Details
| Original language | English |
|---|---|
| Title of host publication | 2024 IEEE Global Engineering Education Conference (EDUCON) |
| Pages | 1-3 |
| Number of pages | 3 |
| ISBN (electronic) | 979-8-3503-9402-3 |
| Publication status | Published - 2024 |
Publication series
| Name | IEEE Global Engineering Education Conference |
|---|---|
| ISSN (Print) | 2165-9559 |
| ISSN (electronic) | 2165-9567 |
Abstract
This work presents a prototype of a learning tool that was developed to remedy current challenges and issues in the field of block-based programming for ML and AI education. Based on recent research strengths and weaknesses of current solutions were identified. Most of tools don't follow defined guidelines, don't meet certain requirements, or they neglect important points such as simple design or manageable black-box consideration. Based on these and other criteria we developed a block-based programming learning tool prototype that consists of three different learning games about supervised and reinforcement learning. Unsupervised learning might be included in further steps. The tool provides an introduction to algorithms such as decision trees, image classification, and agent based systems. These were identified as common algorithms to be taught in the area of ML and AI. Finally, we talk about current and expected results of this work. First study shows that the prototype we developed made a noticeable impact for programming newbies. Furthermore, younger people seem to enjoy using the block-based tool more than the older ones. The main goal of this work is to create a block-based programming environment that represents complex ML and AI concepts in a simplified way and makes them tangible for the students.
Keywords
- Computer science education, Educational technology, Learning (artificial intelligence), Machine learning, Software tools
ASJC Scopus subject areas
- Decision Sciences(all)
- Information Systems and Management
- Engineering(all)
- General Engineering
- Social Sciences(all)
- Education
Cite this
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- BibTeX
- RIS
2024 IEEE Global Engineering Education Conference (EDUCON). 2024. p. 1-3 ( IEEE Global Engineering Education Conference).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Block-Based Programming Learning Tool for ML and AI Education (Work in Progress)
AU - Amanuel, Yousuf
AU - Garlisch, Joshua
AU - Krugel, Johannes
N1 - Publisher Copyright: © 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This work presents a prototype of a learning tool that was developed to remedy current challenges and issues in the field of block-based programming for ML and AI education. Based on recent research strengths and weaknesses of current solutions were identified. Most of tools don't follow defined guidelines, don't meet certain requirements, or they neglect important points such as simple design or manageable black-box consideration. Based on these and other criteria we developed a block-based programming learning tool prototype that consists of three different learning games about supervised and reinforcement learning. Unsupervised learning might be included in further steps. The tool provides an introduction to algorithms such as decision trees, image classification, and agent based systems. These were identified as common algorithms to be taught in the area of ML and AI. Finally, we talk about current and expected results of this work. First study shows that the prototype we developed made a noticeable impact for programming newbies. Furthermore, younger people seem to enjoy using the block-based tool more than the older ones. The main goal of this work is to create a block-based programming environment that represents complex ML and AI concepts in a simplified way and makes them tangible for the students.
AB - This work presents a prototype of a learning tool that was developed to remedy current challenges and issues in the field of block-based programming for ML and AI education. Based on recent research strengths and weaknesses of current solutions were identified. Most of tools don't follow defined guidelines, don't meet certain requirements, or they neglect important points such as simple design or manageable black-box consideration. Based on these and other criteria we developed a block-based programming learning tool prototype that consists of three different learning games about supervised and reinforcement learning. Unsupervised learning might be included in further steps. The tool provides an introduction to algorithms such as decision trees, image classification, and agent based systems. These were identified as common algorithms to be taught in the area of ML and AI. Finally, we talk about current and expected results of this work. First study shows that the prototype we developed made a noticeable impact for programming newbies. Furthermore, younger people seem to enjoy using the block-based tool more than the older ones. The main goal of this work is to create a block-based programming environment that represents complex ML and AI concepts in a simplified way and makes them tangible for the students.
KW - Computer science education
KW - Educational technology
KW - Learning (artificial intelligence)
KW - Machine learning
KW - Software tools
UR - http://www.scopus.com/inward/record.url?scp=85199102482&partnerID=8YFLogxK
U2 - 10.1109/EDUCON60312.2024.10578627
DO - 10.1109/EDUCON60312.2024.10578627
M3 - Conference contribution
SN - 979-8-3503-9403-0
T3 - IEEE Global Engineering Education Conference
SP - 1
EP - 3
BT - 2024 IEEE Global Engineering Education Conference (EDUCON)
ER -