National Science Education Standards

Jeannette Wing’s influential article on computational thinking 6 years ago argued for adding this new competency to every child’s analytical ability as a vital ingredient of science, technology, engineering, and mathematics (STEM) learning. What is computational thinking? Why did this article resonate with so many and serve as a rallying cry for educators, education researchers, and policy makers? How have they interpreted Wing’s definition, and what advances have been made since Wing’s article was published? This article frames the current state of discourse on computational thinking in K–12 education by examining mostly recently published academic literature that uses Wing’s article as a springboard, identifies gaps in research, and articulates priorities for future inquiries.

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Computational Thinking in K–12 A Review of the State of the Field

We present steps toward a conceptual framework for tangible user interfaces. We introduce the MCRpd interaction model for tangible interfaces, which relates the role of physical and digital representations, physical control, and underlying digital models. This model serves as a foundation for identifying and discussing several key characteristics of tangible user interfaces. We identify a number of systems exhibiting these characteristics, and situate these within 12 application domains. Finally, we discuss tangible interfaces in the context of related research themes, both within and outside of the human-computer interaction domain.

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Emerging frameworks for tangible user interfaces

Massive open online courses (MOOCs) are a recent addition to the range of online learning options. Since 2008, MOOCs have been run by a variety of public and elite universities, especially in North America. Many academics have taken interest in MOOCs recognising the potential to deliver education around the globe on an unprecedented scale; some of these academics are taking a research-oriented perspective and academic papers describing their research are starting to appear in the traditional media of peer reviewed publications. This paper presents a systematic review of the published MOOC literature (2008-2012): Forty-five peer reviewed papers are identified through journals, database searches, searching the Web, and chaining from known sources to form the base for this review. We believe this is the first effort to systematically review literature relating to MOOCs, a fairly recent but massively popular phenomenon with a global reach. The review categorises the literature into eight different areas of interest, introductory, concept, case studies, educational theory, technology, participant focussed, provider focussed, and other, while also providing quantitative analysis of publications according to publication type, year of publication, and contributors. Future research directions guided by gaps in the literature are explored.

https://centaur.reading.ac.uk/33109/6/MOOCs_%20AsystematicStudyOfThePublishedLiterature2008-2012.pdf

MOOCs: A systematic study of the published literature 2008-2012

This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: (1) opacity as intentional corporate or state secrecy (2) opacity as technical illiteracy, and (3) an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices (as they are publicly presented), and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is key to determining which of a variety of technical and non-technical solutions could help to prevent harm.

How the Machine 'Thinks:' Understanding Opacity in Machine Learning Algorithms

Computational thinking (CT) has been described as the use of abstraction, automation, and analysis in problem-solving [3]. We examine how these ways of thinking take shape for middle and high school youth in a set of NSF-supported programs. We discuss opportunities and challenges in both in-school and after-school contexts. Based on these observations, we present a "use-modify-create" framework, representing three phases of students' cognitive and practical activity in computational thinking. We recommend continued investment in the development of CT-rich learning environments, in educators who can facilitate their use, and in research on the broader value of computational thinking.

Computational thinking for youth in practice

In many educational settings, manipulative materials (such as Cuisenaire Rods and Pattern Blocks) play an important role in children’s learning, enabling children to explore mathematical and scientific concepts (such as number and shape) through direct manipulation of physical objects. Our group at de MJT Media Lab has developed a new generation of “digital manipulatives”-computationallyenhanced versions of traditional children’s toys. These new manipulatives enable children to explore a new set of concepts (im particular, “systems concepts” such as feedback and emergence) that have previously been considered “too advanced” for children to learn. In this paper, we discuss four of our digital manipulatives-computationallyaugmented versions of blocks, beads, balls, and badges.

https://llk.media.mit.edu/courses/readings/digital-manipulatives.pdf

Digital manipulatives: new toys to think with

Sharing recent experiences with an online course.

Will massive open online courses change how we teach

In this paper, we discuss the applications and implications of the Programmable Brick—a tiny, portable computer embedded inside a LEGO ® brick, capable of interacting with the physical world in a large variety of ways. We describe how Programmable Bricks make possible a wide range of new design activities for children, and we discuss experiences in using Programmable Bricks in three types of applications: autonomous creatures, active environments, and personal science experiments.

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Programmable bricks: toys to think with

The stereo has many attractions: it is easier to play and it provides immediate access to a wide range of music. But "ease of use" should not be the only criterion. Playing the piano can be a much richer experience. By learning to play the piano, you can become a creator (not just a consumer) of music, expressing yourself musically in ever-more complex ways. As a result, you can develop a much deeper relationship with (and deeper understanding of) music. So too with computers. In the field of educational technology, there has been too much emphasis on the equivalent of stereos and CDs, and not enough emphasis on computational pianos. In our research group at the MIT Media Lab, we are developing a new generation of "computational construction kits" that, like pianos, enable people to express themselves in ever-more complex ways, deepening their relationships with new domains of knowledge.

Fred Martin

Papers

Computational thinking for youth in practice

Digital manipulatives: new toys to think with

Will massive open online courses change how we teach

Programmable bricks: toys to think with

Pianos not stereos: creating computational construction kits