LearnBlock: A Robot-Agnostic Educational Programming Tool


Education is evolving to prepare students for the current sociotechnical changes. An increasing effort to introduce programming and other STEM-related subjects into the core curriculum of primary and secondary education is taking place around the world. The use of robots stands out among STEM initiatives, since robots are proving to be an engaging tool for learning programming and other STEM-related contents. Block-based programming is the option chosen for most educational robotic platforms. However, many robotics kits include their own software tools, as well as their own set of programming blocks. LearnBlock, a new educational programming tool, is proposed here. Its major novelty is its loosely coupled software architecture which makes it, to the best of our knowledge, the first robot-agnostic educational tool. Robot-agnosticism is provided not only in block code, but also in generated code, unifying the translation from blocks to the final programming language. The set of blocks can be easily extended implementing additional Python functions, without modifying the core code of the tool. Moreover, LearnBlock provides an integrated educational programming environment that facilitates a progressive transition from a visual to a general-purpose programming language. To evaluate LearnBlock and demonstrate that it is platform-agnostic, several tests were conducted. Each of them consists of a program implementing a robot behaviour. The block code of each test can run on several educational robots without changes.

Publication DOI: https://doi.org/10.1109/ACCESS.2020.2972410
Additional Information: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
Uncontrolled Keywords: Educational tool,learning programming,robot-agnostic,software architecture,Computer Science(all),Materials Science(all),Engineering(all)
Publication ISSN: 2169-3536
Last Modified: 29 Apr 2024 17:50
Date Deposited: 19 Feb 2020 16:09
Full Text Link:
Related URLs: https://ieeexpl ... ocument/8986589 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2020-02-18
Published Online Date: 2020-02-07
Accepted Date: 2020-02-02
Authors: Bachiller, Pilar
Barbecho, Ivan
Calderita, Luis V.
Bustos, Pablo
Manso, Luis J. (ORCID Profile 0000-0003-2616-1120)



Version: Published Version

License: Creative Commons Attribution

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