Block-Based Programming Learning Tool for ML and AI Education (Work in Progress)

verfasst von: Yousuf Amanuel, Joshua Garlisch, Johannes Krugel
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.
Organisationseinheit(en): Fachgebiet Didaktik der Elektrotechnik und Informatik
Typ: Aufsatz in Konferenzband
Seiten: 1-3
Anzahl der Seiten: 3
Publikationsdatum: 2024
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Informationssysteme und -management, Ingenieurwesen (insg.), Ausbildung bzw. Denomination
Elektronische Version(en): https://doi.org/10.1109/EDUCON60312.2024.10578627 (Zugang: Geschlossen)

BibTeX

@inproceedings{483ac6f6a37d4012a72696bbf55028dc,
title = "Block-Based Programming Learning Tool for ML and AI Education (Work in Progress)",
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",
author = "Yousuf Amanuel and Joshua Garlisch and Johannes Krugel",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.",
year = "2024",
doi = "10.1109/EDUCON60312.2024.10578627",
language = "English",
isbn = "979-8-3503-9403-0",
series = " IEEE Global Engineering Education Conference",
pages = "1--3",
booktitle = "2024 IEEE Global Engineering Education Conference (EDUCON)",
}