Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-22751| Publication type: | Article in scientific journal |
| Type of review: | Peer review (publication) |
| Title: | The AI-Atlas : didactics for teaching AI and machine learning on-site, online, and hybrid |
| Authors: | Stadelmann, Thilo Keuzenkamp, Julian Grabner, Helmut Würsch, Christoph |
| et. al: | No |
| DOI: | 10.3390/educsci11070318 10.21256/zhaw-22751 |
| Published in: | Education Sciences |
| Volume(Issue): | 11 |
| Issue: | 7 |
| Page(s): | 318 |
| Issue Date: | 25-Jun-2021 |
| Publisher / Ed. Institution: | MDPI |
| ISSN: | 2227-7102 |
| Language: | English |
| Subjects: | Flexible educational design; E-learning; Constructivism; Design-based research; COVID-19; Post-pandemic tertiary engineering education; Artificial intelligence |
| Subject (DDC): | 006: Special computer methods 378: Higher education |
| Abstract: | 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/22751 |
| Fulltext version: | Published version |
| License (according to publishing contract): | CC BY 4.0: Attribution 4.0 International |
| Departement: | School of Engineering |
| Organisational Unit: | Centre for Artificial Intelligence (CAI) Institute of Data Analysis and Process Design (IDP) |
| Appears in collections: | Publikationen School of Engineering |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2021_Stadelmann-etal_AI-Atlas.pdf | 2.2 MB | Adobe PDF |  View/Open |
Show full item record
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.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.