A Research Agenda for Computational Thinking

A Research Agenda for Computational Thinking

Betul C. Czerkawski (University of Arizona, USA)
Copyright: © 2018 |Pages: 13
DOI: 10.4018/978-1-5225-3200-2.ch004


It has been more than a decade since Jeanette Wing's (2006) influential article about computational thinking (CT) proposed CT to be a “fundamental skill for everyone” (p. 33) and that needs to be added to every child's knowledge and skill set like reading, writing and arithmetic. Wing suggested that CT is a universal skill, and not only for computer scientists. This call resonated with many educators leading to various initiatives by the International Society for Teacher in Education (ISTE) and Computer Science Teachers Association (CSTA) provided the groundwork to integrate CT into the K-12 curriculum. While CT is not a new concept and has been taught in computer science departments for decades, Wing's call created a shift towards educational computing and the need for integrating it into curriculum for all. Since 2006, many scholars have conducted empirical or qualitative research to study the what, how and why of CT. This chapter reviews the most current literature and identifies general research patterns, themes and directions for the future. The purpose of the chapter is to emphasize future research needs by cumulatively looking at what has been done to date in computational thinking research. Consequently, the conclusion and discussion section of the paper presents a research agenda for future.
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Computational thinking (CT) describes the collection of computer science based knowledge, practices, and attitudes which may be leveraged across domains in combination with the affordances of computational tools and systems in the solving of complex, often ill-defined problems. ISTE and CSTA (2011) also created an operational definition of CT and listed the following characteristics: formulating problems; logically organizing and analyzing data; representing data through abstractions; identifying, analyzing and implementing possible solutions and generalization. Others tried to define CT by emphasizing skills, habits and dispositions (Barr and Stephenson, 2011; Grover and Pea, 2012) but Voogt, Fisser, Good, Mishra & Yadav (2015) highlight the need for differentiating CT from other 21st century skills (i.e. media information literacy) and support a more pragmatic definition with strong ties to cognitive science. They think Lu and Fletcher’s (2009) definition that treats CT as “a conceptual way to systematically, correctly, and efficiently process information and tasks to solve complex problems” (p. 261) is a more appropriate one, as it captures all the major components of CT by ignoring the peripheral ones.

In addition to various approaches to define CT, it should be noted that the roots of educational computing could be found in Seymour Papert’s earlier work with Logo. In 1980s, Papert used a simple programming language to teach children logical and procedural thinking skills. However, the Logo language did not gain acceptance in the majority of K-12 schools due to incompatibility between traditional school culture and the pedagogical approaches (i.e. discovery-based; experiential) required by the Logo language (Lye & Koh, 2014). The recent resurgence of interest in CT could be explained with the newer technologies and tools that are available to educators (Voogt et al., 2015) as well as the increased computerization of our daily lives.

In the past decade, there has been significant interest in computational thinking research as educator study and further explore the ways to integrate CT in learning and teaching environments. This interest mostly has been on the primary education level, since that’s when students start using academic computation and build the most basic foundation of reading, writing and arithmetic skills. Most published work on computational thinking emphasized descriptive and informative information about the use and integration of CT skills across the curriculum by laying the ground for conceptual consensus as what CT is and its break down as teachable skills. There were also some empirical studies are conducted by the educators. There is, of course, an established and mature research agenda within the computer science field but ‘K-12 education’ is a new area of interest. The research on ‘educational’ computational thinking has been intense, and after more than ten years there is a clear need to look at these studies more closely, find out major research findings and synthesize them to make better sense of common research findings. The purpose of this chapter is to present such a synthesis on CT for other researchers, which will then lay the ground for future research.

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