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I hypothesize that one solution is to empower students by providing them with direct access to AR development tools and grounding their learning through computational action. My work focuses on creating such augmented reality development tools, which are comprehensible and employable by anyone. I built upon MIT App Inventor, a blocks-based mobile development tool, to democratize augmented reality application development. I developed a set of 20 augmented reality components, which allows users to build iOS augmented reality applications and enables the development of curriculum that promotes computational action with AR.

I created a set of tutorials for the AR components and designed sample applications using those components with increasing levels of complexity. Finally, I conducted a pilot study with MIT students where they built applications using the augmented reality components.

My findings indicate that study participants felt empowered by working with the AR components. Participants, who originally doubted their ability to create AR applications, believed that they could build augmented reality applications after using the AR components. Accepted thesis Evan W. Patton, Michael Tissenbaum, and Farzeen Harunani In: Computational Thinking Education, S. Abelson Eds. Abstract: MIT App Inventor is an online platform designed to teach computational thinking concepts through development of mobile applications. Students create applications by dragging and dropping components into a design view and using a visual blocks language to program application behavior.

In this chapter, we discuss 1 the history of the development of MIT App Inventor, 2 the project objectives of the project and how they shape the design of the system, and 3 the processes MIT uses to develop the platform and how they are informed by computational thinking literature.

Key takeaways include use of components as abstractions, alignment of blocks with student mental models, and the benefits of fast, iterative design on learning. Abstract: Computational action, by which learners are given opportunities to build projects with real-world applicability, is, we argue, a preferable way to engage young people in learning how to program computers. Mobile computing, as the dominant form-factor of computing currently, provides an especially meaningful opportunity for youth to create these projects.

Meanwhile, platforms with powerful abstractions, that make operations that might otherwise be difficult simple, build student capability to create projects that enhance their computational action. Position Paper Natalie Lao, Abstract: Data sharing through Cloud technology is one of the most powerful new computer science concepts of the past few decades.

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As such, developing powerful and easy-to-use tools for incremental learning and application of shared data concepts is an important endeavor. My work focuses on using MIT App Inventor, a popular blocks-based mobile application development tool for teaching computational thinking to young students, to make shared data technology understandable and usable by anyone without the need for extensive computer science training. I present the deployment of CloudDB, a set of coding blocks for MIT App Inventor that allows users to store, retrieve, and share various types of data in tag-value pairs on a Redis server for their mobile applications.

Demo abstract Evan W.

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Panel: Block Abstractions for Artificial Intelligence. Abstract: Block-based programming languages, such as Scratch and MIT App Inventor, make computational concepts accessible to those as young as primary school students. As technology advances, so do the abilities of such block-based programming languages. This panel will discuss the state-of-the-art in artificial intelligence AI abstractions in block-based languages. Specifically, it will address block-based AI challenges, pedagogical value, and applications.

Organized Panel Evan W. Patton, Mark Sherman, and Michael Tissenbaum, Abstract: Understanding how ideas move through a classroom as students explore, learn, and share their findings with other students is important for computational thinking education. We present a set of enhancements to MIT App Inventor to enable fine-grained analysis of both qualitative and quantitative data. Our framework combines real-time event streams, project snapshots, and screen captures with audio recording to allow for targeted queries around when components or concepts not previously seen appear in a project.

This automated approach reduces the amount of time spent sifting through data by providing researchers new tools for performing analysis of App Inventor projects. Short Paper Evan W. Patton and Danny Tang, Abstract: Block languages abstract away the syntax of languages and allow for people to focus on the semantics of a program. Text languages, however, can make use of a variety of syntactical sugar to provide abbreviated means of unpacking complex data structures.

We present a use case involving a complex data structure in the JavaScript Object Notation and show how nested elements would be accessed using the MIT App Inventor platform. We then introduce a new block to show how further abstractions within the blocks language can simplify access while making it more readable, more compact, and easier to construct.

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Abstract: Internet of Things IoT integrates physical devices and creates opportunities for people to interact with the surrounding environment. While a number of blocks-based approaches exist for programming some hardware, such as Snap! In this paper, we propose a block-based programming approach using MIT App Inventor to enable novices be able to build mobile apps integrated with IoT technology. We also review other block languages applied for IoT.

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We conclude with some thoughts on how blocks languages might inspire people to create with IoT. Position Paper J. Van Brummelen and H. Abstract: This work aims to democratize conversational AI technology. Currently, MIT App Inventors block-based coding tools empower anyone to develop their own mobile apps. Soon, users will also be able to program their own Alexa skills, conversational AI, and neural networks using blocks.

Through middle school workshops, research questions, such as "Can students learn about the capabilities, limitations and implications of AI through developing conversational AI mobile apps? Poster S.

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Kong, D. Andone, G. Biswas, G. Crick, T. Hoppe, T. Hsu, R. Huang, K. Li, C.

Looi, M. Milrad, J. Sheldon, J. Shih, K.

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Sin, M. Vahrenhold Eds.

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  • Abstract: Development of Computational Thinking CT is an area of many initiatives in the last years, due to the importance of having CT skills. There are many environments that allow learners to develop such skills, for instance Scratch and MIT App Inventor, in a visual and intuitive way. As in professional software development, assisting tools that help and guide learners are starting to appear.

    In this paper, we discuss the current status of these tools, based on an analysis of what state-of-the-art CT assessment tools, such as Dr. We report their limitations and envision and discuss future enhancements. Peer Reviewed Paper C. Tseng, M. Tissenbaum, W. Kuan, F.

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    Hsu, C. A design-based approach to implementing a computational thinking curriculum with App Inventor and the Internet of Things, CTE , July Abstract: The growing ubiquity of everyday devices connected over the internet, know generally as the Internet of Things IoT , has opened up new avenues for students to explore their worlds and think and create computationally. Combining IoT with mobile technologies such as smartphones , enables students to move their designs and computational thinking out of traditional classroom settings and into the real world.

    This article outlines a design-based IoT curriculum that connects Taiwanese students with the personally-relevant issue of air pollution. This paper reports on changes to the curriculum based on a preliminary pilot and observations of student engagement during the most recent enactment. Peer Reviewed Paper L.

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    Constrained Sets: The Effects of Multi-Layered Environments in Learning App Inventor term paper, Massachusetts Institute of Technology, June Abstract: MIT App Inventor is a mobile application development platform that seeks to democratize the construction of mobile apps by making app development accessible to people with little to no experience with script-based programming. It uses block-based programming to introduce and teach programming concepts to its users.

    Users drag and drop functional and visual components onto their planned app in the screen editor, and construct the logic behind those components by using blocks in the block editor. In this thesis, we design and implement Constrained Sets, a system that allows instructors and developers to allow access to only a subset of App Inventor functionality by hiding component and block access. This system allows for the construction of multi-layered interfaces, which we then use to conduct an experiment that explores how novice App Inventor users learn App Inventor in different interface environments.

    Furthermore, we discuss and test the possibility of using a React based implementation of the App Inventor designer, and what implications that may have on creating more flexible user interfaces.