Exploring Issues About Computational Thinking in Higher Education By Betul C. Czerkawski and Eugene W. Lyman III, University of Arizona
Abstract The term computational thinking (CT) has been in academic discourse for decades, but gained new currency in 2006, when Jeanette Wing used it to describe a set of thinking skills that students in all fields may require in order to succeed. Wing’s initial article and subsequent writings on CT have been broadly influential; experts in computational thinking have started developing teaching and leadership materials to support integration of CT across the K-12 curriculum. Despite interest at the K-12 level, however, outside of computer science and other science, technology, engineering and mathematics (STEM) fields there has been less interest in –and research conducted on– the potential of CT in higher education. The purpose of this paper is to review the current state of the field in higher education and discuss whether CT skills are relevant outside of STEM fields. Keywords: Computational thinking; higher education; STEM learning
Introduction Computational thinking (CT) is “the thought processes involved in formulating problems and their solutions so that the solutions are represented in a form that can be effectively carried out by an information processing agent” (Cuny, Synder & Wing, 2010, para. 2, as cited in Wing, 2010). The idea of Computational thinking (CT) has been present in academic discourse for decades under various names and definitions. One of the earliest was proposed by Alan Perlis in 1962, while describing an introductory programming course at the Carnegie Institute of Technology –now Carnegie Mellon University (Perlis, 1962, pp. 1991). Seymour Papert, based on his research teaching children mathematical concepts using the LOGO programming language in the 1980’s, held out the hope that procedural thinking Volume 59, Number 2
(i.e.,“think[ing] like a computer”) would someday be considered a valuable component of overall thinking skills (Papert, 1980, p. 155). CT acquired new currency in 2006, when Jeanette Wing published the viewpoint essay “Computational Thinking” in Communications of the ACM (Wing, 2006). Wing’s essay shifted the tenor of discourse around CT, proposing that it is not only a skill useful to computer scientists, but to anybody who uses mental processes to solve problems and discover computational solutions. Viewed in this broader sense, as part of ‘logical thinking’, CT may be considered a skill relevant to all disciplines, not only computer science. Therefore, Wing (2006) argued, “to reading, writing and arithmetic, we should add computational thinking to every child’s analytical ability” (p. 33).
Why Computational Thinking? Wing’s definition of computational thinking skills is suitable to wide application across multiple fields. “Computational thinking is a way humans solve problems; it is not trying to get humans to think like computers” (Wing, 2006, p. 35). Computational thinking makes use of logic, algorithmic thinking, recursive thinking, abstraction, parallel thinking, pattern-matching and related processes. “The most important and highlevel thought process in computational thinking is the abstraction process. Abstraction is used in defining patterns, generalizing from instances and parameterization” (Wing, 2010, p. 1). In other words, abstraction is a tool that permits the creation of large and complex systems of information by defining and generalizing from simpler components. Conversely, when confronted with very large and complex problems, CT methods of reduction, embedding, transformation and simulation enable “reformulating a seemingly difficult problem into one we know how to solve” (Wing, 2006, p. 33). In short, computational thinking
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enables individuals to become more efficient problem solvers by teaching them to recognize computable problems and approaching the problem-solving process skillfully.
A Disjuncture Since the publication of Wing’s essay, there has been considerable interest in, and research devoted to, incorporating computational thinking skills into K-12-level curricula (Barr & Stephenson, 2011). Government agencies and private interests ranging from the National Science Foundation and the British Royal Society to Microsoft and Google have supported these efforts (Wing, 2010). Grover and Pea (2013) have provided a thorough analysis of this trend to date and identified several areas for further exploration that should be of interest to K-12 educators and educational researchers. In higher education, however, the response to Wing’scallforbroaderapplicationofcomputational thinking has been more scattered. Certainly there is interest; issues related to teaching CT skills to early-stage computer science undergraduates remain a topic of active research. There is also wide acknowledgement that CT methods are critical to –and sometimes transformative in– a variety of STEM fields, most notably in genomics. Wing also notes a proliferation of sub-disciplines incorporating “computational [subject]” as part of the disciplinary title (Wing, 2010). But many clusters of cross-disciplinary interest in CT are local in scope, either manifesting in specific institutions such as Carnegie Mellon University (Wing 2010) and Stanford University (Moretti, 2011) or as specialized sub-branches of larger academic departments. There is not yet a coherent cross-institutional movement to incorporate CT as a fundamental skill-set, outside of computer science and a few STEM disciplines. There is a variety of potential reasons why this may be so; we present the following list in the understanding it is likely incomplete: 1. Issues around the conceptualization of computational thinking. These issues are more easily addressed at the K-12 level by a pragmatic focus on teachable methods (Grover & Pea, 2013, pp. 39-40); they become potential obstacles in the more research-oriented domain of higher education. 2. Outside of computer science and the STEM fields, the difference between applying CT methods derived from computer science and simple application of computers to problems within a discipline (“data crunching”) is either less well-understood or simply elided.
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3. Even within computer science, computation is understood to be limited –some problems are computable; others are not. Potentially, in humanities and other non-science fields core disciplinary questions are constructed in such a way as to be non-computable; in some cases a “computable” construction of these questions may be undesirable. An important caveat, however, is that our current understanding of computational limitations may be changed significantly by emerging computational models such as natural computing and concurrent interactive computing (Dodig-Crnkovic, 2011). In this paper we will further review the status and potential of computational thinking in higher education. We make no assertion that the following survey is complete. We believe, however, that we have assembled a comprehensive overview of the current state of CT in higher education that provides a clear set of directions for future study and application.
Method Keyword searches on terms relating to computational thinking and higher education were conducted across 87 databases of academic publications and conference proceedings using omnibus search utilities such as EBSCOhost, ProQuest, Web of Knowledge, EDITLibrary and the ACM Digital Libraries. Whenever possible, search results were limited to peer-reviewed academic journals. This yielded approximately 70 articles that were manually reviewed for content relevance and categorized as research articles, position analyses or supplementary documents. This process of review and categorization reduced the total number of articles to 41. Additional resources, such as documents from the Stanford Literary Lab, were selected manually and reviewed for possible contributions to our discussion of CT. Not surprisingly, the largest single category of sources (approximately half) addressed issues of specific concern within the fields of computer science: arriving at a suitable definition of computational thinking and/or addressing pedagogy in computational thinking concepts for early-career CS undergraduates. Direct discussion of computational thinking in other disciplines was more scattered; use of computational methods or modeling is often implied, but our review focused on sources within the research corpus that address computational thinking directly, whenever possible. Our preferred definition of computational thinking derives from Wing, but we noted that other researchers and writers often chose to adopt either variant or entirely separate meanings for “computational thinking”.
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Results and Discussion Issues around the definition and scope of CT As we have noted Wing (2006, 2010) views computational thinking expansively, as a mindset and collection of problem solving methods suitable for application across multiple disciplines. Arguably, computational thinking is a pre-existing component of many human endeavors; the old formula of “computing = programming” is obsolete –if it was ever truly applicable. From this perspective, computational thinking is “a new way of reasoning” that should be presented in as many contexts as possible (Henderson, 2009). A potential difficulty with this expansive view of computational thinking arises, however, when computational thinking is considered in its disciplinary and historical contexts. Aho (2010), notes “the term computation means different things to different people depending on the kinds of computational systems they are studying and the kinds of problems they are investigating” (pp. 834-835). Aho’s discussion of Turing machinebased computing highlights how articulating even simple computational models requires considerable precision and detail (Aho, 2010). The Turing machine is a theoretical construct proposed by Alan Turing in 1936 that is “the most important model of sequential computation studied in computer science” (p. 833). Turing machines are deceptively simple: a machine with a read-write head follows a set of instructions provided on a moving tape to provide an output (or set of outputs) that are written to the same tape. When the instructions are completed, the Turing machine halts. The beauty of the Turing machine model is that it “provides a precise definition for the term algorithm: an algorithm for a function f is just a Turing machine that computes f” (p. 233). So-called “Turing-complete” computing is foundational to computer science as a discipline, but it is limited both in the sense that it is a closed model (the machine is only aware of its own instruction set) and some inputs (problems or instruction sets) are non-computable. Non-computable problems either produce infinite output (i.e., the Turing machine never halts) or unpredictable –though constrained– variations in output (the Turing machine is nondeterministic) (p. 834). Aho points out there are emergent models of computing that overcome the limitations of Turing machine computation, but extending the scope of computational thinking to encompass these models may require a great deal of effort (p. 835). Wing’s model of computational thinking is not limited to problem solving methods derived Volume 59, Number 2
from Turing-based computing (Wing, 2010). Nonetheless, because of the foundational aspect of Turing-based computing in computer science, its methods are sometimes assumed to be coterminous with computational thinking as a whole. Guzdial (2008) points out that these methods may influence how computational thinking is conceptualized by practitioners within the computer science field. This means that spreading computational thinking from computer science to other academic fields – which have their own specialized problemsolving methods–may require adapting existing CT theory and methods to match the needs of “novices” and other non-specialists (Guzdial, 2008). A promising alternative may be to expand computational thinking to include methods derived from alternative models of computing, such as natural computing and concurrent interactive computing (DodigCrnkovic, 2011). Dodig-Crnkovic (2011) and Denning (2007) both note that this move towards open, or interactive computation both expand and transcend simple algorithmic thinking to encompass “games...seen as models for larger, complex adaptive systems that never terminate” (Denning, 2007, p. 18). Denning (2007, 2009) raises the question of whether computational thinking itself is an aspect, or extension, of scientific inquiry and may in fact be subsumed within a broader framework of scientific principles: ...computational science is seen in the other sciences not as a notion that flows out of computer science, but as a notion that flows from science itself. Computational thinking is seen as a characteristic of this way of science. It is not seen as a distinctive feature of computer science. (Denning, 2009, p. 29) Denning suggests that the pervasiveness of computation in the natural sciences effectively means that “[c]omputing is an infinite game” (Denning, 2007, p. 18) that “is more fundamental than computational thinking” (Denning, 2009, p. 30) and that understanding computer science (and computation in the broader realm of the sciences) requires a wider view than the tools provided by computational thinking alone. Along the same lines, Dodig-Crnkovic (2011) states that “If we take…computing with a wide scope, as computationalism suggests, computing becomes…a natural science...The Turing model…is a special case of a more general concept of natural computation” (pp. 319-320).
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Application of CT in Higher Education Teaching CT within CS and the STEM disciplines –Potentials of game-based learning Although there is broad-based discussion around methods for teaching CT at the K-12 level, at the collegiate level, practical research on teaching CT skills continues largely to take place within computer science and the science, technology, engineering and math (STEM) fields. Miller and Settle (2011) studied a sample of computer science and non-computer science majors who were exposed to multiple methods for learning tree (path) traversal methods. Students who were provided opportunities for unstructured study of examples demonstrated better performance overall than students performing more traditional task- and projectoriented exercises. Kilpeläinen (2010) provides additional food for creative thought by demonstrating various ways that a simple travel metaphor can be used to explain the concept of reduction in computational problems. There is also an intriguing line of research that suggests game-based learning may be an effective strategy to teach complex computational thinking skills. We note, in passing, that conceptually, this was understood as a possibility as far back as the early 1960’s (Perlis, 1962, p. 185). In more current research, Kazimoglu et al. (2010) studied puzzle-solving game play as a method to teach introductory programming. This was accomplished initially by prototyping a computer game in which students used a mix of directaction and programming commands to maneuver an avatar figure through an increasingly complex series of “brain teaser” problems (Kazimoglu et al., 2010). The authors argued that “as learning content is presented as an integral part of the game-play, students develop an understanding on how programming concepts work and use these concepts as game-play elements to solve problems in an environment that makes sense to them” (p.1384). A follow-on pre-experimental study (Kazimoglu et al., 2011) of their game “Program your Robot” indicated “the game can support the visualization of introductory programming constructs” (p. 644). The authors hope to conduct further experimental research with larger sample sizes to explore their hypotheses concerning game-based learning and CT. Liu, Cheng and Huang (2011) conducted research that used a railroad simulation game to assist students in learning computational problem solving. After analyzing 117 students’ feedback and evaluating their problem solving skills, 60
the authors concluded that game play enhanced student motivation and provided a “flow” learning experience not available in a more traditional teaching environment. Although a small percentage of students did not benefit from the game-based learning environment, “[t]he students who perceived a flow experience state frequently applied trial-and-error, learning-by example, and analytical reasoning strategies to learn...computational problem solving skills” (Liu, Cheng & Huang, 2011, p. 1907). This study is of interest at multiple levels: simulation is itself a category of abstraction derived from CT methodology. Moreover, game-based learning provides linkages between Turing-based computation (abstraction), interactive computation (games as models of complex systems) and constructivist pedagogy. A 2011 study conducted by Berland and Lee suggests that computational thinking can be taught by, and applied in, game-based scenarios that involve no computers at all. Berland and Lee video-recorded 3 groups of 3-4 college-age students playing a strategic and collaborative board game, Pandemic, and tracked “1) quantitative analysis of the makeup of the students’ computational thinking; 2) quantitative analysis of code counts for instances of ‘global’ and ‘local’ computational thinking; and 3) some descriptive examples of computational thinking” (p. 66). Their focus was on five characteristics commonly understood as elements of CT: conditional logic, distributed processing, debugging, simulation, and algorithm building as well as two stages of logic; local and global. Based on their analysis of the video-taped sessions, the authors concluded that complex computational thinking had occurred in a non-digital game environment and the players demonstrated various CT skills continuously throughout the game.
Interdisciplinary Outreach In addition to innovation in teaching and learning methods, propagation of computational thinking outside of computer science depends on interdisciplinary interest and outreach. Roberts (2011) frames the expansion of computational thinking in terms of cross-pollination of methods with other disciplines in the natural and social sciences. According to Roberts, this will “involve computer science and computer scientists in a fundamental way in addressing some of the most challenging problems facing society” (p. 637). This focus on interdisciplinarity is shared by Curzon et al. (2009), who assert “The beauty of computing that it is engineering, science, and
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art; a subject without clear borders and with tentacles pushing into every other subject... This interdisciplinary approach gives us the opportunity to reach students with interests outside computing” (p. 201). An exceptionally strong area of conceptual overlap has emerged between computer science and biology (Denning, 2007; Navlakha & Bar-Joseph, 2011). Navlakha and Bar-Joseph (2011) provide a detailed summary of how the convergence of various concepts in systems biology and computational thinking have crosspollinated to the benefit of both disciplines. Notably, from a computational thinking standpoint, this cross pollination is taking place both at the level of “traditional” (Turing-based) CT concepts and emerging CT concepts such as decentralized and network-based computing (pp. 2-3). They argue in favor of further convergence between the two disciplines as “we can improve our understanding of biological processes and at the same time improve the design of algorithms” (p. 9). This strong interdisciplinary relationship notwithstanding, Qin (2009) notes that although “Most biology students readily express their interests in improving their computational skills; however, few of them actually take computing-oriented courses” (p. 189). Qin’s own experiences designing a CTbased bioinformatics course yielded positive responses from students (p. 190), but a variety of factors, ranging from student discomfort with learning more technical computer operating systems such as Linux to issues in computer lab design will require further innovation in pedagogical environments and methods (2009). Caballero, Kohlmyer and Schatz (2012) discuss their experience teaching an introductory mechanics course using the VPython programming environment to introduce computational thinking concepts. They found that “[a]fter solving a suite of computational homework problems, most students...were able to model the motion of a novel problem successfully” (p. 18). In those cases where students failed to construct a successful model, performance would have been improved by an additional focus on qualitative problem analysis and debugging skills (Caballero et al., 2012, p. 18). Another approach, studied by Hambrusch, et al. (2009) involved creating a computational thinking course for science majors that nondomain-specific within the sciences. This course, offered in fulfillment of a general computing requirement at Purdue University applied programming and computational thinking concepts to problems in physics, biology and statistics (p. 185). Analysis of student entry and Volume 59, Number 2
exit surveys showed a statistically significant increase in both computer science and computer programming in general on completion of the course, though Hambrusch and her colleagues advise cautious interpretation of this result due to small sample sizes (2009, pp. 186-187).
Imprecision around the meaning of “computational” in non-STEM/CS fields Incorporation of computational thinking methods outside of computer science and STEM fields is hindered both by imprecision around the meaning of “computational” and by a perception that CT is limited to solving problems using methods appropriate to a closed, or Turingbased, model of computing. The former issue is of concern first because it perpetuates the notion that applying computers within the boundaries of existing methods somehow makes those methods “computational” and second because it may present advocates of CT with an inaccurate perception of how extensively CT has been adopted across disciplines. For example, significant advances that have been made in the field of Archaeology through the application of computerized data collection and processing. It is now possible to produce detailed 3D visualizations of archaeological sites that incorporate both artifact distribution and radiocarbon dating information (Petrovic et al., 2011). Agent-based modeling has been used to explore hypotheses about human interaction with prehistoric environments (Ch’ng et al., 2011). But simply adding a computer scientist to an archaeological excavation (or even merely someone trained in the use of sophisticated hardware or software) does not mean that “computational thinking” has become a major thread in the field as a whole. This (overly) expansive application of “computational” as an adjective for any field employing computers as adjuncts also has the potential to create confusion. The term “Computational Archaeology” can have radically different meanings depending on disciplinary context. In the field of academic archaeology, it describes the application of computer tools and analysis to sites and collections of artifacts (Ames, Costopoulos & Wren, 2010). In the field of genomics, it is a set of methods for analyzing horizontal gene transfer between species (Rödelsperger & Sommer, 2011). This lack of precision in terminology makes it that much more difficult to identify actual applications of CT in post-secondary and higher education.
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Cross-disciplinary obstacles: Principles or practices that render problems non-computable within a Turing-based model Application of CT in the humanities is less consistent, although some interesting outposts have emerged at various individual institutions. The Stanford Literary Lab, for example, has been an incubator for applying various elements of computational thinking to works of literature --one of the more notable (or infamous) examples being the Stanford Literary Lab’s Pamphlet 2 (Moretti, 2011). Among other borrowings from computational thinking, Moretti (2011) applies Graph Theory to perform a network analysis of character relationships and interactions in William Shakespeare’s plays Hamlet, Macbeth and King Lear, Charles Dickens’ Our Mutual Friend and Cao Xueqin’s The Story of the Stone. Arguably, however, the humanities pose problems that “traditional” algorithmic (Turing-type) CT may find difficult to solve. The application of computational thinking to the study of literature, for example, is far from straightforward --not least because the data to be analyzed is itself ambiguous. Heuser and LeKhac (2012) describe the difficulty in adding a semantic dimension to computational analysis of texts, noting that “after quickly exhausting the… words mentioned in several studies, we turned, awkwardly, to thesauruses and sheer invention to add more” (Heuser & Le-Khac, 2012, p. 4). Additionally, within the humanities there is in some cases a desire to avoid analytical techniques that may be perceived as reductionist. Drucker (2011) observes: ...the humanities are committed to the concept of knowledge as interpretation, and. . .the apprehension of the phenomena of the physical, social, cultural world is through constructed and constitutive acts, not mechanistic or naturalistic realist representations of pre-existing or selfevident information (p. 3). This epistemological concern is not new and actually extends beyond the humanities and into the STEM fields. Papert (1980) noted the risks of creating a false dichotomy between “propositional knowledge” and “procedural knowledge” when considering scientific knowledge (p. 135). Nonetheless, in some humanities and socialscience fields an aversion to strongly algorithmic methods of knowing may, in fact, be wellconsidered. In the realm of political science, for example, Frohock (1997) observed that the linear and rules-based nature of algorithmic thinking 62
potentially closes off the open debate necessary for a pluralist society. Turkle and Papert (1990) examined the impact of a purely formalist model of computation and discussed the performance of so-called “concrete-style” thinkers taking programming courses and observed “[c]oncrete, proximal gear builders did almost as well and in some cases better than the formal thinkers” (p. 149); they further suggest that differences in approach to computational problems may be exacerbated by societal assumptions concerning student gender and thinking styles. It’s worth noting, however, that many objections to applying CT in the humanities and fine arts assume a closed and finite (Turing-type) model of CT theory and methods; an emphasis on emerging models of computation and interdisciplinary training for computer scientists may encourage the development of computational thinking methods suitable to the “open-ended” issues studied in the humanities and fine arts (Soh, 2009). For example, in realm of musical performance, Senyshyn (1999) presented four paradoxes in support of his argument that certain functions in musical performance and composition could not be solved computationally. This is in contrast to a discussion of algorithmic composition by Edwards (2011), who traces the origins of composers using computational methods back as far as the 11th century CE –although he also observes that explicit application of computers to musical composition originated in the middle of the twentieth century. Against Senyshyn’s observations that computer programs cannot share the qualities of indeterminacy and anxiety that shape human compositions and musical performances, Edwards points out that algorithmic composition in music is, effectively, a human-computer collaboration –the computer serving as a tool that extends the composer’s ability to explore new musical ideas ( 2011, p. 87). This notion of human-computer collaboration was also explored by Mishra and Yadav (2013). In line with Wing’s assertion that CT is “A way that humans, not computers, think” (2006, p. 35) Mishra and Yadav examine the work of two digital artists creating music (David Cope) and visual design (Christopher Carlson). Among their conclusions: “the partnership of deep human content knowledge and technology can lead to deeper and more profound creative insights” (Mishra & Yadav, 2013, p. 13). We think that this give-and-take of human indeterminacy and computation both expands the range of human creativity by incorporating computational thinking and expands computational thinking by promoting the development of new models of interactive computing.
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Engaging humanities and fine-arts majors in computational thinking projects requires field-appropriate introduction to CT principles. Survey-type introductory computer science courses for students in non-technical majors have existed for decades. Cortina (2007) notes, however, that “for non-technical non-majors...the preciseness and detail needed to write computer programs correctly [is] overwhelming” (p. 218). Cortina’s response to this was to develop a new course entitled Principles of Computation as an alternative to the more typical introductory survey/programming course. Principles of Computation focused on algorithms and principles of computational thinking without requiring any actual programming. Instead of writing code, students used a flowchart simulator to create simple computer games (Cortina, 2007, p. 219). Student feedback on the new course has been positive and term-over-term enrollment numbers increased during the first three semesters the course was offered. A more ambitious interdisciplinary CT curriculum was detailed by Soh, et al. (2009) for the Renaissance Computing Project, conducted at the University of Nebraska-Lincoln (UNL). This project included faculty in a cross-section of departments cutting across computer science, STEM disciplines, humanities and fine arts. The framework proposed multiple pathways through a series of computer science courses that were specialized according to the students’ main areas of study (engineering, sciences, arts or humanities). Students would also participate in “collaborative learning activities with interdisciplinary projects or assignments for groups of students from different courses” (p. 61). Although our review of the literature does not indicate any publications yet on the outcome of the Renaissance Computing project, several revisions to introductory computer science classes based on the project have continued to the present day at UNL (http://cse.unl.edu/ renaissance/courses.php) and the project itself (http://cse.unl.edu/renaissance/) continues to be listed on the UNL website.
Computational Thinking and digital divide/social-equity issues One issue that cuts across all academic disciplines at the collegiate level is the issue of unequal pre-college exposure to computers and advanced technology in general. This unequal exposure is of particular importance for advocates of CT, not only because introductory CT courses must accommodate students of differing socio-economic and cultural backgrounds, but also because comVolume 59, Number 2
putational thinking is also becoming “an essential skill for those who would be our future inventors, innovators, and shapers of culture and public discourse” (Pearson, 2009, p. 42). Varma (2006) identified multiple factors shaping the experience of underrepresented minority students –defined as African American, Hispanic and Native American students– “with [computer science] classes, faculty, advisors, teaching assistants, and fellow classmates” (p. 129). Some factors derived from the need for better understanding of the specific issues confronting minority students and better overall student care (Varma, 2006, pp. 130-132). Notably, however, less overall exposure to computers in K-12 educational environments and weaker preparation using computers for programming and math-related skills (Varma, 2006, p. 130) mean that colleges must be prepared to offer innovative programs and support for students crossing the “digital divide” as undergraduates. Varma’s findings corroborate those of Margolis, et al. (2010) whose case studies of Los Angeles high schools revealed significant disparities in the type and kinds of computer courses offered. Margolis’ research indicates that socioeconomic and racial stereotyping continue to limit access to advanced experiences with computer science (and, by extension, computational thinking skills) for many K-12 students –leaving colleges to make up the difference for those students who make it past these initial social barriers.
Contributions from Educational Technologists Computational thinking is, in some ways, a natural fit for educational technologists. Czerkawski and Xu (2012) argue that “educational technology practitioners as well as students and faculty of Educational Technology programs, can help in raising awareness for CT in K-12 schools and higher education and serve as change agents and early adopters of teaching CT skills” (p. 2608) outside of CS fields. Additionally, educational technologists are logical candidates for creating and leading most professional development materials and activities for collegiate and university faculty interested in incorporating computational thinking skills as part of their pedagogy. Finally, they assert that educational technology as a field, can bridge the gap between computer science and education by “creating an environment where major concepts and ideas are clearly communicated to educators who are in the position of leading and teaching CT initiatives” (Czerkawski & Xu, 2012, p. 2608).
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In order to identify the most effective ways to integrate CT skills into college curricula, Czerkawski (2013) conducted in-depth interviews with instructional designers who worked in higher education settings. The questions focused mostly on the analysis and design steps of the instructional design process rather than development and implementation. In this study, it was found that most instructional designers treat CT as any other thinking skill; consequently they design CT curricula using strategies similar to those used to teach general critical thinking or problem solving skills. More specific instructional design strategies are required for computational thinking courses and Czerkawski recommends that more studies be conducted to establish a better connection between CT and instructional design and technology.
Conclusions Much of the work summarized in this paper has focused on definitional issues, implementation of computational thinking in computer science curricula and efforts to infuse CT strategies into disciplines other than computer science. It is clear that much remains to be done in the following areas: • Building partnerships between computer science departments and other disciplines in higher education to develop shared models for conceptualization, implementation and evaluation of CT in a variety of contexts. • Establish methods and strategies as well as examples and cases for teaching CT in various non-technical disciplines, especially the social sciences, humanities and education. • Evaluate the resources needed for broader implementation of computational thinking classes in colleges. • Research the outcomes of teaching CT, especially in non-technical disciplines. While the above list is incomplete it also presents a good starting point to make CT a cross-disciplinary skill. There still exists the need for curricular studies that require development of quality learning and teaching tools and assessment procedures for higher education students. In addition, learning outcomes across the disciplines that result in computational competencies should be studied further. Finally, attitudinal and meta-cognitive aspects of computational thinking must be understood with additional investigation. Higher education is a complex and loosely coupled environment where change takes time. Attaching computational thinking to our current list of discussions, however, and seeking deeper 64
understanding of its outcomes will yield future benefits across multiple academic disciplines. Betul Czerkawski is an Associate Professor of Educational Technology and Program Director at the University of Arizona- South Campus, Tucson, AZ Her research interests include instructional design, emerging technologies, and technology integration in online learning environments. Address correspondence regarding this article to her via email at:
[email protected] Eugene W. Lyman III is a Web Developer with the Student Affairs Systems Group at the University of Arizona. He recently completed the degree of Master of Science in Educational Technology at the University of Arizona- South Campus. His research interests include the history of educational technology, computational thinking methods in pedagogy, and evidencebased instructional design methods.
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of Society for Information Technology & Teacher Education International Conference 2013 (pp. 10-17). Chesapeake, VA: AACE. Denning, P. J. (2007). Computing is a natural science. Communications of the ACM, 50(7), 13-18. Denning, P. J. (2009). The profession of IT: Beyond computational thinking. Communications of the ACM, 52(6), 28-30. Dodig-Crnkovic, G. (2011). Significance of models of computation, from Turing model to natural computation. Minds & Machines, 21(2), 301-322. doi:10.1007/s11023011-9235-1. Drucker, J. (2011) Humanities approaches to graphical display. DHQ: Digital Humanities Quarterly 5(1) http:// digitalhumanities.org:8080/dhq/vol/5/1/000091/000091. html. Edwards, M. (2011). Algorithmic composition: Computational thinking in music. Communications of the ACM, 54(7), 58-67. doi:10.1145/1965724.1965742 Frohock, F. M. (1997). The boundaries of public reason. The American Political Science Review, 91(4), 833-844. Guzdial, M. (2008). Education: Paving the way for computational thinking. Communications of the ACM, 51(8), 25-27. Hambrusch, S., Hoffmann, C., Korb, J. T., Haugan, M., & Hosking, A. L. (2009). A multidisciplinary approach towards computational thinking for science majors. SIGCSE Bulletin, 41(1), 183-7 Henderson, P. B. (2009). Ubiquitous computational thinking. Computer, 42(10), 100-102. Heuser, R. & Le-Khac. L. (2012). Literary Lab Pamphlet 4: A Quantitative Literary History of 2,958 Nineteenth-Century British Novels: The Semantic Cohort Method. Retrieved from http://litlab.stanford.edu/ LiteraryLabPamphlet4.pdf. Hong Qin. (2009). Teaching computational thinking through bioinformatics to biology students. SIGCSE Bulletin, 41(1), 188-91. Kazimoglu, C., Kiernan, M., Bacon, L., & MacKinnon, L. (2011). Understanding computational thinking before programming: Developing guidelines for the design of games to learn introductory programming through game-play. International Journal of Game-Based Learning, 1(3), 30-52. doi:10.4018/ijgbl.2011070103. Kazimoglu, C., Kiernan, M., Bacon, L. & Mackinnon, L. (2010). Developing a game model for computational thinking and learning traditional programming through game-play. In J. Sanchez & K. Zhang (Eds.), Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2010 (pp. 1378-1386). Chesapeake, VA: AACE. Retrieved from http://www.editlib.org.ezproxy1.library.arizona. edu/p/35747. Kilpelainen, P. (2010). Do all roads lead to Rome? (or reductions for dummy travelers). Computer Science Education, 20 (3), 181-199. Liu, C., Cheng, Y., & Huang, C. (2011). The effect of simulation games on the learning of computational problem solving. Computers & Education, 57(3), 19071918.
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Margolis, J., Goode, J., Holme, J. J. & Nao, K. (2010). Stuck in the shallow end: Education, race, and computing. Cambridge, MA: The MIT Press. Mishra, P. & Yadav, A. (2013). Rethinking technology & creativity in the 21st century. TechTrends: Linking Research & Practice to Improve Learning, 57(3), 10-14. Miller, C. S., & Settle, A. (2011). When practice doesn’t make perfect: Effects of task goals on learning computing concepts. ACM Transactions on Computing Education, 11(4). Moretti, F. (2011). Literary Lab Pamphlet 2: Network Theory, Plot Analysis Retrieved from: http://litlab. stanford.edu/LiteraryLabPamphlet2.pdf. Navlakha, S., & Bar-Joseph, Z. (2011). Algorithms in nature: The convergence of systems biology and computational thinking. Molecular Systems Biology, 7, 546. Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York, NY: Basic Books. Pearson, K. (2009). From a usable past to a collaborative future: African American culture in the age of computational thinking. Black History Bulletin, 72(1), 4144. Perlis, A. (1962). The Computer in the University. In M. Greenberger, Ed. Computers and the World of the Future (pp. 180-219). Cambridge, MA: MIT Press. Perry, R., Turkle, S., & Papert, S. (1990). Epistemological pluralism: Styles and voices within the computer culture. Signs, 16(1), 128. Petrovic, V., Gidding, A., Wypych, T., Kuester, F., DeFanti, T., & Levy, T. (2011). Dealing with archaeology’s data avalanche. Computer, 44(7), 56-60. doi:10.1109/ MC.2011.161. Rödelsperger, C., & Sommer, R. J. (2011). Computational archaeology of the pristionchus pacificus genome reveals evidence of horizontal gene transfers from insects. BMC Evolutionary Biology, 11(1), 239-249. doi:10.1186/14712148-11-239. Shell, D. F., & Soh, L. (2013). Profiles of motivated self-regulation in college computer science courses: Differences in major versus required non-major courses. Journal of Science Education and Technology, doi:10.1007/ s10956-013-9437-9 Soh, L-K., Samal, A., Scott, S., Ramsay, S., Moriyama, E., Meyer, G., Moore, B., T.G. Thomas & Shell, D. F. (2009). Renaissance computing: An initiative for promoting student participation in computing. SIGCSE Bulletin, 41(1), 59-63. Varma, R. (2006). Making computer science minorityfriendly. Communications of the ACM 49(2) 129-134. Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. Wing, J. (2010). Computational thinking: What and why? Retrieved from http://www.cs.cmu.edu/~CompThink/ resources/TheLinkWing.pdf. Xu, L., & Czerkawski, B. (2012). Designing online course components to infuse computational thinking in computer science. Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education 2012, 459-464.
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