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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-22751
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dc.contributor.authorStadelmann, Thilo-
dc.contributor.authorKeuzenkamp, Julian-
dc.contributor.authorGrabner, Helmut-
dc.contributor.authorWürsch, Christoph-
dc.date.accessioned2021-07-01T10:06:50Z-
dc.date.available2021-07-01T10:06:50Z-
dc.date.issued2021-06-25-
dc.identifier.issn2227-7102de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/22751-
dc.description.abstractWe 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.de_CH
dc.language.isoende_CH
dc.publisherMDPIde_CH
dc.relation.ispartofEducation Sciencesde_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectFlexible educational designde_CH
dc.subjectE-learningde_CH
dc.subjectConstructivismde_CH
dc.subjectDesign-based researchde_CH
dc.subjectCOVID-19de_CH
dc.subjectPost-pandemic tertiary engineering educationde_CH
dc.subjectArtificial intelligencede_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc378: Hochschulbildungde_CH
dc.titleThe AI-Atlas : didactics for teaching AI and machine learning on-site, online, and hybridde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitCentre for Artificial Intelligence (CAI)de_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.3390/educsci11070318de_CH
dc.identifier.doi10.21256/zhaw-22751-
zhaw.funding.euNode_CH
zhaw.issue7de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.start318de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume11de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedMachine Perception and Cognitionde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedZHAW digitalde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Stadelmann, T., Keuzenkamp, J., Grabner, H., & Würsch, C. (2021). The AI-Atlas : didactics for teaching AI and machine learning on-site, online, and hybrid. Education Sciences, 11(7), 318. https://doi.org/10.3390/educsci11070318
Stadelmann, T. et al. (2021) ‘The AI-Atlas : didactics for teaching AI and machine learning on-site, online, and hybrid’, Education Sciences, 11(7), p. 318. Available at: https://doi.org/10.3390/educsci11070318.
T. Stadelmann, J. Keuzenkamp, H. Grabner, and C. Würsch, “The AI-Atlas : didactics for teaching AI and machine learning on-site, online, and hybrid,” Education Sciences, vol. 11, no. 7, p. 318, Jun. 2021, doi: 10.3390/educsci11070318.
STADELMANN, Thilo, Julian KEUZENKAMP, Helmut GRABNER und Christoph WÜRSCH, 2021. The AI-Atlas : didactics for teaching AI and machine learning on-site, online, and hybrid. Education Sciences. 25 Juni 2021. Bd. 11, Nr. 7, S. 318. DOI 10.3390/educsci11070318
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 (7): 318. https://doi.org/10.3390/educsci11070318.
Stadelmann, Thilo, et al. “The AI-Atlas : Didactics for Teaching AI and Machine Learning On-Site, Online, and Hybrid.” Education Sciences, vol. 11, no. 7, June 2021, p. 318, https://doi.org/10.3390/educsci11070318.


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