a large part of the current and future economy (Lacey & Wright, 2010), “most states treat high school computer science courses as simply an elective and not part of a student’s core education” (Wilson et al., 2010) resulting in the inability to fill many U.S. jobs that will shape our future (National Center for Women and Information Technology, 2009). As a response, there is a call for education, government, and industry to better prepare our students to have the critical thinking skills necessary for developing and interacting with digital devices and information (Wing, 2006; National Governors Association & Pew Center on the States, 2007; Wilson et al., 2010). In her seminal article, Wing (2006) was clear that these “computational thinking” skills should be a part of everyone’s education, not just computer science majors. And although critical thinking is a large focus of the new Common Core standards (http://www.corestandards.org), it should not be confused with computational thinking, or CT. As stated by Voskoglou and Buckley:

It can be concluded that critical thinking is a prerequisite to knowledge acquisition and application to solve problems, but not a sufficient condition when we are faced with complex real technological problems. Technological problems require also a pragmatic way of thinking such as [‘computational thinking’.]” (2012, p.32)

The push to develop “computational thinking” skills means we must support students to “apply basic strategies in problem solving, understand the character of a solution or algorithm, and have a sense of the ways in which computerization and digitization have changed how research is conducted,” as stated the National Science Foundation’s partnership, Mobilize (www.mobilizingcs.org/about/computational-thinking).

While the alarm has been sounded and organizations have mobilized to begin promoting CT in K-12, Wing argues that learning research has yet to be sufficiently utilized to maximize the impact of CT on K-12 education (National Research Council, 2011). This is apparent in the gaps that exist in research on CT assessment (Grover & Pea, 2013), making teaching CT and incorporating it into other domains difficult for K-12 educators. To address this, we used Gagne's outcomes of learning (1977) as a framework for aligning CT objectives with appropriate assessments in a summer robotics program for middle school students at Northeastern Illinois University.

Key Terms in this Chapter

Learned Capabilities: Types of outcomes as observed by human tasks ( Gagné, 1977 ).

External Conditions of Learning: Factors and stimuli that could impact the learner’s behavior ( Gagné, 1985 ).

Internal Conditions of Learning: Skills that a learner possesses before any new learning begins ( Gagné, 1985 ).

Cognitive Strategies: Skills which manage one’s own learning, remembering, and thinking ( Gagné, 1977 ).

Computational Thinking (CT): “The thought processes involved in formulating problems and their solutions so that the solutions are represented in a form that can effectively be carried out by an information-processing agent” ( Wing, 2011 ).

Intellectual Skills: The ability to distinguish, combine, classify, analyze, and quantify objects, events, and symbols; the are divided into discriminations, concrete concepts, defined concepts, rule using, and higher-order rule using ( Gagné, 1977 ).

Alignment of Learned Capabilities: The coordination of the learning objective with its appropriate internal and external conditions, as well as its assessment, as defined by the learning capability.

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