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Case Report

The AI-Atlas: Didactics for Teaching AI and Machine Learning On-Site, Online, and Hybrid

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CAI Centre for Artificial Intelligence, ZHAW Zurich University of Applied Sciences, Obere Kirchgasse 2, 8400 Winterthur, Switzerland
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ZHAW Digital, ZHAW Zurich University of Applied Sciences, Gertrudstrasse 15, 8400 Winterthur, Switzerland
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IDP Institute of Data Analysis and Process Design, ZHAW Zurich University of Applied Sciences, Rosenstrasse 3, 8400 Winterthur, Switzerland
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ICE Institute for Computational Engineering, OST Eastern Switzerland University of Applied Sciences, Werdenbergstrasse 4, 9471 Buchs, Switzerland
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Author to whom correspondence should be addressed.
ECLT European Centre for Living Technology, 30123 Venice, Italy.
Academic Editor: Georgios N. Kouziokas
Educ. Sci. 2021, 11(7), 318; https://doi.org/10.3390/educsci11070318
Received: 31 March 2021 / Revised: 18 June 2021 / Accepted: 22 June 2021 / Published: 25 June 2021
(This article belongs to the Special Issue Artificial Intelligence and Education)
We present the “AI-Atlas” didactic concept as a coherent set of best practices for teaching Artificial Intelligence (AI) and Machine Learning (ML) to a technical audience in tertiary education, and report on its implementation and evaluation within a design-based research framework and two actual courses: an introduction to AI within the final year of an undergraduate computer science program, as well as an introduction to ML within an interdisciplinary graduate program in engineering. The concept was developed in reaction to the recent AI surge and corresponding demand for foundational teaching on the subject to a broad and diverse audience, with on-site teaching of small classes in mind and designed to build on the specific strengths in motivational public speaking of the lecturers. The research question and focus of our evaluation is to what extent the concept serves this purpose, specifically taking into account the necessary but unforeseen transfer to ongoing hybrid and fully online teaching since March 2020 due to the COVID-19 pandemic. Our contribution is two-fold: besides (i) presenting a general didactic concept for tertiary engineering education in AI and ML, ready for adoption, we (ii) draw conclusions from the comparison of qualitative student evaluations (n = 24–30) and quantitative exam results (n = 62–113) of two full semesters under pandemic conditions with the result of previous years (participants from Zurich, Switzerland). This yields specific recommendations for the adoption of any technical curriculum under flexible teaching conditions—be it on-site, hybrid, or online. View Full-Text
Keywords: flexible educational design; e-learning; constructivism; design-based research; COVID-19; post-pandemic tertiary engineering education; artificial intelligence flexible educational design; e-learning; constructivism; design-based research; COVID-19; post-pandemic tertiary engineering education; artificial intelligence
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MDPI and ACS Style

Stadelmann, T.; Keuzenkamp, J.; Grabner, H.; Würsch, C. The AI-Atlas: Didactics for Teaching AI and Machine Learning On-Site, Online, and Hybrid. Educ. Sci. 2021, 11, 318. https://doi.org/10.3390/educsci11070318

AMA Style

Stadelmann T, Keuzenkamp J, Grabner H, Würsch C. The AI-Atlas: Didactics for Teaching AI and Machine Learning On-Site, Online, and Hybrid. Education Sciences. 2021; 11(7):318. https://doi.org/10.3390/educsci11070318

Chicago/Turabian Style

Stadelmann, Thilo, Julian Keuzenkamp, Helmut Grabner, and Christoph Würsch. 2021. "The AI-Atlas: Didactics for Teaching AI and Machine Learning On-Site, Online, and Hybrid" Education Sciences 11, no. 7: 318. https://doi.org/10.3390/educsci11070318

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