Report of a Workshop on the Pedagogical Aspects of Computational Thinking (2011) / Chapter Skim
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3 Committee Member Perspectives
3 Committee Member Perspectives
Pages 36-64
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From page 36... ...
First, he argued, computational thinking has an impact on virtually every area of human endeavor, as illustrated by the first work shop report's discussion of computational thinking applications in fields as diverse as law, medicine, archeology, journalism, and biology. Second, he noted dangers in computational thinking done badly.
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From page 37... ...
Recalling Idit Caperton's thoughts that using information technology in an appropriate manner "engages people, engages their souls, their passion, and their productiv ity, and people care," Aho described similar experiences in working with undergraduates. He found that using creative programming projects to hone and develop computational thinking skills motivated students to pursue further education in computer science.
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From page 38... ...
Finally, he also suggested that developmental psychology might have value in contributing to different pedagogical models for learners with different cognitive styles and in shaping the infrastructure and tools needed to teach computational thinking. Infrastructure for Computational Thinking Addressing the issue of the infrastructure needed to support a seri ous educational effort to promote computational thinking broadly, Aho noted that such an infrastructure did not consist only of hardware but also necessarily included continuing funding streams, instruments for gathering data needed to analyze outcomes, and an ongoing data collec tion effort.
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From page 39... ...
the kind of thought process that a student is following to get the bunny to eat these carrots, I am not sure what the student is actually learning about some of these much deeper issues that a serious programmer would have to face." 3.2 URI WILENSKY Committee member Uri Wilensky, a Northwestern University professor and director of the Center for Connected Learning and ComputerBased Modeling, shared his observations on a number of key issues dis cussed at the workshop, including the motivation and value in teaching computational thinking, the challenges arising from the continuing nonconvergence on one definition of computational thinking, and identification of the best environment and tools for conveying computational thinking to different audiences. Motivation Wilensky noted that in recent years, many branches of science and engineering have changed in ways that require researchers to be facile with computational thinking.
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From page 40... ...
A second reason for encouraging computational thinking is the power that it affords for greater automation of tedious tasks and the ability to manage more complexity for all types of learning and discovery. Mechanical automation allows one to delegate certain tedious tasks and simple problem solving in favor of more complex tasks and problem solving.
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From page 41... ...
(This diversity of perspective was also reflected in the first workshop report.) Specifically, he thought that the different ways of understanding computational thinking discussed in the second workshop fell into several categories: ways of seeing and knowing, ways of doing or capacities, a method of inquiry, and ways of collaborating.
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From page 42... ...
Wilen sky held that "instead of a spatial model of collaboration, we have this kind of network model of collaboration where there are many different opportunities for synching up, and that capacity is becoming more and more important in our society, and computation is another way to facili tate that." A Diversity of Venues for Computational Thinking Represented at the workshop were a number of different perspectives regarding the most effective environment and tools for teaching computational thinking. Wilensky distilled the points of view as those favoring formal curricular learning versus extracurricular learning and those favoring lab-based learning versus in-the-field learning.
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From page 43... ...
To this point, Wilensky responded that this may be more a strategic discus sion rather than a pedagogical one. Work presented by Paulo Blikstein and others shows that there is room even in current science curricula to introduce computational thinking concepts in a way that they fit, and also mutually support learning of other complex concepts.
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From page 44... ...
Create and Modify as Complementary Approaches Wilensky noted the difference between students writing programs starting from a blank screen versus students modifying existing programs, but argued that both approaches have value in conveying concepts of computational thinking. However, he did caution that the canonical "use-modify-create" sequence is not the only viable approach to teaching the skills of computational thinking.
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From page 45... ...
They included a disciplineoriented approach for identifying key facets of computational thinking, a developmental progression approach for teaching, a real-world problem-solving approach for identifying concepts and teaching, and a cycle approach (use-modify-create) for teaching and assessing learning.
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From page 46... ...
develop all the programming on their own, especially if they don't have any prior competencies in it." She argued that learning to use the code and manipulate it is a good way to try out strategies before designing one's own programs. In addition, the cycle approach works across the disciplines and can be used to facilitate computational learn ing based in data analysis, visualization, and game design approaches to teaching computational thinking.
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From page 47... ...
Linn argued that computational thinking is a powerful concept that by its very nature involves multiple disciplines. She recommended characterizing the trajectory of computational thinking from elementary to college courses.
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From page 48... ...
Linn argued that computational thinking would be most effective when integrated into specific disciplines rather than as a stand-alone course. Linn remarked, "It seems more efficient to take a disciplinary course and create activities that use computational ideas to advance understanding, but the case could be made for other solutions." She noted the challenges associated with incorporating computational thinking into the already-packed school curriculum.
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From page 49... ...
We need to think about ways to build coherent understanding of computa tional thinking as students encounter it across disciplines." Linn saw the overarching goal of the workshop as being to catalyze thought about the steps needed to make computational thinking central to all of education. Linn also commented on the nature of the curricular materials available for teaching computational thinking.
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From page 50... ...
A one-time summer workshop will not be sufficient. Computational thinking education cannot succeed in the long term without several teachers at every school doing the same thing, "because if you don't have a community, you don't have anything that can sustain this kind of exciting, innovative work." As far as options to start this integration, there was quite a bit of dis cussion at the workshop as to whether the best initial approach would be to start with the informal extracurricular activities or the typical school curricula to incorporate computational thinking.
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From page 51... ...
Snyder acknowledged this point, noting that young learners are capable of creat ing programs, even before they can read programs from other people; in fact, students likely prefer to create their own original programs, which in turn may motivate them to learn more computational thinking skills and concepts. Snyder made several points highlighting some of the different approaches available for teacher education and development related to computational thinking.
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From page 52... ...
Kolodner found that some of the discussions delved very deeply into many practical issues associated with developing computational thinking curricula. In particular, she found presentations by Robert Tinker, Mitch Resnick, and Robert Panoff particularly impressive in this respect.
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From page 53... ...
Definitions of Computational Thinking Kolodner argued that the computational thinking community needs to be able to identify exactly what is meant by computational thinking to decide what learners should learn and to assess and evaluate what learners know, what they can do, and their attitudes and capabilities with respect to computational thinking. The community must be specific about the definition of computational thinking.
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From page 54... ...
This view of computational thinking is consistent with systems thinking and with model-based reasoning, both of which play a huge role both in scientific reasoning and in engaging in computational sciences. Indeed, both Tinker and Panoff proposed integrating model building, simulation, and model-based reasoning into math and science classes as a way to engage kids in computational thinking as they are getting to greater understanding and raising and solving problems in mathematical and scientific domains.
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From page 55... ...
This requires fluency with computational media. Relationship Between Two Views of Computational Thinking Kolodner argued that a deep understanding of computational thinking may encompass a synthesis of these two views.
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From page 56... ...
Kolodner believes this blurred relation ship is "a really interesting conundrum that needs more attention from the research community." Helping People Learn to Be Computational Thinkers Presenter Derek Briggs of the University of Colorado, Boulder, put forth a question during one of the panel discussions that Kolodner found helpful in articulating how to promote computational thinking. Briggs questioned the goals sought with respect to learning computational thinking.
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From page 57... ...
Kolodner believes it is important not to fall prey to the mistaken notion that if one learns computational thinking skills in one context, one will automatically be able to use them in another context. Rather, it will be important to remember that one can learn to use computational thinking skills across contexts only if (1)
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From page 58... ...
Kolodner thought that many of the examples of computational thinking learning discussed in the workshop reflected adoption of this approach to teaching computational thinking, with varying levels of success. One example was Tinker's learning progression for learning computational thinking in a science class, learning that involved the following: • umbers are associated with things and their interactions (e.g., N temperature)
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From page 59... ...
Kolodner expressed concern over a thread of discussion running through some of the presentations that seemed to presume that as a part of the process of learning to program, students would learn computational thinking. For Kolodner, a big question is how an instructor can be sure that students engaging in programming activities are actually learning computational thinking.
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From page 60... ...
Kolodner believes that a student's reflecting on a computational activity, being able to teach or help someone else learn the concepts, or being able to effectively articulate the relevant computa tional process at issue can be seen as likely indications that the student is learning computational thinking. As students are able to use increasingly elegant, efficient, and sophisticated approaches to tackle computational thinking tasks, this ability can also demonstrate learning and improve ment in computational thinking, Kolodner believes.
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From page 61... ...
Kolodner agreed with presenter Cathy Lachapelle of the Museum of Science, Boston, who also discussed evaluation, specifically with respect to the need for usability in a computational thinking project in order to incorporate computational thinking effectively into a curriculum and make it widely available. In response to discussion from Lachapelle, Kolodner said that the computational thinking community should consider at some point creat
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From page 62... ...
Kolodner also felt that reflection on pedagogical content knowledge with respect to computational thinking is important for instructors of computational thinking. In response to Michelle Williams's presentation, Kolodner asked for more information about how the reflection questions were developed that were posed to teachers after they had completed a teacher development computational thinking learning project.
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From page 63... ...
Maybe at the moment they are having them, maybe later, but the talking uncovers things that you might not see otherwise." Kolodner believes that tracking of activities seems particularly important to analyzing computational thinking. Whether Blikstein's log files, or Schwarz's interviewing to help track thinking, or Clancy's noting details of collaborative discussions, such tracking enables particularly important and informative project assessment and evaluation.
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From page 64... ...
To illustrate, Blake pointed back to Henderson's Thomas the Train example and suggested that a simple activity with embedded computational thinking challenges might be a means of identifying talent. Concerning the idea of training, Blake argued that by taking opportunities to identify and assess computational thinking talent in individual students, and to start to enumerate indicators of such talent, a researcher or an educator might be able to recognize when a student either is demonstrating a significant talent in computational thinking or is at least at the appropriate learning progression level for that age range.
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