Abstract
In this paper, we present a teaching material designed as part of a research project aiming to investigate approaches to teaching lower secondary school students technology-critical aspects of AI, specifically machine learning. The approach is scenario-based, meaning that students act in real-life roles, in this case in a fictional company that develops apps. The material has been tested in four classes in 2024, and analysis of the results is still ongoing. As part of our research, we examine whether and how a scenario-based approach contributes to understanding machine learning and its' use in the real world. Preliminary analysis suggests that participating students perceive the approach differently.
| Original language | English |
|---|---|
| Title of host publication | ITiCSE 2025: Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education, Volume 2 |
| Editors | Erik Barendsen, Floor Binkhorst, Ángel Velázquez-Iturbide, Jaime Urquiza Fuentes, James Paterson, Keith Quille |
| Number of pages | 2 |
| Place of Publication | New York |
| Publisher | Association for Computing Machinery |
| Publication date | 2025 |
| Pages | 727-728 |
| ISBN (Print) | 9798400715693 |
| ISBN (Electronic) | 9798400715693 |
| DOIs | |
| Publication status | Published - 2025 |
| Event | The 30th ACM Conference on Innovation and Technology in Computer Science Education. ITiCSE 2025 - Radboud University, Nijmegen, Netherlands Duration: 30 Jun 2025 → 2 Jul 2025 Conference number: 30 https://iticse.acm.org/2025/ |
Conference
| Conference | The 30th ACM Conference on Innovation and Technology in Computer Science Education. ITiCSE 2025 |
|---|---|
| Number | 30 |
| Location | Radboud University |
| Country/Territory | Netherlands |
| City | Nijmegen |
| Period | 30/06/2025 → 02/07/2025 |
| Internet address |
| Series | Proceedings of the Annual Conference on Innovation & Technology in Computer Science Education |
|---|---|
| Volume | 30 |
| ISSN | 1942-647X |
Keywords
- Secondary school education
- Scenario-based learning
- Computer Science
- Machine learning
- AI