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“Hidden” Integration of Industrial Design-Tools in E-Learning Environments

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Cross Reality and Data Science in Engineering (REV 2020)

Abstract

One of the problems that has emerged during the intensive usage of the GOLDi remote lab over the years is the usage of external third-party development tools, necessary for the software- or hardware-oriented design. The installation and setup (e.g. of Atmel Studio or Intel Quartus Prime) on a private PC or laptop is quite complicated, requires some expert knowledge to use these tools safely and effectively, and is not always platform-independent. Above this, these IDE occupy several gigabytes in memory size and sometimes take hours to install. These very powerful tools, of which only a fraction of the offered functionality is needed to solve the given educational task, generate a large overhead. In addition, high school and university students may have only little or no knowledge of microcontrollers, FPGA, compiler linker toolchain, hardware-related languages, etc.

That is why we developed a new tool as an integral part of the GOLDi remote lab infrastructure – called WIDE (WEB IDE) – which will be described in this paper. WIDE supports all the design flows with the only requirement of an Internet browser. This means WIDE is running in standalone mode to write and compile code in the specific language or directly inside the Experiment Control Panel (ECP) of the student’s Web browser.

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Acknowledgment

The authors would like to acknowledge the work of Pierre Helbing for the Arduino integration and templates, Jan Höpfner for the development of the Language Server, Sven Mollenhauer for the WIDE backend implementation, Luisa Bortz, Ziyang Song, Eric Winzer for the software project WIDE as well as Florian Köhler, Patrick Langer, Söhnke-Benedikt Fischedick for their contribution to the ELWS project.

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Correspondence to Karsten Henke .

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Henke, K., Nau, J., Hutschenreuter, R., Bock, RN., Wuttke, HD. (2021). “Hidden” Integration of Industrial Design-Tools in E-Learning Environments. In: Auer, M., May, D. (eds) Cross Reality and Data Science in Engineering. REV 2020. Advances in Intelligent Systems and Computing, vol 1231. Springer, Cham. https://doi.org/10.1007/978-3-030-52575-0_36

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