Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings of the 2022 IEEE Global Engineering Education Conference, EDUCON 2023 |
| Herausgeber (Verlag) | IEEE Computer Society |
| ISBN (elektronisch) | 979-8-3503-9943-1 |
| ISBN (Print) | 979-8-3503-9944-8 |
| Publikationsstatus | Veröffentlicht - 2023 |
| Veranstaltung | IEEE Global Engineering Education Conference - Kuwait, Kuwait Dauer: 1 Mai 2023 → 4 Mai 2023 Konferenznummer: 14 https://2023.ieee-educon.org/ |
Publikationsreihe
| Name | IEEE Global Engineering Education Conference |
|---|---|
| ISSN (Print) | 2165-9559 |
| ISSN (elektronisch) | 2165-9567 |
Abstract
This work identifies the capabilities of a block-based programming approach for learning machine learning concepts. It focuses on the following overarching research question: 'How can block-based programming tools be used to facilitate the understanding and application of machine learning concepts in K-12 education?'. To answer this question, guidelines for conducting a systematic literature review are followed, resulting in the study of 17 different learning tools. These tools are examined for their technical nature, content coverage, design features, intelligibility, evaluations, and deployability. The findings suggest that the vast majority of tools focus on a high-level representation of classification models that children can create in an extended version of the Scratch programming environment. By this, however, only one facet of machine learning is addressed, and deeper insights into the underlying functions are not provided. In addition, technical, linguistic, and conceptual barriers to the design of tools and the wider curricula become apparent.
ASJC Scopus Sachgebiete
- Entscheidungswissenschaften (insg.)
- Informationssysteme und -management
- Sozialwissenschaften (insg.)
- Ausbildung bzw. Denomination
- Ingenieurwesen (insg.)
- Allgemeiner Maschinenbau
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- BibTex
- RIS
Proceedings of the 2022 IEEE Global Engineering Education Conference, EDUCON 2023. IEEE Computer Society, 2023. ( IEEE Global Engineering Education Conference).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Learning Tools Using Block-Based Programming for AI Education
AU - Fleger, Chris-Bennet
AU - Amanuel, Yousuf
AU - Krugel, Johannes
N1 - Conference code: 14
PY - 2023
Y1 - 2023
N2 - This work identifies the capabilities of a block-based programming approach for learning machine learning concepts. It focuses on the following overarching research question: 'How can block-based programming tools be used to facilitate the understanding and application of machine learning concepts in K-12 education?'. To answer this question, guidelines for conducting a systematic literature review are followed, resulting in the study of 17 different learning tools. These tools are examined for their technical nature, content coverage, design features, intelligibility, evaluations, and deployability. The findings suggest that the vast majority of tools focus on a high-level representation of classification models that children can create in an extended version of the Scratch programming environment. By this, however, only one facet of machine learning is addressed, and deeper insights into the underlying functions are not provided. In addition, technical, linguistic, and conceptual barriers to the design of tools and the wider curricula become apparent.
AB - This work identifies the capabilities of a block-based programming approach for learning machine learning concepts. It focuses on the following overarching research question: 'How can block-based programming tools be used to facilitate the understanding and application of machine learning concepts in K-12 education?'. To answer this question, guidelines for conducting a systematic literature review are followed, resulting in the study of 17 different learning tools. These tools are examined for their technical nature, content coverage, design features, intelligibility, evaluations, and deployability. The findings suggest that the vast majority of tools focus on a high-level representation of classification models that children can create in an extended version of the Scratch programming environment. By this, however, only one facet of machine learning is addressed, and deeper insights into the underlying functions are not provided. In addition, technical, linguistic, and conceptual barriers to the design of tools and the wider curricula become apparent.
KW - Artificial Intelligence
KW - Computer Uses in Education
KW - Computer science education
KW - Learning environments
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85162729837&partnerID=8YFLogxK
U2 - 10.1109/EDUCON54358.2023.10125154
DO - 10.1109/EDUCON54358.2023.10125154
M3 - Conference contribution
SN - 979-8-3503-9944-8
T3 - IEEE Global Engineering Education Conference
BT - Proceedings of the 2022 IEEE Global Engineering Education Conference, EDUCON 2023
PB - IEEE Computer Society
T2 - IEEE Global Engineering Education Conference
Y2 - 1 May 2023 through 4 May 2023
ER -