[2210.13820] Supporting Electronics Learning through Augmented Reality
Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Human-Computer Interaction

arXiv:2210.13820 (cs)
[Submitted on 25 Oct 2022]

Title:Supporting Electronics Learning through Augmented Reality

Authors:Thomas Kosch, Julian Rasch, Albrecht Schmidt, Sebastian Feger
View PDF
Abstract:Understanding electronics is a critical area in the maker scene. Many of the makers' projects require electronics knowledge to connect microcontrollers with sensors and actuators. Yet, learning electronics is challenging, as internal component processes remain invisible, and students often fear personal harm or component damage. Augmented Reality (AR) applications are developed to support electronics learning and visualize complex processes. This paper reflects on related work around AR and electronics that characterize open research challenges around the four characteristics functionality, fidelity, feedback type, and interactivity.
Subjects: Human-Computer Interaction (cs.HC)
ACM classes: H.5.1
Cite as: arXiv:2210.13820 [cs.HC]
  (or arXiv:2210.13820v1 [cs.HC] for this version)
  https://doi.org/10.48550/arXiv.2210.13820

Submission history

From: Thomas Kosch [view email]
[v1] Tue, 25 Oct 2022 07:53:50 UTC (10,317 KB)
Full-text links:

Access Paper:

  • View PDF
  • TeX Source
  • Other Formats
license icon view license
Current browse context:
cs.HC
< prev   |   next >
new | recent | 2022-10
Change to browse by:
cs

References & Citations

export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)